ar X iv :1 61 0. 01 81 4v 3 [h ep -e x] 1 2 N ov 2 01 6 Prepared for submission to JINST Very High-Energy Gamma-Ray Follow-Up Program Using Neutrino Triggers from IceCube The IceCube Collaboration: M. G. Aartsen, 2 K. Abraham, 34 M. Ackermann, 52 J. Adams, 16 J. A. Aguilar, 12 M. Ahlers, 30 M. Ahrens, 42 D. Altmann, 24 K. Andeen, 32 T. Anderson, 48 I. Ansseau, 12 G. Anton, 24 M. Archinger, 31 C. Argüelles, 14 J. Auffenberg, 1 S. Axani, 14 X. Bai,40 S. W. Barwick, 27 V. Baum, 31 R. Bay,7 J. J. Beatty, 18,19 J. Becker Tjus, 10 K.-H. Becker, 51 S. BenZvi, 49 D. Berley, 17 E. Bernardini 52,∗ A. Bernhard, 34 D. Z. Besson, 28 G. Binder, 8,7 D. Bindig, 51 M. Bissok, 1 E. Blaufuss, 17 S. Blot, 52 C. Bohm, 42 M. Börner, 21 F. Bos, 10 D. Bose, 44 S. Böser, 31 O. Botner, 50 J. Braun, 30 L. Brayeur, 13 H.-P. Bretz, 52 S. Bron, 25 A. Burgman, 50 T. Carver, 25 M. Casier, 13 E. Cheung, 17 D. Chirkin, 30 A. Christov, 25 K. Clark, 45 L. Classen, 35 S. Coenders, 34 G. H. Collin, 14 J. M. Conrad, 14 D. F. Cowen, 48,47 R. Cross, 49 M. Day,30 J. P. A. M. de André, 22 C. De Clercq, 13 E. del Pino Rosendo, 31 H. Dembinski, 36 S. De Ridder, 26 P. Desiati, 30 K. D. de Vries, 13 G. de Wasseige, 13 M. de With, 9 T. DeYoung, 22 J. C. Díaz-Vélez,30 V. di Lorenzo, 31 H. Dujmovic, 44 J. P. Dumm, 42 M. Dunkman, 48 B. Eberhardt, 31 T. Ehrhardt, 31 B. Eichmann, 10 P. Eller, 48 S. Euler, 50 P. A. Evenson, 36 S. Fahey,30 A. R. Fazely, 6 J. Feintzeig, 30 J. Felde, 17 K. Filimonov, 7 C. Finley, 42 S. Flis, 42 C.-C. Fösig, 31 A. Franckowiak, 52 R. Franke, 52,∗ E. Friedman, 17 T. Fuchs, 21 T. K. Gaisser, 36 J. Gallagher, 29 L. Gerhardt, 8,7 K. Ghorbani, 30 W. Giang, 23 L. Gladstone, 30 T. Glauch, 1 T. Glüsenkamp, 52 A. Goldschmidt, 8 G. Golup, 13 J. G. Gonzalez, 36 D. Grant, 23 Z. Griffith, 30 C. Haack,1 A. Haj Ismail, 26 A. Hallgren, 50 F. Halzen,30 E. Hansen, 20 T. Hansmann, 1 K. Hanson, 30 D. Hebecker, 9 D. Heereman, 12 K. Helbing, 51 R. Hellauer, 17 S. Hickford, 51 J. Hignight, 22 G. C. Hill, 2 K. D. Hoffman, 17 R. Hoffmann, 51 K. Holzapfel, 34 K. Hoshina, 30,a F. Huang, 48 M. Huber, 34 K. Hultqvist, 42 S. In,44 A. Ishihara, 15 E. Jacobi, 52 G. S. Japaridze, 4 M. Jeong, 44 K. Jero, 30 B. J. P. Jones, 14 M. Jurkovic, 34 A. Kappes, 35 T. Karg, 52 A. Karle, 30 U. Katz,24 M. Kauer, 30 A. Keivani, 48 J. L. Kelley, 30 A. Kheirandish, 30 M. Kim, 44 T. Kintscher, 52 J. Kiryluk, 43 T. Kittler, 24 S. R. Klein, 8,7 G. Kohnen, 33 R. Koirala, 36 H. Kolanoski, 9 R. Konietz, 1 L. Köpke, 31 C. Kopper, 23 S. Kopper, 51 D. J. Koskinen, 20 M. Kowalski, 9,52 K. Krings, 34 M. Kroll, 10 G. Krückl, 31 C. Krüger, 30 J. Kunnen, 13 S. Kunwar, 52 N. Kurahashi, 39 T. Kuwabara, 15 M. Labare, 26 J. L. Lanfranchi, 48 M. J. Larson, 20 F. Lauber, 51 D. Lennarz, 22 M. Lesiak-Bzdak, 43 M. Leuermann, 1 L. Lu, 15 J. Lünemann, 13 J. Madsen, 41 G. Maggi, 13 K. B. M. Mahn, 22 S. Mancina, 30 M. Mandelartz, 10 R. Maruyama, 37 K. Mase,15 R. Maunu, 17 F. McNally, 30 K. Meagher, 12 M. Medici, 20 M. Meier,21 A. Meli, 26 T. Menne,21 G. Merino, 30 *Corresponding author., *Corresponding author. http://arxiv.org/abs/1610.01814v3 T. Meures, 12 S. Miarecki, 8,7 L. Mohrmann, 52 T. Montaruli, 25 M. Moulai, 14 R. Nahnhauer, 52 U. Naumann, 51 G. Neer,22 H. Niederhausen, 43 S. C. Nowicki, 23 D. R. Nygren, 8 A. Obertacke Pollmann, 51 A. Olivas, 17 A. O’Murchadha, 12 T. Palczewski, 46 H. Pandya, 36 D. V. Pankova, 48 P. Peiffer, 31 Ö. Penek,1 J. A. Pepper, 46 C. Pérez de los Heros, 50 D. Pieloth, 21 E. Pinat, 12 P. B. Price, 7 G. T. Przybylski, 8 M. Quinnan, 48 C. Raab,12 L. Rädel, 1 M. Rameez,20 K. Rawlins, 3 R. Reimann, 1 B. Relethford, 39 M. Relich, 15 E. Resconi, 34 W. Rhode, 21 M. Richman, 39 B. Riedel, 23 S. Robertson, 2 M. Rongen, 1 C. Rott, 44 T. Ruhe,21 D. Ryckbosch, 26 D. Rysewyk, 22 L. Sabbatini, 30 S. E. Sanchez Herrera, 23 A. Sandrock, 21 J. Sandroos, 31 S. Sarkar, 20,38 K. Satalecka, 52 P. Schlunder, 21 T. Schmidt, 17 S. Schoenen, 1 S. Schöneberg, 10 L. Schumacher, 1 D. Seckel, 36 S. Seunarine, 41 D. Soldin, 51 M. Song, 17 G. M. Spiczak, 41 C. Spiering, 52 T. Stanev, 36 A. Stasik, 52 J. Stettner, 1 A. Steuer, 31 T. Stezelberger, 8 R. G. Stokstad, 8 A. Stößl, 52 R. Ström, 50 N. L. Strotjohann, 52 G. W. Sullivan, 17 M. Sutherland, 18 H. Taavola, 50 I. Taboada, 5 J. Tatar, 8,7 F. Tenholt, 10 S. Ter-Antonyan, 6 A. Terliuk, 52 G. Teši ć,48 S. Tilav, 36 P. A. Toale, 46 M. N. Tobin, 30 S. Toscano, 13 D. Tosi, 30 M. Tselengidou, 24 A. Turcati, 34 E. Unger, 50 M. Usner, 52 J. Vandenbroucke, 30 N. van Eijndhoven, 13 S. Vanheule, 26 M. van Rossem, 30 J. van Santen, 52 J. Veenkamp, 34 M. Vehring, 1 M. Voge,11 E. Vogel, 1 M. Vraeghe, 26 C. Walck, 42 A. Wallace, 2 M. Wallraff, 1 N. Wandkowsky, 30 Ch. Weaver, 23 M. J. Weiss, 48 C. Wendt, 30 S. Westerhoff, 30 B. J. Whelan, 2 S. Wickmann, 1 K. Wiebe, 31 C. H. Wiebusch, 1 L. Wille, 30 D. R. Williams, 46 L. Wills, 39 M. Wolf, 42 T. R. Wood, 23 E. Woolsey, 23 K. Woschnagg, 7 D. L. Xu,30 X. W. Xu,6 Y. Xu,43 J. P. Yanez,52 G. Yodh, 27 S. Yoshida, 15 and M. Zoll 42 and M. Zoll 40 The MAGIC Collaboration: M. L. Ahnen, 51a S. Ansoldi, 52a L. A. Antonelli, 53 P. Antoranz, 54 A. Babic, 55 B. Banerjee, 56 P. Bangale, 57 U. Barres de Almeida, 57,72 J. A. Barrio, 58 J. Becerra González, 59,73 W. Bednarek, 60 E. Bernardini, 52,9 A. Berti, 52a,74 B. Biasuzzi, 52a A. Biland, 51a O. Blanch, 61 S. Bonnefoy, 58 G. Bonnoli, 53 F. Borracci, 57 T. Bretz, 62,75 S. Buson, 63 A. Carosi, 53 A. Chatterjee, 56 R. Clavero, 59 P. Colin, 57 E. Colombo, 59 J. L. Contreras, 58 J. Cortina, 61 S. Covino, 53 P. Da Vela,54 F. Dazzi,57 A. De Angelis, 63 B. De Lotto, 52a E. de Oña Wilhelmi, 64 F. Di Pierro, 53 M. Doert, 21 A. Domínguez, 58 D. Dominis Prester, 55 D. Dorner, 62 M. Doro, 63 S. Einecke, 21 D. Eisenacher Glawion, 62 D. Elsaesser, 21 M. Engelkemeier, 21 V. Fallah Ramazani, 65 A. Fernández-Barral, 61 D. Fidalgo, 58 M. V. Fonseca, 58 L. Font, 66 K. Frantzen, 21 C. Fruck, 57 D. Galindo, 67 R. J. García López, 59 M. Garczarczyk, 52 D. Garrido Terrats, 66 M. Gaug,66 P. Giammaria, 53 N. Godinovi ć,55 A. González Muñoz, 61 D. Góra,52,9,∗ D. Guberman, 61 D. Hadasch, 68 A. Hahn, 57 Y. Hanabata, 68 M. Hayashida, 68 J. Herrera, 59 J. Hose, 57 D. Hrupec, 55 G. Hughes, 51a W. Idec,60 K. Kodani, 68 Y. Konno, 68 H. Kubo, 68 J. Kushida, 68 A. La Barbera, 53 D. Lelas, 55 E. Lindfors, 65 S. Lombardi, 53 F. Longo, 52a,74 M. López, 58 R. López-Coto, 61,76 P. Majumdar, 56 M. Makariev, 69 K. Mallot, 52 G. Maneva,69 M. Manganaro, 59 K. Mannheim, 62 L. Maraschi, 53 B. Marcote, 67 M. Mariotti, 63 M. Martínez, 61 D. Mazin,57,77 U. Menzel,57 J. M. Miranda, 54 R. Mirzoyan, 57 A. Moralejo, 61 *Corresponding author. E. Moretti, 57 D. Nakajima, 68 V. Neustroev, 65 A. Niedzwiecki, 60 M. Nievas Rosillo, 58 K. Nilsson, 65,78 K. Nishijima, 68 K. Noda, 57 L. Nogués, 61 A. Overkemping, 21 S. Paiano, 63 J. Palacio, 61 M. Palatiello, 52a D. Paneque, 57 R. Paoletti, 54 J. M. Paredes, 67 X. Paredes-Fortuny, 67 G. Pedaletti, 52 M. Peresano, 52a L. Perri, 53 M. Persic, 52,79 J. Poutanen, 65 P. G. Prada Moroni, 70 E. Prandini, 51a,80 I. Puljak, 55 I. Reichardt, 63 W. Rhode, 21 M. Ribó, 67 J. Rico, 61 J. Rodriguez Garcia, 57 T. Saito, 68 K. Satalecka, 52 S. Schroeder, 21 C. Schultz, 63 T. Schweizer, 57 A. Sillanpää, 65 J. Sitarek, 60 I. Snidaric, 55 D. Sobczynska, 60 A. Stamerra, 53 T. Steinbring, 62 M. Strzys, 57 T. Suri ć,55 L. Takalo, 65 F. Tavecchio, 53 P. Temnikov, 69 T. Terzi ć,55 D. Tescaro, 64 M. Teshima, 57,77 J. Thaele, 21 D. F. Torres, 71 T. Toyama, 57 A. Treves, 52 G. Vanzo,59 V. Verguilov, 69 I. Vovk, 57 J. E. Ward, 61 M. Will, 59 M. H. Wu,64 R. Zanin, 67,76 The VERITAS Collaboration: A. U. Abeysekara, 81 S. Archambault, 82 A. Archer, 83 W. Benbow, 84 R. Bird, 85 E. Bourbeau, 82 M. Buchovecky, 85 V. Bugaev, 83 K. Byrum, 86 J. V Cardenzana, 87 M. Cerruti, 84 L. Ciupik, 88 M. P. Connolly, 89 W. Cui,90,91 H. J. Dickinson, 87 J. Dumm, 92 J. D. Eisch, 87 M. Errando, 83 A. Falcone, 93 Q. Feng,82 J. P. Finley, 90 H. Fleischhack, 94 A. Flinders, 81 L. Fortson, 92 A. Furniss, 95 G. H. Gillanders 89 S. Griffin, 82 J. Grube, 88 M. Hütten, 94 N. Håkansson, 96 O. Hervet, 97 J. Holder, 98 T. B. Humensky, 99 C. A. Johnson, 97 P. Kaaret, 100 P. Kar,81 N. Kelley-Hoskins, 94 M. Kertzman, 101 D. Kieda, 81 M. Krause, 94 F. Krennrich, 87 S. Kumar, 98 M. J. Lang, 89 G. Maier,94 S. McArthur, 90 A. McCann, 82 P. Moriarty, 89 R. Mukherjee, 102 T. Nguyen, 103 D. Nieto, 99 S. O’Brien, 104 R. A. Ong, 85 A. N. Otte, 103 N. Park,105 M. Pohl, 96,94 A. Popkow, 85 E. Pueschel, 104 J. Quinn, 104 K. Ragan, 82 P. T. Reynolds, 106 G. T. Richards, 103 E. Roache, 84 C. Rulten, 92 I. Sadeh,94 M. Santander, 102 G. H. Sembroski, 90 K. Shahinyan, 92 D. Staszak, 105 I. Telezhinsky, 96,94 J. V. Tucci, 90 J. Tyler, 82 S. P. Wakely, 105 A. Weinstein, 87 P. Wilcox, 100 A. Wilhelm, 96,94 D. A. Williams, 97 B. Zitzer. 82 1III. Physikalisches Institut, RWTH Aachen University, D-52056 Aachen, Germany 2Department of Physics, University of Adelaide, Adelaide, 5005, Australia 3Dept. of Physics and Astronomy, University of Alaska Anchorage, 3211 Providence Dr., Anchorage, AK 99508, USA 4CTSPS, Clark-Atlanta University, Atlanta, GA 30314, USA 5School of Physics and Center for Relativistic Astrophysics, Georgia Institute of Technology, Atlanta, GA 30332, USA 6Dept. of Physics, Southern University, Baton Rouge, LA 70813, USA 7Dept. of Physics, University of California, Berkeley, CA 94720, USA 8Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA 9Institut für Physik, Humboldt-Universität zu Berlin, D-12489 Berlin, Germany 10Fakultät für Physik& Astronomie, Ruhr-Universität Bochum, D-44780 Bochum, Germany 11Physikalisches Institut, Universität Bonn, Nussallee 12,D-53115 Bonn, Germany 12Université Libre de Bruxelles, Science Faculty CP230, B-1050 Brussels, Belgium 13Vrije Universiteit Brussel, Dienst ELEM, B-1050 Brussels,Belgium 14Dept. of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 15Dept. of Physics and Institute for Global Prominent Research, Chiba University, Chiba 263-8522, Japan 16Dept. of Physics and Astronomy, University of Canterbury, Private Bag 4800, Christchurch, New Zealand 17Dept. of Physics, University of Maryland, College Park, MD 20742, USA 18Dept. of Physics and Center for Cosmology and Astro-Particle Physics, Ohio State University, Columbus, OH 43210, USA 19Dept. of Astronomy, Ohio State University, Columbus, OH 43210, USA 20Niels Bohr Institute, University of Copenhagen, DK-2100 Copenhagen, Denmark 21Dept. of Physics, TU Dortmund University, D-44221 Dortmund, Germany 22Dept. of Physics and Astronomy, Michigan State University,East Lansing, MI 48824, USA 23Dept. of Physics, University of Alberta, Edmonton, Alberta, Canada T6G 2E1 24Erlangen Centre for Astroparticle Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, D-91058 Erlangen, Germany 25Département de physique nucléaire et corpusculaire, Université de Genève, CH-1211 Genève, Switzerland 26Dept. of Physics and Astronomy, University of Gent, B-9000 Gent, Belgium 27Dept. of Physics and Astronomy, University of California, Irvine, CA 92697, USA 28Dept. of Physics and Astronomy, University of Kansas, Lawrence, KS 66045, USA 29Dept. of Astronomy, University of Wisconsin, Madison, WI 53706, USA 30Dept. of Physics and Wisconsin IceCube Particle Astrophysics Center, University of Wisconsin, Madison, WI 53706, USA 31Institute of Physics, University of Mainz, Staudinger Weg 7, D-55099 Mainz, Germany 32Department of Physics, Marquette University, Milwaukee, WI, 53201, USA 33Université de Mons, 7000 Mons, Belgium 34Physik-department, Technische Universität München, D-85748 Garching, Germany 35Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, D-48149 Münster, Germany 36Bartol Research Institute and Dept. of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA 37Dept. of Physics, Yale University, New Haven, CT 06520, USA 38Dept. of Physics, University of Oxford, 1 Keble Road, OxfordOX1 3NP, UK 39Dept. of Physics, Drexel University, 3141 Chestnut Street,Philadelphia, PA 19104, USA 40Physics Department, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA 41Dept. of Physics, University of Wisconsin, River Falls, WI 54022, USA 42Oskar Klein Centre and Dept. of Physics, Stockholm University, SE-10691 Stockholm, Sweden 43Dept. of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA 44Dept. of Physics, Sungkyunkwan University, Suwon 440-746,Korea 45Dept. of Physics, University of Toronto, Toronto, Ontario,Canada, M5S 1A7 46Dept. of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA 47Dept. of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802, USA 48Dept. of Physics, Pennsylvania State University, University Park, PA 16802, USA 49Dept. of Physics and Astronomy, University of Rochester, Rochester, NY 14627, USA 50Dept. of Physics and Astronomy, Uppsala University, Box 516, S-75120 Uppsala, Sweden 51Dept. of Physics, University of Wuppertal, D-42119 Wuppertal, Germany 52DESY, D-15735 Zeuthen, Germany aEarthquake Research Institute, University of Tokyo, Bunkyo, Tokyo 113-0032, Japan 51aETH Zurich, CH-8093 Zurich, Switzerland 52aUniversità di Udine, and INFN Trieste, I-33100 Udine, Italy 53INAF National Institute for Astrophysics, I-00136 Rome, Italy 54Università di Siena, and INFN Pisa, I-53100 Siena, Italy 55Croatian MAGIC Consortium, Rudjer Boskovic Institute, University of Rijeka, University of Split and University of Zagreb, Croatia 56Saha Institute of Nuclear Physics, 1/AF Bidhannagar, Salt Lake, Sector-1, Kolkata 700064, India 57Max-Planck-Institut für Physik, D-80805 München, Germany 58Universidad Complutense, E-28040 Madrid, Spain 59Inst. de Astrofísica de Canarias, E-38200 La Laguna, Tenerife, Spain; Universidad de La Laguna, Dpto. Astrofísica, E-38206 La Laguna, Tenerife, Spain 60University of Łódź, PL-90236 Lodz, Poland 61Institut de Fisica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona), Spain 62Universität Würzburg, D-97074 Würzburg, Germany 63Università di Padova and INFN, I-35131 Padova, Italy 64Institute for Space Sciences (CSIC/IEEC), E-08193 Barcelona, Spain 65Finnish MAGIC Consortium, Tuorla Observatory, Universityof Turku and Astronomy Division, University of Oulu, Finland 66Unitat de Física de les Radiacions, Departament de Física, and CERES-IEEC, Universitat Autònoma de Barcelona, E-08193 Bellaterra, Spain 67Universitat de Barcelona, ICC, IEEC-UB, E-08028 Barcelona, Spain 68Japanese MAGIC Consortium, ICRR, The University of Tokyo, Department of Physics and Hakubi Center, Kyoto University, Tokai University, The University of Tokushima, KEK, Japan 69Inst. for Nucl. Research and Nucl. Energy, BG-1784 Sofia, Bulgaria 70Università di Pisa, and INFN Pisa, I-56126 Pisa, Italy 71ICREA and Institute for Space Sciences (CSIC/IEEC), E-08193 Barcelona, Spain 72now at Centro Brasileiro de Pesquisas Físicas (CBPF/MCTI), R. Dr. Xavier Sigaud, 150 - Urca, Rio de Janeiro - RJ, 22290-180, Brazil 73now at NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA and Department of Physics and Department of Astronomy, University of Maryland, College Park, MD 20742, USA 74also at University of Trieste 75now at Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland 76now at Max-Planck-Institut fur Kernphysik, P.O. Box 103980, D 69029 Heidelberg, Germany 77also at Japanese MAGIC Consortium 78now at Finnish Centre for Astronomy with ESO (FINCA), Turku,Finland 79also at INAF-Trieste and Dept. of Physics& Astronomy, University of Bologna 80also at ISDC - Science Data Center for Astrophysics, 1290, Versoix (Geneva) 81Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA 82Physics Department, McGill University, Montreal, QC H3A 2T8, Canada 83Department of Physics, Washington University, St. Louis, MO 63130, USA 84Fred Lawrence Whipple Observatory, Harvard-Smithsonian Center for Astrophysics, Amado, AZ 85645, USA 85Department of Physics and Astronomy, University of California, Los Angeles, CA 90095, USA 86Argonne National Laboratory, 9700 S. Cass Avenue, Argonne,IL 60439, USA 87Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA 88Astronomy Department, Adler Planetarium and Astronomy Museum, Chicago, IL 60605, USA 89School of Physics, National University of Ireland Galway, University Road, Galway, Ireland 90Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907, USA 91Department of Physics and Center for Astrophysics, Tsinghua University, Beijing 100084, China. 92School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA 93Department of Astronomy and Astrophysics, 525 Davey Lab, Pennsylvania State University, University Park, PA 16802, USA 94DESY, Platanenallee 6, 15738 Zeuthen, Germany 95Department of Physics, California State University - East Bay, Hayward, CA 94542, USA 96Institute of Physics and Astronomy, University of Potsdam,14476 Potsdam-Golm, Germany 97Santa Cruz Institute for Particle Physics and Department ofPhysics, University of California, Santa Cruz, CA 95064, USA 98Department of Physics and Astronomy and the Bartol ResearchInstitute, University of Delaware, Newark, DE 19716, USA 99Physics Department, Columbia University, New York, NY 10027, USA 100Department of Physics and Astronomy, University of Iowa, Van Allen Hall, Iowa City, IA 52242, USA 101Department of Physics and Astronomy, DePauw University, Greencastle, IN 46135-0037, USA 102Department of Physics and Astronomy, Barnard College, Columbia University, NY 10027, USA 103School of Physics and Center for Relativistic Astrophysics, Georgia Institute of Technology, 837 State Street NW, Atlanta, GA 30332-0430 104School of Physics, University College Dublin, Belfield, Dublin 4, Ireland 105Enrico Fermi Institute, University of Chicago, Chicago, IL60637, USA 106Department of Physical Sciences, Cork Institute of Technology, Bishopstown, Cork, Ireland E-mail: Dariusz.Gora@desy.de, Elisa.Bernardini@desy.de, Robert.Franke@desy.de Abstract: We describe and report the status of a neutrino-triggered program in IceCube that gener- ates real-time alerts for gamma-ray follow-up observations by atmospheric-Cherenkov telescopes (MAGIC and VERITAS). While IceCube is capable of monitoringthe whole sky continuously, high-energy gamma-ray telescopes have restricted fields ofview and in general are unlikely to be observing a potential neutrino-flaring source at the time such neutrinos are recorded. The use of neutrino-triggered alerts thus aims at increasing the availability of simultaneous multi-messenger data during potential neutrino flaring activity, which can increase the discovery potential and con- strain the phenomenological interpretation of the high-energy emission of selected source classes (e.g. blazars). The requirements of a fast and stable onlineanalysis of potential neutrino signals and its operation are presented, along with first results of the program operating between 14 March 2012 and 31 December 2015. Keywords: Neutrino detectors, Gamma telescopes mailto:Dariusz.Gora@desy.de, Elisa.Bernardini@desy.de, Robert.Franke@desy.de Contents 1 Introduction 1 2 Selection of target sources 3 3 The IceCube detector and IACT partners 5 4 Neutrino event selection 5 4.1 Muon Filter 6 4.2 Online Level 2 Filter 7 4.3 NToO selection variables 9 4.4 NToO cut optimization 11 4.5 Properties of the neutrino sample 13 5 The time-clustering algorithm 14 5.1 Alert rate, detection probability 17 6 Data stability monitoring 18 6.1 Rate measurements and data quality assessment 19 6.2 Stability-score calculation 19 6.3 Implementation of the stability-score calculation 21 7 Technical design of the alert system 21 8 Monitoring of alert system 22 9 Results of NToO program 25 10 Recent and upcoming improvements 27 11 Summary and Conclusions 30 12 Appendix 35 1 Introduction Observations of astrophysical neutrinos may help to answersome of the most fundamental ques- tions in astrophysics, in particular the mystery of the source of cosmic rays (for a general discussion see [1]). For neutrinos in the TeV range, prime source candidates are Galactic supernova remnants [2]. Neutrinos in the PeV range and above are suspected to be produced by active galactic nuclei (AGN) and gamma ray bursts (GRB) with many AGN models predicting a significant neutrino – 1 – flux [3–5]. However, the recent results from the IceCube Collaboration strongly disfavor GRBs as sources of the highest energy cosmic rays [6]. Recently, the IceCube Collaboration has also reported the first observation of a cosmic diffuse neutrino flux which lies in the 100 TeV to PeV range [7]. Individual neutrino sources, however, could not be identified up to now. While many astrophysical sources of origin have been suggested [8], there is not yet enough information to narrow down the possibilities to any particular source or source class. Gamma-ray observations by imaging atmospheric-Cherenkovtelescopes (IACTs) such as VER- ITAS [9], HESS [10] or MAGIC [11] have also a potential to find hadronicγ-rays from the neu- trino directions and to identify neutrino sources [12, 13]. The expected neutrino flux from observed high-energy gamma-ray fluxes of blazars in their brightest states (e.g. the flares of Markarian 501 in 1997 [17] and 2005 [18], Markarian 421 in 2000/2001 [19] and 2008 [20] and PKS 2155-304 in 2006 [21]) can be at the level of the neutrino flux detected by the high-energy neutrino telescopes [14–16]. For sources which manifest large time variations in the emitted electromagnetic radiation, the signal-to-noise ratio can be increased by searching for periods of enhanced neutrino emission (a time-dependent search). Of special interest is the relation of these periods of enhanced neutrino emission with periods of strong high-energy gamma-ray emission. However, IACTs have a duty cycle limited to the clear, dark nights (roughly 10% of totaltime), such correlation studies are not always possible after the neutrino flare has occurred. Therefore, it is desirable to ensure the availability of simultaneous neutrino and high-energy gamma-ray data for periods of interest. This can be achieved by an online neutrino flare search that alertsthe partner IACT experiments when an elevated rate of neutrino events from the direction of a source candidate is detected. Such a Neutrino Triggered Target of Opportunity program (NToO), using a list of pre-defined sources, has been developed and has been operating since 2006 using the AMANDA neutrino telescope to initiate gamma-ray follow-up observations by MAGIC [24, 25]. Similarly, one can conduct a search for neutrinos from shorttransient sources (with a time scale of 100 seconds), such as gamma-ray bursts (GRBs) (see e.g. [27]) and core-collapse super- novae (SNe) (see e.g. [28]). These sources are most accessible in X-ray and optical wavelengths, where one can observe the GRB afterglow or the rising SN lightcurve, respectively. Similarly to IACTs, the field of view and observation time of X-ray and optical telescopes are limited. Since identification of a GRB or SN is only possible within a certaintime range (a few hours after a GRB and a few weeks after a SN explosion), it is important to obtain electromagnetic data within these time frames. To accomplish this, a NToO program triggering optical and X-ray follow-up of short neutrino transients has been operating since 2008 [29, 30]. Upon observing multiplets of neutrino candidates (at least two within 100 seconds and within 3.5◦ (angular resolution), from any direction) alerts are sent to the Robotic Optical TransientSearch Experiment (ROTSE) [31]1 and the Palomar Transient Factory (PTF) [32] for optical observations, and to Swift [33, 34] for X-ray follow-up, depending on the multiplet’s significance. We present here a refined and enhanced implementation of the NToO system using the IceCube Neutrino Observatory (see also [26]). An important goal of this program was to establish and to test procedures to trigger promptly the gamma-ray community to collect high-sensitive VHE data 1ROTSE was used from 2008-2014 and it is not operational anymore. – 2 – from specific sources during periods of time when IceCube measures a potential increase in their neutrino flux. The program is based on a multi-step neutrino selection that is applied online at the South Pole. An alert is sent to the partner telescopes MAGIC and VERITAS in the case that a statistically significant cluster of neutrinos is observed from any of the monitored sources. If the source were to be found in an enhanced flux state by the IACTfollow-up observation, the combination of the neutrino observation and the very high-energy gamma-observation could help to establish the discovery of neutrino point sources. Furthermore, combining the two observations would increase the potential insight into the physical processes in the source that lead to the flare. The structure of the paper is the following: Section 2 definesthe sources used in the NToO system. Section 3 focuses on the short description of the IceCube and IACTs detectors and their detection principle. In Section 4 the NToO neutrino event selections and the properties of the final neutrino sample are shown. Section 5 describes how the significances of neutrino clusters in the NToO are calculated. Sections 6, 7, 8 describe the technicaldesign and implementation of the NToO system. In Section 9 we present first results of the program operating between 14 March 2012 and 31 December 2015. In Section 10 recent and upcoming improvements of the NToO system are discussed. Finally, in Section 11 a short summaryis given. 2 Selection of target sources The probability of discovering extraterrestrial neutrinopoint sources varies with the phenomenol- ogy of the accelerators and of their emission mechanisms. Coincident observation of gamma rays and neutrinos might be possible for sources where charged and neutral mesons are produced si- multaneously from hadronic p-p or p-γ interactions. These hadronic processes may be present in variable extragalactic objects such as BL Lacs or flat-spectrum radio quasars (FSRQs), as well as in Galactic systems like microquasars and magnetars. Blazars, a subset of radio-loud active galactic nuclei withrelativistic jets pointing towards the Earth [41] are commonly classified based on the properties of the spectral energy distribution (SED) of their electromagnetic emission. The blazar SED featurestwo distinctive peaks: a low-energy peak between IR and X-ray energies, attributed to synchrotron emission of energetic electrons, and a high-energy peak at gamma-ray energies, which can be explained by several and possibly competing interaction and radiation processes of high-energy electrons and high-energy nuclei [42]. It has been suggested that blazar SED follow a sequence [43–45] in which the peak energy of the synchrotron emission spectrum decreases with increasing blazar luminosity. Accordingly, blazars can be classified into low synchrotron peak (LSP), intermediate synchrotron peak (ISP) and high synchrotron peak (HSP) objects, a classication schemeintroduced in [46]. A second classifier is based on the prominence of emission lines in the SED over the non-thermal continuum emission of the jet. FSRQs show Doppler-broadened optical emission lines [47], while in the BL Lac objects the emission lines do not exist, or are hidden in a strong continuum emission. The probability for detection of an individual AGN neutrinoflare can be estimated based on the predictions of different mechanisms for the observed electromagnetic emission at high energies [3, 4]. A common feature of several models is that the class of high-energy peaked HSP is expected to have lower gamma luminosity as compared to low-energy peaked LSP and FSRQs, if the observed high-energy gamma-ray emission is largely the result of interactions of protons with ambient or – 3 – self-produced radiation. With high target matter density,the neutrino yield can be highest when the very high-energy gamma-ray emission is strongly attenuated by internal absorption (although the cutoff energy is somewhat uncertain). In the case of pp-dominated scenarios, the conclusions are different [4], favoring LSP to FSRQs. In all cases, the availability of simultaneous information on high-energy gamma-ray emission and neutrinos is crucialfor distinguishing between different production models. The most interesting targets for gamma-ray follow-up observations triggered by IceCube alerts are promising sources of TeV neutrinos, which are either known to exhibit a bright GeV flux in gamma rays and show extrapolated fluxes detectable by IACTs,or are already detected by IACTs and are variable. We consider two different target source lists. One list was selected based on the the secondFermi-LAT point-source catalog [36] 2. The following criteria were applied: • Redshift< 0.6 3 • Fermi-LAT variability index > 41.64 (corresponding to the 99% confidence level of the source being variable, see [36] for the definition of this quantity) • Power-law spectral index as observed withFermi-LAT < 2.3 (BL Lacs only4) • Fermi-LAT flux [1 − 100GeV ]> 1 · 10−9ph cm−2 s−1 (BL Lacs only) • Fermi-LAT flux [0 .1− 1GeV ]> 7 · 10−8ph cm−2 s−1 (FSRQs only5) These selection criteria result in 21 sources on the list in total (three FSRQs and 18 BL Lacs). This list of target sources was combined with lists providedby the partner experiments (currently MAGIC and VERITAS) covering the Northern Hemisphere (declinationδ > 0◦). All known po- tentially variable VHE sources and all sources in theFermi-LAT monitored-sources list [39] with declination larger than 0◦ are used. In total 109 sources are included in the follow-up program for the 2012/2013 IceCube season, see Table6 in Appendix. As we can see from this table 43/(31) sources are present only in the VERITAS/(MAGIC) list and 35 sources are in the list for both experiments. From November 2013 to December 2015, the number of sources were reduced6 to 83 i.e. 5 for MAGIC; 65 for VERITAS, and 13 sources are present in both lists, see Table7 in Appendix. In principle, the neutrinos could also come from unknown sources anywhere in the sky. However, such an all-sky search was not feasible at the time the program was started due to limiting computing resources at the South Pole. Furthermore, an all-sky search suffers from large trial factors compared to the pre-defined source list search. 2The thirdFermi-LAT point-source catalog [37] or catalog of hardFermi-LAT sources [38] would have been more suitable, but it was not available when the selection criteria were established and the program started. 3The MAGIC and VERITAS telescopes have recently detected sources withz ∼ 0.94, PKS 1441+25 [40, 48] and B0218+357 [49]); therefore this selection criterion (”cut”) will be extended toz = 1 for the next IceCube observing season 2016/2017. 4As shown in [4] for pp-scenario only BL Lacs with the spectral index below 2.2 - 2.3 are promising candidates for IceCube detection, see Figure 2 in this paper. 5As shown in [3] for p-γ-scenario only FSRQs can be effective for interpretation of gamma-ray fluxes up to GeV energies. 6For MAGIC, the number of sources was reduced in order to fit to amount of observation time that was granted by MAGIC time allocation committee. – 4 – 3 The IceCube detector and IACT partners The IceCube Neutrino Observatory [54–56] is located at the geographic South Pole and was com- pleted at the end of 2010. The goal of the detector is to serve as a neutrino telescope, allowing observations of neutrinos of astrophysical origin in the TeV and PeV energy range. Cherenkov light produced by the secondary leptons from neutrino interactions in the vicinity of the detector is used to detect these neutrinos. IceCube is also sensitiveto downward-going high-energy muons and to neutrinos produced in cosmic-ray-induced air showers. These events represent a background for most IceCube analyses. For the studies presented here, only events produced by charged-current interactions of muon neutrinos are considered, because of the long range of the secondary muons which allows for reconstructing the arrival direction of these events with good accuracy. The pointing information relies on the secondary muon direction, which at energies above a TeV differs from the original neutrino direction by less than the angular resolution of the detector. The program presented in this work sends alerts to IACTs for follow-up observations. IACTs detect photon-induced air showers by means of the Cherenkovlight from the highly relativistic charged particles in the shower. Due to the interplay between the emission geometry and the altitude dependent index of refraction, the Cherenkov light flash (∼ 10 ns duration) is mainly con- centrated in a light pool with a radius of∼ 120 m (for gamma or electron showers) on the ground. A telescope located inside the light pool can reflect the light into a PMT camera. Cherenkov im- ages of the showers are used to differentiate between gamma-ray signal and background, and to reconstruct the energy and the incoming direction of the gamma rays. The MAGIC telescope array is located on the Roque de los Muchachos Observatory (28.8◦ N, 17.9◦ W; 2200 m above sea level), at the Canary Island of La Palma (Spain). The MAGIC array consists of two telescopes, placed 85 m apart, each with a primary mirror of 17 m diameter. The MAGIC telescopes, with a field of view of 3.5◦, are able to detect cosmic gamma rays in the range 50 GeV-50 TeV. The latest performance of MAGIC is reported in[22]. VERITAS is an array of four 12-m diameter imaging atmospheric-Cherenkov telescopes lo- cated at the Fred Lawrence Whipple Observatory in southern Arizona (31◦40′N, 110◦57′W) at an altitude of 1.3 km. Each of the individual telescopes have a 3.5◦ field of view. Full details of the VERITAS instrument performance and sensitivity are given in [23]. 4 Neutrino event selection This section describes the online neutrino selection that is the basis for the NToO system. As the computing resources at the South Pole are limited, different types of software triggers are applied to lower the data event rate. The most important for the purposes of this work is theSimple Multiplicity Trigger (SMT8) which requires eight triggered optical modules (i.e. four coincidence pairs) anywhere in the detector within 5µs. Most of the events which are selected by this trigger are composed of muons produced by cosmic rays in the atmosphere above the detector (about 2.5 kHz at trigger level in the 86-string configuration). As the data volume produced at trigger level is still too large to be transferred via satellite, a first selection has to be applied directly at the South – 5 – IceCube data taking period Start End IC-2011 2011 May 13 2012 May 16 IC-2012 2012 May 16 2013 May 3 IC-2013 2013 May 3 2014 May 5 IC-2014 2014 May 5 2015 May 18 IC-2015 2015 May 18 2016 May 20 Table 1. Summary of IceCube data taking periods (seasons) used by NToO searches Pole by using the so-calledMuon Filter. This filter aims to select well-reconstructed muon tracks of any direction, i.e. from the full sky. The event selection takes place in several steps, called “levels“ in IceCube. TheMuon Filter constitutes the first filtering level. It is a standard IceCube filter and not specific to the program presented here. The subsequentOnline Level 2 Filteris based on the input from theMuon Filter and was specifically developed to enable online analyses. Currently theOnline Level 2 Filterforms the basis of the optical and X-ray follow-up program and the NToO system presented in this work. Based on cut variables calculated from theOnline Level 2 Filter, an online neutrino event selection was implemented. The main goal is to achieve a high efficiency for valid neutrino events with the highest possible rejection of background. 4.1 Muon Filter TheMuon Filter event-selection algorithm is the basis for many standard IceCube muon-neutrino analyses, e.g. the searches for neutrino point sources, searches for neutrinos from gamma-ray bursts and measurements of the atmospheric muon-neutrino flux. Theinput to theMuon Filter is all of the events that trigger the SMT8. All events triggering the SMT8 are reconstructed with theLinefit first-guess algorithm as described in [57]. The result from this track fit forms the input to a single- photoelectron (SPE) likelihood fit [51, 57], which uses only the time and charge of the first hit on each DOM. TheMuon Filter decision is based on variables calculated from the SPE fit. The track reconstructions and cuts applied in theMuon Filter have been stable over several years. However, improvements to reconstruction algorithms, changes in the available satellite trans- fer bandwidth, or changes in the data serialization format lead to small adjustments from season to season. The IceCube seasons important for this work are listed in the Table1. For example, in the IC-2012 season an improvedLinefit algorithm was used, which uses a linear fit with reduced weights for outliers [58] . TheMuon Filter divides the sky into two regions in which different selection techniques are applied to remove background events. In the first region (defined by the zenith angleθ ≥ 78.5◦) the background events are down-going muons mis-reconstructed as up-going (or slightly above the horizon), which in fact originate from cosmic-ray-inducedair showers. The main discriminants to remove these events are parameters characterizing the reconstruction quality of the event. In the second region (zenith angleθ < 78.5◦) both signal and background events have the same signature, namely high-energy muon tracks. As the energy spectrum of muons in cosmic-ray air showers (Φ(E) ∼ E−3.7) is much steeper than the expected signal spectra, cuts on energy-related variables are an efficient way to reduce this background. However, as the currentNToO is only implemented – 6 – for zenith anglesθ > 90◦ the event-selection cuts in the second region will not be described in detail. In the first region theMuon Filter uses a cut variable derived from the value of the likelihood of the SPE track fit. The definition of the cut variable is similar to the scaled log-likelihood of the fit. All events which are reconstructed with a zenith angleθSPE≥ 78.5◦ and that fulfill − log10(maxLSPE)/(NDOM − 3) ≤ 8.7, (4.1) where maxLSPE is the maximum value of the likelihood function of the SPE track fit andNDOM denotes the number of triggered DOMs in that event, passed the filter. The efficiency of theMuon Filter for atmospheric neutrinos is about 87% with respect to SMT8.Neutrinos following a spec- trum of the formΦ(E) ∼ E−2 are selected with an efficiency of approximately 93% with respect to SMT8. The cuts remained unchanged between the different IceCube seasons i.e. from IC-2011 to IC-2014 . The total event passing rate of theMuon Filter amounts to approximately 45 Hz, out of which about 18 Hz consists of events reconstructed with zenith angleθSPE> 90◦. 4.2 Online Level 2 Filter While theMuon Filterprovides a sample of candidate neutrino events it is still heavily background- dominated (compared to the atmospheric-neutrino rate of about 10 mHz at the trigger level). In order to apply further cuts with a high signal efficiency, more elaborate reconstructions with an improved angular resolution are needed. As an example, the multi-photoelectron (MPE) likelihood function, which uses the temporal and amplitude information of the PMT pulses, is applied after several iterations of SPE likelihood fit. The MPE algorithm includes a probability distribution function (PDF) that describes the scattering of photons in the ice, and is fully described in [57]. Further reconstructions that estimate the angular reconstruction uncertainty are also helpful for subsequent analyses. This combination of additional reconstruction and event-selection cuts is referred to as theOnline Level 2 Filter. The SPE fit used as an input to theMuon Filter has limited angular resolution compared to an MPE fit. During the first season of running the IceCube in itsfull 86-string configuration (IC- 2011), the limited CPU resources at the South Pole prohibited applying more resource-intensive reconstructions to all events that passed theMuon Filter. Therefore, it was necessary to apply event-selection cuts to the events passing theMuon Filter before additional reconstruction could be performed. The computing resources at the South Pole wereexpanded prior to the second full season of IceCube in its 86-string configuration (IC-2012).This expansion made it possible to run some reconstruction (a double-iteration SPE fit and the MPE fit) before applying theOnline Level 2 cuts. In the up-going region, the main criteria to distinguish themis-reconstructed atmospheric- muon background from the neutrino events are quality parameters of the reconstructed track. Sev- eral variables derived from the single-iteration SPE fit were used to identify these well-reconstructed tracks and to suppress mis-reconstructed air-shower muonsduring the IC-2011 season. During the IC-2012 season these variables were derived from the MPE fit.The most important variables are: The Scaled Log-Likelihood In a maximum-likelihood fit the value of the likelihood at themaxi- mum divided by the number of degrees of freedom of the fit can measure the fit quality. The scaled – 7 – likelihood of the track fit is SLogL(x) = − log10(maxL) NDOM − x , (4.2) whereNDOM is the number of hit DOMs, andx the numbers of parameters determined by the fit, usually five: two angles, and three coordinates. However, it has been shown empirically thatSLogL(5) is energy-dependent for the track fits employed in IceCube. Thus, a cut onSLogL(5) variable introduces a bias against well-reconstructed low-energy events. In order to reduce this energy dependence bias, the different values ofx is used i.e.x = 3.5 for NToO andx = 4.5 for for optical and X-ray follow up. This variable is especiallyuseful for identifying mis-reconstructed muon tracks. Number of Direct Hits (NDir) Another measure of the track quality is the number of DOMs that have registered a hit with a very small time residualtres ∈ [−15 ns, 75 ns] with respect to the arrival time expected for Cherenkov emission from the reconstructed muon track. Such hits are called “direct hits“. The number of direct hitsNDir is calculated using only the first registered photon in each DOM. A photon causing a direct hit has undergone less scattering in the ice and thus can contribute more information to the directional reconstruction. The number of direct hits is therefore related to the quality of the track reconstruction. Direct Length (LDir) The distance between the projected direct hits onto the reconstructed track is referred to as the “direct length”,LDir . The hits defining the direct length result from the least- scattered photons and hence contribute the most to the reconstruction. If LDir is large, the lever arm for the reconstruction is longer, generally resulting in smaller reconstruction errors. Therefore, selecting events with largerLDir selects the events most valuable for a point-source analysis. The cut variables described above have been combined to achieve good background rejection as well as reasonable signal efficiency. Events that are reconstructed as up-going (zenith angle θSPE> 82◦) and fulfill SLogL(4.5) ≤ 8.3 or log10( QTot p.e. ) ≥ 2.7 or ( LDir [m] 160 )2 + (NDir 9 )2 ≥ 1 (4.3) where QTot is the total charge of the event in photoelectrons(p.e.), are selected by theOnline Level 2 Filter. The pre-selection criterion based on the number of direct hits (NDir) and the direct length (LDir) in Eq. (4.3) is called the “direct ellipse“ cut. The background atmospheric-muon events tend to have short direct lengths and a small number of direct hitssince, if they are mis-reconstructed as upward-going muon tracks, the hit pattern tends to match poorly. The direct ellipse cut keeps ∼ 74% of atmospheric neutrinos and∼ 76% of astrophysical neutrinos while rejecting∼ 93% of atmospheric muons. Quality parameters of the track reconstruction A critical parameter in a maximum-likelihood- based search for neutrino point sources is the error of the reconstruction for each event. As this can only be determined on an event-by-event basis with simulated data, an estimate has to be used for experimental data. Two different approaches are applied in IceCube: theParaboloid fit – 8 – and theCramér-Rao Resolution Estimate. TheParaboloid fitscans the likelihood space around the minimum determined in the track fit by varying the fit parameters. The resulting points in likelihood space are fitted with a parabola [52]. Due to the repeated evaluation of the likelihood functionthis method can be too slow to be used in the online filtering, especially for high-energy events with a large number of hit DOMs. TheCramér-Rao Resolution Estimateis the uncertainty on the reconstructed track direction given by the log-likelihood-based track reconstruction estimated by a method based on the Cramér-Rao inequality [53]. As the calculation involves no minimization of a likelihood it is considerably faster (and have a similarperformance ) than theParaboloid fit and thus is the preferred method to be used in online analysis, also in NToO. Since the likelihood used in the track fit fully describes the Cherenkov light emission and propagation, both angular uncertainty estimates, theParaboloid fitand the Cramér-Rao method, show an energy-dependent bias in the ratios of the estimated to the true angular uncertainty. This bias can be calibrated using Monte Carlo events to derive a correction factor which is a function of the reconstructed event energy. The event rate after application of theOnline Level 2 Filteris reduced to approximately 2 Hz (in the up-going region). More than 99% of well-reconstructed signal neutrinos (i.e. reconstructed within 3◦ from their true direction) from anE−2 energy spectrum are retained, with respect to the number which pass theMuon Filter. In subsequent analysis steps, more-computationally-intensive reconstructions can be performed, including angular-resolution estimators, energy estimators and likelihood fits applied to different subsets of the recorded photons. 4.3 NToO selection variables The background rejection of theOnline Level 2 Filteris still not sufficient for NToO data analyses, since the sample is still dominated by mis-reconstructed atmospheric muons. Only approximately 1 out of every 1000 events is a neutrino. Thus, based on variables from theOnline Level 2 Fil- ter reconstructions the final event sample is selected by employing additional quality cuts. The following additional cut variables are used: Split Fits The track reconstruction for a correctly-reconstructed up-going track should be sta- ble against changes to the set of DOMs used for reconstruction. On the other hand, for two co- incident down-going muons wrongly reconstructed as one up-going track, or for other cases of mis-reconstruction, changes to the DOM set will have a much larger impact on the reconstructed direction. This is the rationale for splitting the DOM set used in the reconstruction into two parts and subsequently performing a maximum-likelihood fit on each part separately. Different criteria can be used to split the DOM set: • geometrical splitting divides the hits according to their position with respect to the center of gravity (COG) of all hits. The center of gravity is calculated as ~xCOG = ∑NDOM i=1 qi × ~xi ∑NDOM i=1 qi (4.4) where~xi are the positions of the individual hit DOMs andqi the charge of each hit DOM. The center of gravity~xCOG is then projected onto the track obtained with the MPE fit, yielding the – 9 – point ~xproj COG . Each hit location is then also projected onto the track, andcompared to~xproj COG . Hits whose projections lie on one side of~xproj COG are sorted into one set, hits whose projections lie on the other side are sorted into a second set. • time-based splitting divides the hits into two sets by comparing each hit to the mean of all hit timestmean. Hits that fulfill ti ≤ tmeanare sorted into one set, hits that fulfillti > tmeanare sorted into another set. For each of the four subsets of hits a standardLinefit is performed which acts as a seed for a two-iteration SPE maximum-likelihood fit. The zenith angleθi resulting from the SPE fit, when only the time and charge of the first hit on each DOM are taken into account in the reconstruction, is used to define the cut variable ∆Split/SPE= max i∈split fits (cosθi − cosθSPE) . (4.5) Bayesian Likelihood Ratio The probability that an event selected by theOnline Level 2 Filter and reconstructed as up-going (i.e. zenith angleθSPE> 90◦) is truly a neutrino-induced muon and not a mis-reconstructed air-shower muon is very small (∼ 10−3). A useful additional cut variable can be derived by forcing a down-going track fit and calculating the likelihood ratio to the SPE fit. This cut is motivated by a Bayesian approach [61] to event reconstruction. Bayes’ Theorem in probability theory states that for two assertionsA andB, P(A |B) P(B) = P(B |A) P(A), whereP(A |B) is the probability of assertionA given thatB is true. IdentifyingA with a particular muon track hypothesisµ, andB with the data recorded for an event in the detector, we have P(µ |data)= LSPE(data| µ) P(µ), where we have dropped a normalization factorP(data), which is a constant for the observed event. The functionLSPE is the regular SPE likelihood function, andP(µ) is the so-called prior function, which is the probability of a muon passing through the IceCube detector, and is given by P(µ) = 2.50 · 10−7 cosθ1.68 · exp ( − 0.78 cosθ ) , (4.6) This function is a fit to the simulated zenith-angle distributions of down-going cosmic ray muons triggered by IceCube, see also [62, 63] for more details. By accounting in the reconstruction for the fact that the flux of down-going muons from cosmic-rays ismany orders of magnitude larger than that of up-going neutrino-induced muons, the number ofdown-going muons that are mis- reconstructed as up-going is greatly reduced. The difference of the logarithms of the SPE likelihood fit and the Bayesian-fit likelihood ∆SPE/Bayesian= log10LSPE− log10(LSPE(data| µ) P(µ)) (4.7) is another useful cut variable, especially for events that have been reconstructed with a zenith angle close to the horizon. – 10 – 4.4 NToO cut optimization For neutrino searches, muons produced in cosmic-ray-induced air showers are the dominant back- ground in IceCube for tracks coming from the Southern Hemisphere. They trigger the detector with a rate 106 times higher than atmospheric neutrinos. Tracks from the Northern Hemisphere originate mostly from atmospheric neutrino interactions that produce muons. BOTH of these back- ground components are simulated using Monte Carlo studies. Cosmic-ray air showers are simulated using a patched version of CORSIKA [64]. The spectra of cosmic-ray primaries are sampled from the phenomenological Polygonatomodel [65] and the background datasets were produced with the Sybill [66] hadronic-interaction model. A sizable fraction of events in the IceCube detector include several muons from distinct air showers. These so-called coincident air-shower events are simulated as well. Neutrino events are simulated based on the Monte Carlo generator ANIS [67]. ANIS generates neutrinos, propagates them through the Earth and finally forces them to interact in a volume around the detector. As different primary neutrino spectra are needed by different analyses, one usually simulates a generic primary spectrumdN/dEµ ∼ E−γν whereγ = 1 or γ = 2. The events can be re-weighted to the desired spectrum for each analysis. The output of the neutrino generator in the case of a charged-currentνµ interaction is a muon produced at the interaction vertex andthe accompanying hadronic cascade. The cascade is not simulated in detail. The simulation of the muon propagation and the muon energy loss is essential to obtain the light distribution in the detector. A software package called PROPOSAL [68] is employed for that purpose. PROPOSAL calculates the continuous energy loss ofthe muon as well as the stochastic losses due to bremsstrahlung, pair production, photonuclear interactions and delta electrons. Fi- nally, the detector simulation is concerned with the response of the PMTs to detected photons, the digitization of the PMT waveform in the DOM, and the trigger system. This is done by the IceCube software framework called IceTray [69]. In order to achieve the best possible sensitivity, the cuts on the variables described in the previous section have been optimized together in independent zenith-angle regions. For each com- bination of cut values, the rate of remaining data events wasused as the approximation for the background rate. The rate of signal events for a given flux wasestimated from neutrino simula- tions, assuming a neutrino flux with a spectral indexγ = 2 . This is motivated by the fact that diffuse shock acceleration leads to power-law spectra with a spectral index around 2 [72, 73] and neutrinos originating in cosmic rays interactions near thesource are expected to follow a similar spectrum. The cut optimization was repeated for flares of different durations ranging from 1 day to 20 days; as an example see Figure1. As traditional minimizers like MINUIT [59] were found to converge on local minima a particle swarm optimization algorithm was used [60]. For simplicity the minimization assumes that the flare time window is known. In the binned point-source method the radius of the on-source bin is a free parameter. The optimal bin size as a function of zenith depends on the angular resolution and the background rate of atmospheric neutrinos at each zenith angle. The search-bin radius has also been optimized together with the cut variables to yield the best sensitivity. The first set of the NToO cuts was optimized using the IC-2011 season, and later redefined – 11 – 0.0 0.2 0.4 0.6 0.8 1.0 sin(δ) 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 S e n si ti v it y [ 1 0− 7 G eV cm −2 s− 1 ] Figure 1. Simulated IceCube sensitivity as a function of source declination to a neutrino flux withdN/dE ∼ E−2 for a neutrino flare length of 10 days and for the IC-2012 data set. The sensitivity did not change significantly for other IceCube data seasons. The sensitivity is defined as the flux required for a 3 sigma detection with a probability of 90%. using data from the IC-2012 season. Only the cut on the Bayesian likelihood ratio was changed. Cut Level Selection criterion Atms. µ Data Atms. νµ Astro. (mHz) (mHz) (mHz) ×10−3 (mHz) 0 cosθMPE ≤ 0 1010.5 1523.81 7.166 6.23 1 SLogL(3.5)≤ 8 282.49 504.44 5.826 5.62 2 NDir ≥ 9 8.839 22.01 3.076 4.06 3 ((cosθMPE > −0.2) AND (LDir ≥ 300 m) OR 1.124 4.30 2.313 3.69 (cosθMPE ≤ −0.2) AND (LDir ≥ 200 m)) 4 ∆Split/MPE <0.5 0.100 2.15 1.899 3.26 5 ((cosθMPE ≤ −0.07) OR 0.080 2.08 1.880 3.25 ((cosθMPE > −0.07) AND (∆SPE/Bayesian≥ 35)) ) 6 ( (cosθMPE ≤ −0.04) OR 0.075 2.06 1.875 3.24 ((cosθMPE > −0.04) AND (∆SPE/Bayesian≥ 40))) Table 2. IceCube neutrino selection cuts and corresponding passing event rate for the IC-2012 season. At an final selection an event has to fulfill all cut criteria to pass the selection (i.e. a logical AND condition between the cut levels is applied). The atmospheric-neutrino flux is based on the prediction by Honda [71], but atmospheric-muon rate is calculated from CORSIKA simulations. The event rate for IceCube data stream corresponds to the total livetime of 332.36 days. The astrophysical neutrino flux is estimated assumingdN/dE = 1 · 10−8 GeVcm−2s−1( E GeV)−2. (Atms.= atmospheric, Astro.= astrophysical) The final set of smooth cuts resulting from the cut optimization is listed in Table2 and the – 12 – optimal search-bin radius as a function of declination angle (δ > 0◦) has been parametrized as r = 1.2◦ + 1.4◦ · sin(δ) . (4.8) Table2 shows also the influence of each selection cut on event rate for data, simulated atmo- spheric neutrinos and muons. The experimental data sample, after application of theOnline Level 2 Filter(Cut Level 0), consists of 4.3 × 107 events acquired within a total livetime of 332.36 days. At this level, see Table2, atmospheric muons dominant the contribution to the measured data sample. However, these mis-reconstructed events being truly down-going andreconstructed as up-going are almost removed by our selection criterion, i.e. the passing rate isreduced by 99.9925 % with respect to Cut Level 0. We also see that at the final selection cut the datarate reach the level of atmospheric muon neutrinos, i.e. about 2 mHz, and selection criterion keeps about 52% of the signal events (with respect to Cut Level 0) for anE−2 signal neutrino spectrum of astrophysical neutrinos. The same set of cuts was used for the next IceCube seasons: IC-2013 and IC-2014 thanks the very stable behavior of the IceCube detector and its performance. 4.5 Properties of the neutrino sample The event selection results in an event rate of about 2 mHz anda median angular resolution of 0.5◦ for an E−2 signal neutrino spectrum. Figure2 depicts the median angular resolution of the final neutrino sample as a function of neutrino energy and as a function of neutrino declination angle7. 2 3 4 5 6 7 8 9 10 log10(Eν [GeV]) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 M e d ia n a n g u la r re so lu ti o n [ ◦ ] MPE fit on final level (a) 0.0 0.2 0.4 0.6 0.8 1.0 sin(δν) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 M e d ia n a n g u la r re so lu ti o n [ ◦ ] MPE fit on final level (b) Figure 2. IceCube median angular resolutions based onCramér-Rao Resolution Estimatorfor the final selected neutrino sample as a function of (true) neutrino energy (left panel) and declination (right panel), assuming in simulation a primary neutrino spectrum withΦ(E) ∼ E−2). The error bars depict the 16th and 84th percentile of the angular resolution. Neutrino angular resolution defined as the median of the point- spread function of the true neutrino direction and the reconstructed muon direction. Figure 3 depicts the effective area for muon neutrinos as a function of neutrino energy in different declination regions. It is worthwhile to note that theeffective area reaches only about 7IceCube is located at the South Pole, so the relation betweenzenith angle and declination is given by simply transformation:θ = δ + 90◦. – 13 – 1 3 5 7 9 log10(Eν [GeV]) 10-6 10-4 10-2 100 102 104 E ff e ct iv e a re a [ m 2 ] Declination 0 ◦ - 10 ◦ 10 ◦ - 30 ◦ 30 ◦ - 60 ◦ 60 ◦ - 90 ◦ Figure 3. Simulated IceCube effective area as a function of the (true) neutrino energy for the final neutrino selection derived from the 2012/2013 data set. The strongly energy dependent neutrino-nucleon cross sec- tion leads to the observed behavior of an effective area that is generally increasing with energy, untilneutrino absorption dominates. For larger declinations the effect of neutrino absorption sets in at lower energies due to the longer path through the Earth. The acceptance is similar for other IceCube data seasons. 1 m2 at 103.2 GeV. For events with declination between 10◦ and 30◦ the effective area reaches a maximum of about 1000 m2 at 106.5 GeV and begins to drop above 107.5 GeV due to absorption of neutrinos in the Earth. For neutrinos very close to the horizon (0◦ ≤ δ ≤ 10◦) and for neutrino energies greater than 108 GeV the effective area can reach 104 m2. The efficiency of the event-selection cuts with respect to theOnline Level 2 Filteris depicted in Figure4 for all events (dashed) and for events that have been reconstructed within angle∆Ψ < 3◦ of their true direction. Well-reconstructed events are selected with an efficiency of more than 60 % above 1 TeV; while the overall peak efficiency of about 80 % is reached between 100 TeV and 10 PeV. As we already mentioned above, the main selection cuts are optimized for a neutrino power- law spectrum with a spectral indexγ = 2 . However, several Galactic gamma-ray sources have energy spectra with energy cutoffs at a few TeV [74], supporting the idea that Galactic neutrino spectra may present cutoff spectra as well [75, 76]. Also, recent IceCube results show that the astrophysical neutrino flux has a neutrino spectrum softer thanE−2. The IceCube neutrino flux can be well described by an unbroken power law with best-fit spectral index 2.50± 0.09 [70]. Therefore in Table3 the efficiency of neutrino selection for softer spectral indexes (e.g. 2.5 and 2.7) is also shown. As we can see, for softer spectra, the performance of NToO cuts is about 20 % worse, but the signal efficiency is still above 50% for well-reconstructed events. 5 The time-clustering algorithm The timescale of a neutrino flare is not fixeda priori and thus a simple rolling-time-window ap- proach is not sufficient to detect flares. The time-clustering approach that was developed for an – 14 – E−2 (∆ΨMPE < 3◦) E−2.5 (∆ΨMPE < 3◦) E−2.7 (∆ΨMPE < 3◦) 52 % (73 %) 43 % (63 %) 39 % (59 %) Table 3. Efficiency (from simulation) of the neutrino selection cuts with respect to theOnline Level 2 Filter (in %). The efficiencies for well-reconstructed events (defined as events with ∆ΨMPE < 3◦) are given in parentheses. 1 3 5 7 9 log10(Eν [GeV]) 0.0 0.2 0.4 0.6 0.8 C u t e ff ic ie n cy w .r .t . O n lin e L e v e l 2 All events ∆Ψ<3 ◦ Figure 4. Efficiency of the neutrino-selection cuts for IceCube with respect toOnline Level 2for all events (blue, dashed line) and events with angular reconstructionerror,∆Ψ < 3◦ (green, solid line). unbiased neutrino flare search [50] looks for any time frame with a significant deviation of the number of detected neutrinos from the expected background.The simplest implementation uses a spatially binned approach where neutrino candidates within a fixed radius around a source are regarded as possible signal events. Figure 5. Schematic of the time-clustering algorithm. For an IceCube event in an on-source bin detected at time t7 the significances of all clusters formed with events detected up to 21 days back are calculated. If a neutrino event is detected at timeti from the search bin around a given source, the expected backgroundNi, j bg is calculated for all other eventsj within a time window∆t j = t j − ti around that bin (see Figure5). To calculateNi, j bg the detector efficiency as a function of the azimuth angle and the uptime has to be taken into account. The number of expected background eventsNi, j bg in a time window [ti , t j ] for a source at a certain declination is given by Ni, j bg = ti, jupṄ(θ)ǫ(Φ(t)) (5.9) whereṄ(θ) is the zenith-angle-dependent rate of data used as background events,ti, jup uptime in a – 15 – time window [ti , t j ] and ǫ(Φ(t)) the normalized azimuth distribution of IceCube events (see Fig- ure6). The Poisson probability of observing the multiplet (i, j) by chance is then calculated according to pobs= ∞ ∑ k=Ni, j obs−1 (Ni, j bg)k k! e−Ni, j bg (5.10) whereNobs is the number of detected on-source neutrinos betweent j andti. Nobs must be reduced by one to take into account the bias introduced by the fact that the background is measured in the time window defined by thejth event. Most very high energy flares detected so far have a duration up to several days, thus we constrain our search for time clusters of neutrinos to 21 days so as to minimize the statistical trial factor penalty. The probability pobs is often expressed in terms of the distance to the center of a normal distribution measured in units of standard deviations thatresults in the same cumulative probability in the right tail (e.g. a probability of log10 (pobs) = −2.87 is often quoted as 3σ). If the cluster with the highest significance exceeds a certain threshold (e.g. corresponding to 3σ) the detector stability is first checked and, if appropriate, an alert is sent to a partner experiment (atmospheric-Cherenkov telescope) to initiate a follow-up observation. The physical layout of the IceCube, with the instrumented strings positioned on a hexago- nal grid, results in an increased trigger rate for events that propagate along the symmetry axes. Therefore, the expected number of background events in a time window for a source at a certain right ascension depends on the azimuth-angle range coveredduring that time. This natural azimuth dependence is reinforced by cut variables that favour events that pass close to many strings (e.g. di- rect hits and direct length). For time-integrated point-source searches, the azimuth dependence 0 1 2 3 4 5 6 Azimuth [radians] 0.7 0.8 0.9 1.0 1.1 1.2 A rb it ra y U n it s 1.3 Figure 6. The normalized distribution of IceCube events as a function of azimuth. The dependence is caused by the hexagonal layout of the grid of IceCube strings that produces symmetry axes. is usually neglected because it is smoothed in right ascension by the rotation of the Earth over – 16 – long integration times. However, in a time-dependent analysis the azimuth dependence becomes important for timescales shorter thanti − t j < 2 days. The stable uptime betweenti, jup in a time window [ti , t j] is calculated using the online detector stability monitoring (described in Sec.6) and combined with information about the start and stop times of the data-taking runs. 5.1 Alert rate, detection probability Since the NToO aims at the discovery of neutrino flares from astrophysical sources, it is important to define what astrophysical neutrino flux level would trigger an alert. In Figure7 (A) we show the flux as a function of declination that results in a trigger probability of 50% for significance thresholds corresponding to 3.0σ and 5.0σ for a flare duration of 10 days, while in Figure7 (B) the probability of triggering an alert as a function of the neutrino flux, assuming a spectral indexγ = 2 and a flare duration of 10 days, is shown for alert thresholds corresponding to 3σ and 5σ for a source at a declinationδ = 28.0◦. As we can see from these plots, a signal flux of the formΦ(E) = 6 · 10−8 GeVcm−2s−1( E GeV)−2 will on average trigger an alert with a probability of 50% for an alert threshold of 3.0σ. 0 10 20 30 40 50 60 70 80 Declination [deg] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Fl u x [ 10 −7 G eV cm −2 s− 1 ] Threshold: 3 σ Threshold: 5 σ 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Flux [10−7 GeVcm−2 s−1 ] 0.0 0.2 0.4 0.6 0.8 1.0 T h re sh o ld c ro ss in g p ro b a b ili ty Threshold: 3 σ Threshold: 5 σ 0.0 3.3 6.7 10.0 13.4 16.7 20.0 Number of events Figure 7. (Left panel) Neutrino flux required by IceCube at a given source declination to trigger an alert with a significance of 3σ (solid line) and 5σ (dashed line) with a probability of 50%.The neutrino spectrum is assumed to be an unbroken power law with a spectral index of2; the flare duration is 10 days. (Right panel) Probability to trigger an NToO alert as a function of flux for flares with a duration of 10 days at a declinationδ = 28.0◦, for alert thresholds of 3σ (red, solid line) and 5σ (blue, dashed line). The upper axis shows the number of required events needed for neutrino flux for alert with given significance. The number of accidental background alerts also needs to be estimated in order to calculate the total significance of all the alerts generated by the program, as well as to set sensible alert thresholds such that the partner observatory is not overwhelmed by follow-up requests. The number of follow-up requests allowed in a given time period is fixed by the Time Allocation Committees of the partner experiments. Figure8 shows the number of accidental background alerts as a function of the alert significance threshold. For a threshold of 3.2σ (MAGIC) this would result in a fake alert rate of about 0.1 alerts/(source· year). Thus, given the number of sources (around 70) in this – 17 – program for the MAGIC experiment this results in about 3 background alerts per year8 . This number is calculated taking into account that a source is on average visible with a probability of 40% by a partner observatory (i.e. if the source rises at least 30 degrees above the horizon for at least 30 minutes during dark time, the current phase of the Moon is less than 0.5 and the source distance to the Moon is larger than 60◦). For VERITAS, a higher alert threshold (3.6σ) leads to one expected background alert per year. 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Alert significance threshold [σ] 10-6 10-5 10-4 10-3 10-2 10-1 100 N u m b e r o f a le rt s/ (s o u rc e · y e a r) Declination 25.7 ◦ Declination 78.5 ◦ 2.2 2.9 3.6 4.5 5.5 6.5 7.7 −log10(p) Figure 8. Expected IceCube fake alert rates for the NToO caused by atmospheric neutrinos for different source declinations as a function of alert significance. 6 Data stability monitoring A dedicated monitoring system was implemented to minimize the rate of false alerts caused by problems with the detector itself, the data acquisition (DAQ) or the filtering software. IceCube has very extensive DAQ monitoring, and processing results which are available with a certain delay after data-taking. However, the monitoring does not provide information on the detector stability with high granularity but declares a whole run, with a usual duration of eight hours, as eithergood or bad. Problems such as a few strings of the detector dropping out of the data taking shortly before the end of a run do not render the data taken up to that point invalid. To ensure that alerts are issued during stable running conditions, a simple but effective online stability monitoring scheme has been developed. The scheme is based on the continuous monitoring (in 10 min time bins) of several trigger and filter event rates, representative of different event topologies from atmospheric muons and neutrinos. 8Since November 2013 the number of MAGIC sources was lowered to 18 and since April 2014 the alert threshold was set to 3.6σ, therefore the expected number of alerts decreased to one alert per two years. – 18 – 6.1 Rate measurements and data quality assessment The trigger rates of the detector, and the filter event rates of the online filters, are quantities that are both sensitive to problems affecting the data quality and simple to measure, record and evaluate. Trigger event rates (e.g. the SMT8) are sensitive to low-level problems, such as possible errors in the trigger configuration or an incorrect DOM calibration. Filter event rates can also be affected by these issues but, additionally, they give information about the stability of the filtering chain. Problems that affect event reconstruction or distributions of cut variablesused in a filter would also change the corresponding filter event rate. All trigger and filter event rates are measured by a central server using a dedicated software module. Events are counted in time bins of 600 seconds and thecorresponding rates and time-bin meta data (e.g. start and end of the time bin) are inserted into a relational database. This database is mirrored to the Northern Hemisphere to be easily accessible for offline studies. Storing the data in a relational database makes it convenient to retrieve any trigger and filter event rate for longer time scales such as hours or days. For each of the trigger and filter event rates approximately 5· 104 measurements are recorded in the database in a full year. The NToO selectsνµ-induced muon tracks to detect time-variable point sourcesof neutrinos. Any problem that affects the detection and reconstruction of these muons would therefore have an impact on this program. Thus the inputs derived from the ratemonitoring for the NToO should be related to the muon-related triggers and filters that form the basis of the neutrino event selection. The following trigger rates, filter event rates and ratios are used to check the stability: • Simple Multiplicity Triggerevent rate • Muon Filterevent rate • Online Level2 Filter rate • Ratio ofOnline Level2 Filter event rate toMuon Filterevent rate • Ratio ofOnline Level2 Filter event rate toSimple Multiplicity Trigger rate A combination of these rates and ratios form astability score, which will be described in Sec.6.2. As the final neutrino event selection is performed in a different subsystem, the final-level event rate is not recorded in the database. Due to the very low atmospheric-neutrino rate of about 2 mHz at the final cut level, the statistical error on the rate measurement with the default time binning of 10 minutes would be very large. Recording this rate with a different binning and combining it with the other rates would make the system much more complicated.Therefore, the final neutrino level event rate is not used as an input in the rate-based detector stability monitoring. 6.2 Stability-score calculation The atmospheric-muon rate depends on the development of theair shower and thus on the atmo- spheric density profile. As seasonal temperature changes ofthe atmosphere influence this density profile, the atmospheric-muon rate measured in the detectorshows a pattern of seasonal variation, – 19 – Da te R a te [ H z ] 4.0 4.5 5.0 5.5 6.0 6.5 7.0 Ju ne 2 01 2 Au g 2 01 2 O ct 2 01 2 D ec 2 01 2 Fe b 20 13 A pr 2 01 3 Figure 9. IceCube rate of events passing theOnline Level 2 Filterover the complete IC-2012 season. The solid red line depicts the moving average of theOnline Level 2 Filterevent rate; the blue lines show the 1σ exponential standard deviation around the average. Each dot corresponds to a 10 min time bin. see Figure9. On top of these slow seasonal variations, changes in the IceCube trigger rate on the time scale of hours to days are apparent. This background of atmospheric muons dominates all trigger and filter event rates used for the online stability monitoring. Therefore, a simple stability decision based on the deviation from fixed reference rates cannot be used. A common method to predict a time series of (potentially noisy) measurements is a moving- average filter. The filter smooths noisy data to either produce smoothed data for presentation purposes or to make forecasts of the time series. Three different averaging methods are usually employed, simple moving averages, weighted moving averages or exponential moving averages. An N-period simple moving average weights the lastN measurements equally to produce the smoothed prediction. In doing this, the average always lagssudden changes in the data. This can be overcome by applying a weight to each measurement in the averaging process, depending upon how long ago the measurement was taken. This requires two inputs, the number of measurements N to average over and the weight function. In the case of the stability monitoring one would assign higher weight to more recent measurements so that recent measurements such that the average reacts faster to changes of the rates caused by a changing muon rate. Another way to achieve this fast adaptation is an exponential moving average. Given measure- ments of a quantityx (e.g. a filter event rate) at time stepsi (denoted asxi) the exponential moving averageS at time stepi is calculated as S1 = x1 (6.11) Si = αxi + (1− α)Si−1 ; for i > 1 . (6.12) The parameterα determines how fast the weight given to past measurements decays; higher – 20 – α gives more weight to recent observations and reduces the impact of past measurements more rapidly. The step width is given by the 600-second time-bin width of the rate monitoring. Analogously to the exponential moving average, an exponential moving standard deviationσ can be defined as σi = √ 〈 x2〉 − Si · Si . (6.13) Here 〈 x2 〉 denotes the exponential moving average (see Eq.6.12) of x2. To update the expo- nential moving average only the most recent calculated value ofSi is needed. This is in contrast to the simple and weighted moving averages, where the pastN data points need to be kept for updates of the average. Therefore, an exponential smoothing has been chosen in the stability monitoring in order to simplify the implementation of the moving-averagecalculation. 6.3 Implementation of the stability-score calculation The stability score provides a metric to compare the currentdetector trigger and filter event rates in time bini to an exponential moving average of these rates up to that point in time. The averages and standard deviations are calculated for the filter event rates and ratios with the parametersα = 0.01 9. In order to judge the detector stability in a time bini, a combined scoreξi is calculated as ξi = ∑ j |x j i − S j i−1| σ j i−1 (6.14) where j enumerates the filter event rates and ratios andSi−1 andσi−1 are the exponential moving averages and standard deviation, respectively, prior to the time bini. If ξi is below a certain thresh- old ξthreshthe time bini is considered to be of good quality and the averages and standard deviations are updated according to Eq.6.12and Eq.6.13. If ξi is above the threshold the data quality in this time bin is judged to be bad. In this case, all final-level events in that time bin are discarded, the time bin is counted as detector dead time and the averages andstandard deviations are not updated with the rates from time bini. The threshold employed in the NToO isξthresh = 8 . For this threshold, comparisons of the online stability monitoring with the more extensive offline quality checks show that the online system reliably identifies unstable detector conditions. As an example, for IC-2012 the data taking season started on 15 May 2012 at 10:05:48 UTC and ended on 2 May 2013 at 09:48:49 UTC. Of the 351.98 days between the season start and end, 322.17 days are marked as good by the stability monitoring. This results in an uptime fraction of 91.5%. Typical IceCube offline analyses for this season report an uptime fraction of around 95 %. 7 Technical design of the alert system The NToO system runs online at the South Pole with minimal human intervention. In order to maximize the uptime of the system it has to be very stable. Themain design driver was that the 9Until 25 November 2012α = 0.005, which gave more weight to past measurements. In order tobe better able to cope with fast rate variations due to weather changes the value ofα was changed to 0.01. – 21 – failure of any of the sub-components should not lead to the loss of the online program’s data. Therefore all components have been separated as much as possible and intermediate results are stored frequently. The basic components of the NToO are depicted in Figure10. Computer Center in the NorthSouth Pole IceCube data Stability monitoring Figure 10. Schematic of the design of the IceCube NToO. In the first step, the selection of neutrino candidate eventshappens inside the IceCube data- processing system at South Pole. Each event is serialized tothe text-based and human-readable JSON (JavaScript Object Notation) format and written to a dedicated directory on disk. The event directory is checked for new events every 30 seconds by the daemon that runs the time-clustering algorithm. This daemon keeps a list of events detected in thelast 21 days from each of the moni- tored sources and adds new events to the appropriate list if the detector was stable when the event was detected. For each new event that falls into the search bin of one of the monitored sources the time-clustering algorithm for that particular source is run. If the significance for an evaluated event cluster exceeds a certain threshold (see below), an alert message containing the source name, event positions,event times and the significance of the cluster is generated. The alert message is then sent to the University of Wisconsin via the IceCube Teleport System (ITS) which uses the network of Iridium satellites. This low-bandwidth connection allows short messages to be sent from the South Pole without any significant delay. Once the message arrives in the North it is checked to see whether it represents areal alert or atest alert from a monitoring source (see next section for an explanation of the difference). If it is areal alert, the alert is forwarded to the respective partner experiment, MAGIC or VERITAS or to both of them if the alert significance is above the threshold for MAGIC and VERITAS. Currently the alerts are forwarded via email and follow-up observations are initiated manually. The total time delay between the (latest) neutrino event detected by IceCube andthe moment that alert is forwarded to the partner experiment is on average 12 minutes. 8 Monitoring of alert system The low rate of accidental background alerts from atmospheric neutrinos (see Figure8) makes it necessary to add additional monitoring to the system in order to ensure that all components are working as expected. Ideally, this monitoring should cover the whole chain, from the event – 22 – selection and stability monitoring, to the generation, sending and receiving of alerts. In order to reach this goal, so-called test alerts can be generated atthe South Pole using the same event sample as used by the NToO. To achieve a sufficiently large rate of test alerts the number of source positions that are monitored should be high. Thus, 1000 random positions were chosen as test sources, with a flat distribution in cosθ. The threshold for sending a test alert should be lower than the corresponding threshold for the physics alerts in orderto achieve a high number of test alerts. Thus, the threshold for test alerts was set topobs= 0.1 (see Eq.5.10). Using the same original neutrino event sample for both the physics alerts and for the test alerts would unblind additional positions in the sky. The usual wayto test point-source analyses in a way that preserves blindness is through scrambled data sets. The event times are shuffled and new sky coordinates are calculated for each event. Due to the location of the IceCube exactly at the geographic South Pole, only the right ascension is affected by this procedure. In the case of the NToO, however, a continuous stream of events must be shuffled while preserving properties such as the azimuth and time distribution of the events. 19 20 21 22 23 24 25 Right Ascension [ ◦ ] 23 24 25 26 D e cl in a ti o n [ ◦ ] Source position Weighted average (a) Spatial event distribution 56110 56112 56114 56116 56118 Event time [MJD] 10-2 10-1 100 101 E v e n t w e ig h t Alert data Events: 8 −log10(p) =4.852 ∆t=10.207 days δ=24.7 ◦ (b) Temporal event distribution Figure 11. Left panel shows spatial distribution of IceCube events (marked by stars) contributing to a test alert. The circles describe the estimated angular error for the reconstructed tracks. The dashed circle indicates the size of the on-source bin. Right panel shows the temporal distribution of eight events depicted in the left panel. The height of the bars in right panel corresponds to the event weights derived from the angular reconstruction uncertainties. The weights are used to calculate the weighted average direction of the events, shown as an inverse full triangle in the left panel. To randomize the event coordinates in right ascension for the blind generation of test alerts one could, in principle, assign each event a random azimuth angle. This would, however, destroy the pattern due to the azimuth-dependent efficiency of the detector, see Figure6. In order to preserve this pattern in the scrambled dataset, the conversion of local coordinates (zenith and azimuth) to sky coordinates (right ascension and declination) for eachevent is done not with its original event time, but with the time of the previous neutrino event. The first event after the startup of the event- selection process is assigned a random right ascension. As the rate of atmospheric-neutrinos is about 2 mHz this results in a random shift of each event by several degrees on average. The test alerts generated from the blinded event sample are collected and analyzed. To aid – 23 – −5 0 5 10 15 20 25 30 35 40 Alerts/24 hours 0 5 10 15 20 25 30 35 40 Fr e q u e n cy (a) Number of test alerts per day 0 100 200 300 400 500 600 ∆t between alerts [min] 100 101 102 103 N u m b e r o f a le rt s (b) Time between test alerts 0 2 4 6 8 10 12 Events/alert 100 101 102 103 104 N u m b e r o f a le rt s (c) Number of events in alert 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 −log10(p) of alert 10-1 100 101 102 103 N u m b e r o f a le rt s (d) Significance of test alerts Figure 12. Monitoring information derived from the test alerts for the IceCube NToO. See text for a de- scription. the interpretation of these alerts a web page was created that displays each alert. The web page is automatically updated upon receiving a new test alert. In addition to each individual test alert, the global properties of all test alerts received to date are shown, e.g. the rate of test alerts, their zenith distribution and their significance distribution. Each alert is displayed on a web page showing the distribution of events contributing to the alert both in time and space (see Figure11). In the case of an alert for an astrophysical source this allows for a rapid inspection of the event properties. As an example, Figure11 depicts a high-multiplicity test alert, consisting of 8 events, issued on 7 July 2012. It corresponds to the test alert with the highest significance in the IC-2012 data taking season with− log10(pobs) = 4.85. The contributing events were detected over a duration of 10.2 days. For each alert the weighted-average direction is calculated as xavg = ∑ i ∑ j σ 2 j σ2 i xi (8.15) where theσi are the resolution estimates of the individual events (i.e.Cramér-Rao Resolution Estimate) and thexi are their directions described by the right ascension and declination. The – 24 – weighted average is displayed as a full triangle in the spatial event plot, the individual event weights (1/(σ2 i / ∑ j σ 2 j )) are represented as the height of the bars in the temporal plot. Plots of the global properties of all monitoring alerts received to date can be used to monitor the stability of the operation of the whole alert system. Forexample, changes in the total test-alert rate can indicate problems with the event selection or uptime calculation. Long delays between the detection of the events and the arrival of the test alertsin the North can be a sign of problems with the data processing, the stability-monitoring database or the transfer of test alerts to the North. Figure12shows some of the quantities derived from the test alerts which allow the alert system to be monitored. An important quantity to monitor is the rate of received testalerts (Figure12(a)). The regular arrival of test alerts in the North is used as a “heartbeat” for the overall system. If no test alert is received for more than six hours, a warning email is issued toa list of people so that the cause can be investigated. Warning emails are reissued every two hours if no new alert has been received in the meantime. This threshold of six hours for warning emailsis rather conservative, as can be seen in Figure12(b). This figure shows the histogram of the wait times between subsequent test alerts. It follows the expected exponential distribution reasonably well. A time difference of six hours is well within the range of expected waiting times. However, toenable timely interventions, an early warning is preferred. Figure12(d)depicts the distribution of the significances of the test alerts. 9 Results of NToO program The IceCube follow-up programs such as optical, X-ray follow-up and NToO have been running in a stable fashion for a few years and are taking high-quality data from both IceCube and the follow- up instruments. The results are the subject of a forthcomingpublications. Only a short status report will be given here, highlighting the most important results. For the optical and X-ray follow-up, no significant excess ofmultiplets was found since the inauguration of the program in December 2008. One neutrino triplet was found in the data in February 2016, this result will be subject of a forthcoming IceCube publication. In March 2012, the most significant alert during the first three years of operation of the optical and X-ray follow-up program was issued by IceCube. In the follow-up observations performed by the PTF, a Type IIn supernova PTF12csy was found 0.2◦ away from the neutrino alert direction [35]. The supernova has a redshift ofz = 0.0684, corresponding to a luminosity distance of about 300 Mpc, and the Pan-STARRS1 survey shows that its explosion time was at least 158 days (in the host-galaxy rest frame) before the neutrino alert, implying that a causal connection is unlikely [35]. From the inauguration of the NToO program, on 14 March 2012, to 31 December 2015, 14 alerts were sent: 4 in 2012, 2 in 2013, 6 in 2014 and 2 in 2015. The program continues, and alerts during 2016 and beyond will be reported elsewhere. From the above-mentioned 14 alerts issued, 8 of those were forwarded and 4 (out of 8) were followed-up by MAGIC or VERITAS observations. Another 6 alerts (out of 14 issued) were not forwarded due to bad observing conditions or the partner experiment was not operational. Table4 gives an overview of all of the alerts generated by the NToO upto 31 December 2015. Below, only the alerts forwarded to the partner experimentsare discussed in more detail. – 25 – alert Source Time − log10(pobs) Nobs Duration Follow-up Observed ID (UTC) (days) Instrum. yes/no 1 PG 1424+240∗,∗∗ 2012-04-14 23:47 3.47 6 7.617 No - 2 GB6 B1310+4844 2012-08-20 09:53 3.75 6 6.344 No - 3 4C15.54 2012-09-13 01:52 4.06 2 0.001 MAGIC No 4 SBS 1150+497∗∗ 2012-11-09 07:28 4.64 6 4.169 VERITAS Yes 5 RGB J0152+017∗ 2013-04-29 06:36: 4.07 8 15.801 No - 6 RGB J0505+612∗∗ 2013-09-12 20:00 3.31 (4.10) 7 (10) 11.790 (20.73) MAGIC Yes 7 1ES 2344+514∗ 2014-02-19 23:18 4.07 (4.23) 8 (9) 12.844 (16.40) VERITAS Yes 8 1ES 1959+650∗ 2014-03-09 10:28 3.40 9 20.944 MAGIC No 9 B3 1708+433 2014-06-22 02:42 4.34 3 0.118 No - 10 PKS 1717+177∗∗ 2014-09-24 13:47 3.20 2 0.007 No - 11 MG4 J200112+4352∗ 2014-10-05 15:05 4.05 9 18.631 VERITAS Yes 12 B3 1343+451 2014-11-16 17:00 3.64 (5.04) 3 (4) 0.301 (0.576) VERITAS No 13 AO 0235+164∗∗ 2015-04-27 04:55 3.97 8 16.395 No - 14 CGRaBS J0211+1051∗∗ 2015-07-05 00:06 4.09 4 1.205 VERITAS No Table 4. Overview of the IceCube alerts generated by the NToO up to 31December 2015; see text for more details.Time of alert corresponds to the time when alert was received at North. (Follow-up Instrum.= Follow-up Instruments; Numbers in brackets correspond to the followed alert during the next few days,∗ - known VHE source,∗∗ - existing VHE limit in [77]). The most interesting alert (alert #4 in Table4 ) was generated on 9 November 2012, consistent with position of the source SBS 1150+497 (located at zenith angleθ = 139.5◦, with respect to IceCube). The alert comprised six events observed during 4.169 days. The spatial and temporal distribution of these events is shown in Figure13. The Poisson probability (pre-trial) for this observation is− log10(pobs) = 4.64, the post-trial probability− log10(pobs) = 2.60, making it the most significant alert sent during this IceCube season (IC-2012). The alert was forwarded to the VERITAS collaboration and resulted in a follow-up observation. Due to poor weather and bright moonlight conditions, VERITAS observations were not possible until 12 November 2012, at which point the source was visible at low elevation at the very end of the night. A further observation was made on the following night giving a total exposure time of 71.5 min. No evidence for gamma- ray emission was seen from the position of the source, givingan integral flux upper limit (99% confidence) above 300 GeV of 3.0 × 10−10 cm−2s−1 for an assumed differential spectrum with spectral indexγ = 2.5. Another high-significance alert was sent to VERITAS on 19 February 2014, followed by a second alert on 23 February 2014, spatially coincident with the source 1ES 2344+514 (alert #7). The first of these was triggered by 8 neutrinos observed over a period of 12.844 days, with − log10(pobs) = 4.07. An additional event, observed 3.6 days later, increasedthe p-value to − log10(pobs) = 4.23, the post-trial probability− log10(pobs) = 2.31, which resulted in the second forwarded alert for this source. The source was barely visible to VERITAS (zenith angle> 60◦), and weather conditions were poor. The online VERITAS analysis showed no evidence for gamma- ray emission (no excess was detected), indicating that the source flux was likely not exceptionally high above a few TeV. During the next IceCube season (IC-2014) another two alertswere sent to VERITAS. The – 26 – 174 176 178 180 182 Right Ascension [ ◦ ] 47 48 49 50 51 D e cl in a ti o n [ ◦ ] Source position Weighted average 56236 56237 56238 56239 56240 Event time [MJD] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 E v e n t w e ig h t Figure 13. Left panel shows position of events (star symbols) and related uncertainty (circles) from the alerts that were sent to VERITAS on 2012 November 9. The weighted average of the contributing events is calculated using an event-by-event angular resolution estimator. The dashed circle indicates the size of the on-source bin. Right panel shows the temporal distributionof eight events depicted in the left panel. first, generated on 5 October 2014 corresponded to the sourceMG4 J200112+4352 (alert #11), which had been recently reported as VHE emitter by the MAGIC collaboration [78]. A one-hour observation was performed, but under extremely poor weather and bright moonlight conditions. No conclusion regarding the gamma-ray flux state is possiblewith these data. The second alert was issued by the NToO system for the source B3 1343+451 on 16 November 2014 (alert #12), but the source was again barely visible (zenith angle larger than 60◦) and so follow-up observations were not performed. The last alert was sent to VERITAS on 5 July 2015 for the source CGRaBS J0211+1051 (alert #14), but at this time VERITAS was undergoing itsannual summer shutdown, and so no observation was made. For MAGIC, the first alert was sent on 14 April 2012, spatiallycoincident with the source 4C15.54 (alert #3). However, as the MAGIC telescope was in a commissioning phase, the alert could not be followed up. Then, a series of four alerts were issued by the NToO from 12 September 2013 to 21 September 2013 for the source RGB J0505+612 (alert #6). The alert resulted in a follow-up observation by MAGIC (1 hour), which showed no statistically significant evidence for gamma-ray emission. The computed integral flux upper limit (99% confidence) at energies>200 GeV is 1.57 x 10−11 cm−2s−1. The last alert forwarded to MAGIC was generated on 9 March 2014 for the source 1ES 1959+650 (alert #8), but the low elevation of the source precludedobservations. 10 Recent and upcoming improvements The currently deployed neutrino event selection in the NToOemploys simple cuts on a number of variables that discriminate between signal neutrinos and the atmospheric-muon background. The cuts on these parameters have been optimized to achieve best sensitivity. However, further improvements in signal and background separation should bepossible through the use of more sophisticated discrimination algorithms, such as boosteddecision tree (BDT) [79] and multivariate learning machines. The aim is to replace the present NToO selection by developing a new event – 27 – 10-1 10-2 10-3 10-4 10-5 10-6 10-1 100 101 BDT Score R a te p e r b in [ H z ] d a ta /m c r a ti o ----- Data-- -= Signal E-2 (arb. norm) Atmos. muons Atmos. neutrinos Total MC -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 0.2 0.3 B D T S c o re cos(zenith angle) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.1 10-7 10 -6 10 -5 10 -4 10 -3 R a t e p e r b in [ H z ] Figure 14. (Upper panel) IceCube distribution of BDT score for the ensemble of trees trained with anE2 spectrum. Vertical dashed line corresponds to the optimized BDT cut (Northern Hemisphere). (Lower panel) The BDT score as a function of cos(zenith angle). The red linekeeps approximately an equal rate per zenith bin (≃ 10−4 Hz). A piecewise polynomial function is then fitted to the redcurve (black curve). selection, which could also be used by other IceCube follow-up programs. This new event se- lection is comparable to offline point-source samples and will cover the entire sky. Here, a short description of the new BDT selection is presented, which hasbeen implemented for the IC-2015 data-acquisition season. For the new BDT selection scheme, the multivariate cuts werebased on 14 observables ob- tained by choosing parameters with low correlation in the background event sample, but with high discriminating power between signal and background. Observables specifying the geometry and time evolution of the hit pattern, as well as the quality and consistency of the various track re- – 28 – Cut Level Data rate Atm. νµ rate E−1 Eff. E−2 Eff. E−2.7 Eff. (mHz) (mHz) (%) (%) (%) Northern Hemisphere Simple Cuts 2.0 1.9 79 69 54 BDT E−2.0 1.9 1.7 86 81 72 Southern Hemisphere BDT E−2.0 2.1 0.06 78 45 35 Table 5. Data and atmospheric neutrino muon for different cut progression in IceCube. The signal efficiency for an E−2 neutrino spectrum and for well reconstructed events with∆Ψ < 5◦ with respect to theOnline Level 2 Filteris also shown. constructions and the number of strings with signals are used. The BDT training was done with simulated signal events for a soft neutrino spectrum ofE−2.7 and for anE−2 spectrum. As an exam- ple, Figure14 depicts results of the BDT training for anE−2 spectrum. A set of real data provided the background sample for training. Additionally, for the simulated signal, the reconstructed track was required to be within of 5◦ of the simulated direction in order to train the BDTs with only well- reconstructed events. The final selection cut on the BDT output variable was optimized to provide the best discovery potential for anE−2 neutrino flux, which results in a BDT score value of 0.106. This final cut leads to a rate of 2 mHz for the final sample and, asshown in Table5, to a better signal efficiency (with respect toOnline Level 2 Filterefficiency) than the original NToO cuts. The BDT-based event selection leads to an improvement in the signal efficiency of about+12(+18)% for anE−2 (E−2.7) spectrum with respect to the simple cuts. The BDT selection was also used for the Southern Hemisphere (zenith angleθ < 90◦). How- ever, instead of a single BDT-score cut value, a zenith-dependent cut was applied in order to select a constant number of events per solid angle, as shown in Figure 14 (Lower panel). This zenith- dependent cut was also optimized with respect to sensitivity and discovery potential for anE−2 neutrino spectrum. The optimized cut described by a polynomial fit (Figure 14 (Lower panel)) leads to a total data rate of 2.1 mHz for the South only and an average signal efficiency of about 45 % (with respect to theOnline Level 2 Filter), see also Table5. The first step in establishing the NToO program was to demonstrate its technical feasibility and to prove that a time-dependent point-source search can be run stably and reliably over long periods of time at the South Pole. Therefore a simple search technique like the binned method was implemented first. However, current offline IceCube searches for neutrino point sources usually employ unbinned maximum-likelihood methods [80] to increase the discovery potential. Such an approach has now also been implemented for the NToO10, which allows the alert significance to be calculated by taking into account an event-by-event angular reconstruction uncertainty estimation and an energy estimation of the event. Upgrading the NToO with a BDT-based event selection and a subsequent likelihood analysis leads to an increased sensitivity in the Northern Hemisphere of 30-40%, yielding a comparable sensitivity to the standard offline point-source analysis [81]. It also opens up the possibility of 10 At the moment the unbinned maximum likelihood is implemented as a standalone cpython module, which will be be included in the next forthcoming upgrade of the NToO system. – 29 – observing neutrino flares in the Southern Hemisphere and to forward these alerts to the H.E.S.S. collaboration [10], with whom a memorandum of understanding has been established. In previous years of operation of the NToO systems, neutrinocandidate event selections, mul- tiplet selection and alert generation all took place withinthe data-acquisition system at the South Pole. This system was found to be somewhat inflexible and difficult to expand. To address these shortcomings, the NToO systems are currently transitioning to a new approach. Instead of selecting the neutrino candidates at the South Pole, a BDT-selected stream of single high-quality neutrino events is transmitted to the North via a rapid satellite communication channel. Follow-up processes in the North now evaluate the neutrino candidates, and generate alerts for external observatories; see [81] for a more detailed description. Until December 2015, alerts were sent to the partner ex- periments privately. However, in the future we plan to distribute alerts to the full multi-messenger astrophysics community (ANTARES, KM3NeT, Auger, H.E.S.S./CTA, LIGO/VIRGO, etc.) via the Astrophysical Multimessenger Observatory Network (AMON) [82]. 11 Summary and Conclusions In this work we described a NToO program, which uses IceCube to monitor a list of predefined source candidates for neutrino flares. An important goal of this program was to establish and to test procedures to trigger promptly the gamma-ray community to collect sensitive VHE data from specific sources during periods of time when IceCube measures a potential increase in their neutrino flux. These periods of elevated emission (“flares”), both in gamma rays and neutrinos, are of particular interest to identify the sources of astrophysical neutrinos, and to understand the source emission mechanisms. The second goal of the NToO is toincrease the discovery potential for time-variable point-sources of neutrinos with IceCube. The detection of a high-energy gamma- ray flare with an IACT triggered by an alert from IceCube can help to establish the neutrino signal, even if it is not significant enough on its own to qualify as a discovery. The NToO is the first online analysis in IceCube searching for neutrino flares from point sources on time scales longer than a few minutes. We have shown that such an analysis can be done efficiently and reliably, and presented the first results from this program. Acknowledgments We acknowledge the support from the following agencies: U.S. National Science Foundation- Office of Polar Programs, U.S. National Science Foundation-Physics Division, University of Wis- consin Alumni Research Foundation, the Grid Laboratory Of Wisconsin (GLOW) grid infrastruc- ture at the University of Wisconsin - Madison, the Open Science Grid (OSG) grid infrastruc- ture; U.S. Department of Energy, and National Energy Research Scientific Computing Center, the Louisiana Optical Network Initiative (LONI) grid computing resources; Natural Sciences and Engineering Research Council of Canada, WestGrid and Compute/Calcul Canada; Swedish Re- search Council, Swedish Polar Research Secretariat, Swedish National Infrastructure for Com- puting (SNIC), and Knut and Alice Wallenberg Foundation, Sweden; German Ministry for Educa- tion and Research (BMBF), Deutsche Forschungsgemeinschaft (DFG), Helmholtz Alliance for As- troparticle Physics (HAP), Research Department of Plasmaswith Complex Interactions (Bochum), – 30 – Germany; Fund for Scientific Research (FNRS-FWO), FWO Odysseus programme, Flanders Insti- tute to encourage scientific and technological research in industry (IWT), Belgian Federal Science Policy Office (Belspo); University of Oxford, United Kingdom; MarsdenFund, New Zealand; Australian Research Council; Japan Society for Promotion of Science (JSPS); the Swiss National Science Foundation (SNSF), Switzerland; National Research Foundation of Korea (NRF); Villum Fonden, Danish National Research Foundation (DNRF), Denmark. The VERITAS Collaboration acknowledges the support of the U.S. Department of Energy Of- fice of Science, the U.S. National Science Foundation and theSmithsonian Institution, and NSERC in Canada. We also acknowledge the excellent work of the technical support staff at the Fred Lawrence Whipple Observatory and at the collaborating institutions in the construction and opera- tion of the instrument. The MAGIC collaboration would like to thank the Instituto deAstrofísica de Canarias for the excellent working conditions at the Observatorio del Roque de los Muchachos in La Palma. The financial support of the German BMBF and MPG, the Italian INFN and INAF, the Swiss Na- tional Fund SNF, the he ERDF under the Spanish MINECO (FPA2015-69818-P, FPA2012-36668, FPA2015-68278-P, FPA2015-69210-C6-2-R, FPA2015-69210-C6-4-R, FPA2015-69210-C6-6-R, AYA2013-47447-C3-1-P, AYA2015-71042-P, ESP2015-71662-C2-2-P, CSD2009-00064), and the Japanese JSPS and MEXT is gratefully acknowledged. 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(IceCube Collaboration),The IceCube Neutrino Observatory - Contributions to ICRC 2015 Part I: Point Source Searches, Proc. 34th International Cosmic Ray Conference, The Hague , 2015 [arXiv:1510.05222 p.45]. [82] M.W.E. Smith et al.,The Astrophysical Multimessenger Observatory Network (AMON), Astroparticle Physics45 (2013) 56 [arXiv:1211.5602]. 12 Appendix List of sources used by NToO for IC-2012 season (Table6) and for IC-2013 and IC-2014 season (Table7). In the table the source name, the declination (DEC), the right ascension (RA), search bin – 35 – radius, and threshold for sending alerts is listed. The lastcolumn indicates if the source belongs only to the MAGIC list or VERITAS list or if the source is present in the list for both experiments (BOTH). L.P. Source DEC RA Search radius Threshold Exper. (deg) (deg) (deg) (σ) 1 PMN J0948+ 0022 0.3740 147.2390 1.21 3.63 VERITAS 2 BL 0414+ 009 1.0900 64.2187 1.23 3.16 BOTH 3 PKS B0906+ 015 1.3600 137.2920 1.23 3.63 VERITAS 4 RGB J0152+ 017 1.7779 28.1396 1.24 3.63 VERITAS 5 3C 273 2.0525 187.2779 1.25 3.16 BOTH 6 BL 0323+ 022 2.4208 51.5583 1.26 3.16 MAGIC 7 MG1 J050533+ 0415 4.2650 76.3950 1.30 3.63 VERITAS 8 J123939+ 044409 4.7000 189.9000 1.31 3.63 VERITAS 9 HESS J0632+ 057 5.8056 98.2429 1.34 3.63 VERITAS 10 1 ES 1212+ 078 7.5347 183.7958 1.38 3.16 MAGIC 11 4C+ 09.57 9.6300 267.8900 1.43 3.63 VERITAS 12 PKS 0754+ 100 9.9400 119.3100 1.44 3.63 VERITAS 13 PKS 1502+ 106 10.4940 226.1040 1.46 3.63 VERITAS 14 CGRaBS J0211+ 1051 10.8600 32.8050 1.46 3.63 VERITAS 15 PKS 2032+ 107 11.0000 308.8600 1.47 3.63 VERITAS 16 PG 1553+ 113 11.1900 238.9292 1.47 3.16 BOTH 17 RGB 0847+ 115 11.56389 131.8038 1.48 3.16 MAGIC 18 CTA 102 11.7310 338.1520 1.48 3.63 VERITAS 19 BL 1722+ 119 11.8708 261.2679 1.49 3.16 MAGIC 20 1ES 1440+ 122 12.0111 220.7010 1.49 3.63 VERITAS 21 M87 12.3975 187.6970 1.50 3.63 VERITAS 22 PKS 0528+ 134 13.5320 82.7350 1.53 3.63 VERITAS 23 4C 14.23 14.4200 111.3200 1.55 3.63 VERITAS 24 RGB 0648+ 151 15.2736 102.1983 1.57 3.16 BOTH 25 4c15.54 15.8594 241.7775 1.58 3.16 MAGIC 26 3C 454.3 16.1480 343.4910 1.59 3.63 VERITAS 27 AO 0235+ 164 16.6164 39.6621 1.60 3.16 BOTH 28 RGB 0250+ 172 17.2025 42.6579 1.61 3.16 MAGIC 29 PKS 0735+ 178 17.7053 114.5308 1.62 3.16 MAGIC 30 OX 169 17.7300 325.8980 1.63 3.63 VERITAS 31 PKS 1717+ 177 17.7517 259.8042 1.63 3.16 BOTH 32 HB89 0317+ 185 18.7594 49.9658 1.65 3.16 BOTH 33 MG2 J071354+ 1934 19.5830 108.4820 1.67 3.63 VERITAS 34 1ES 1741+ 196 19.5858 265.9908 1.67 3.16 BOTH 35 OJ 287 20.1108 133.7033 1.68 3.16 BOTH 36 RGB 1117+ 202 20.2356 169.2758 1.68 3.16 MAGIC 37 1ES 0229+ 200 20.2881 38.2025 1.68 3.16 BOTH – 36 – 38 RGB 0521+ 211 21.2142 80.4412 1.71 3.16 BOTH 39 PKS1222+ 21 21.3794 186.2270 1.71 3.16 BOTH 40 Crab Pulsar 22.0140 83.6330 1.73 3.63 VERITAS 41 RGB 0909+ 231 23.1867 137.2529 1.75 3.16 MAGIC 42 RGB 0321+ 236 23.6031 50.5000 1.76 3.16 MAGIC 43 PG 1424+ 240 23.8000 216.7517 1.77 3.16 BOTH 44 1ES 1255+ 244 24.2111 194.3829 1.77 3.16 MAGIC 45 0827+ 243 24.2200 127.4900 1.77 3.63 VERITAS 46 1ES 0647+ 250 25.0500 102.6938 1.79 3.16 MAGIC 47 RGB 1417+ 257 25.7236 214.4858 1.80 3.16 MAGIC 48 W Comae 28.2331 185.3821 1.86 3.16 BOTH 49 Ton 599 29.2460 179.8830 1.88 3.63 VERITAS 50 HB89 0912+ 293 29.5567 138.9683 1.89 3.16 MAGIC 51 ON 325 30.1169 184.4671 1.90 3.16 BOTH 52 1ES 1218+ 304 30.1769 185.3413 1.90 3.16 BOTH 53 B2 1520+ 31 31.7370 230.5420 1.94 3.63 VERITAS 54 4C 31.03 32.1380 18.2100 1.95 3.63 VERITAS 55 CGRaBS J1848+ 3219 32.3170 282.0920 1.95 3.63 VERITAS 56 B2 0619+ 33 33.4360 95.7180 1.97 3.63 VERITAS 57 HB89 1721+ 343 34.2994 260.8367 1.99 3.16 MAGIC 58 1ES 0120+ 340 34.3472 20.7867 1.99 3.16 MAGIC 59 B2 2308+ 34 34.4200 347.7720 1.99 3.63 VERITAS 60 RGB 0706+ 377 37.7433 106.6321 2.06 3.16 MAGIC 61 NVSS 232914+ 3754 37.9042 352.309167 2.06 3.16 MAGIC 62 1633+ 382 38.1350 248.8150 2.06 3.63 VERITAS 63 Mkn 421 38.2089 166.1138 2.07 3.16 BOTH 64 B3 2247+ 381 38.4103 342.5238 2.07 3.16 BOTH 65 RGB 0136+ 391 39.1000 24.1363 2.08 3.16 MAGIC 66 0FGL J1641.4+ 3939 39.6660 250.3550 2.09 3.63 VERITAS 67 Mkn 501 39.7603 253.4675 2.10 3.16 BOTH 68 IC 310 41.3247 49.1792 2.12 3.63 VERITAS 69 TeV J2032+ 4130 41.5100 308.0830 2.13 3.63 VERITAS 70 NGC1275 41.5117 49.9504 2.13 3.16 BOTH 71 1ES 2321+ 419 42.1831 350.9671 2.14 3.16 BOTH 72 BL Lac 42.2778 330.6804 2.14 3.16 BOTH 73 B3 0814+ 425 42.3800 124.5500 2.14 3.63 VERITAS 74 1ES 1426+ 428 42.6725 217.1358 2.15 3.16 BOTH 75 3C66A 43.0356 35.6650 2.16 3.16 BOTH 76 B3 1307+ 433 43.0847 197.3563 2.16 3.16 MAGIC 77 B3 1708+ 433 43.3120 257.4210 2.16 3.63 VERITAS 78 MG4J200112+ 4352 43.8814 300.3038 2.17 3.16 BOTH 79 B3 1343+ 451 44.8830 206.3880 2.19 3.63 VERITAS 80 GB6 B1310+ 4844 48.4750 198.1810 2.25 3.63 VERITAS – 37 – 81 1ES 1011+ 496 49.4336 153.7675 2.26 3.16 BOTH 82 1150+ 497 49.5190 178.3520 2.27 3.63 VERITAS 83 1ES 0927+ 500 49.8406 142.6567 2.27 3.16 MAGIC 84 BL 1ZW187 50.2194 262.0775 2.28 3.16 MAGIC 85 1ES 1028+ 511 50.8933 157.8271 2.28 3.16 MAGIC 86 1ES 2344+ 514 51.7050 356.7700 2.30 3.16 BOTH 87 1ES 0806+ 524 52.3000 122.4542 2.31 3.16 BOTH 88 BZU J0742+ 5444 54.7400 115.6660 2.34 3.63 VERITAS 89 4C55.17 55.3828 149.4092 2.35 3.16 MAGIC 90 RGB 1903+ 556 55.6772 285.7983 2.36 3.16 MAGIC 91 RGB 1058+ 564 56.4697 164.6570 2.37 3.16 BOTH 92 RBS 1409 56.6569 219.2404 2.37 3.16 MAGIC 93 PG 1246+ 586 58.3414 192.0783 2.39 3.16 MAGIC 94 exo 0706+ 5913 59.1389 107.6250 2.40 3.16 BOTH 95 1ES 0033+ 595 59.8347 8.9692 2.41 3.16 MAGIC 96 S4 1030+ 61 60.8520 158.4640 2.42 3.63 VERITAS 97 RGB 0505+ 612 61.2267 76.4950 2.43 3.16 MAGIC 98 LSI + 61 303 61.2290 40.1310 2.43 3.63 VERITAS 99 1ES 1959+ 650 65.1486 299.9992 2.47 3.16 BOTH 100 S4 0954+ 658 65.5653 149.6967 2.48 3.16 MAGIC 101 CGRaBS J1849+ 6705 67.0950 282.3170 2.49 3.63 VERITAS 102 RGB 1136+ 676 67.6178 174.1254 2.49 3.16 MAGIC 103 1ES 0502+ 675 67.6233 76.9842 2.49 3.16 BOTH 104 GB6 J1700+ 6830 68.5020 255.0390 2.50 3.63 VERITAS 105 HB89 1749+ 701 70.09750 267.1367 2.52 3.16 BOTH 106 Mkn 180 70.1575 174.1100 2.52 3.16 BOTH 107 S5 0836+ 71 70.8950 130.3520 2.52 3.63 VERITAS 108 S5 0716+ 714 71.3433 110.4725 2.53 3.16 BOTH 109 S5 1803+ 78 78.4680 270.1900 2.57 3.63 VERITAS Table 6: List of sources used by NToO from November 2013 to December 2015. In the table the source name, the declination (DEC), the right ascension (RA), search bin radius, and threshold for sending alerts is listed. The last column indicates if the source belongs only to the MAGIC list or VERITAS list or if the source is present in the list for both experiments (BOTH). L.P. Source DEC RA Search radius Threshold Exper. (deg) (deg) (deg) (σ) 1 PG 1553+113 11.1902 238.9418 1.47 3.16 BOTH 2 PKS 1424+240 23.9750 216.7597 1.77 3.16 BOTH 3 PKS 1717+177 17.7425 259.8300 1.63 3.16 BOTH – 38 – 4 RBS 0413 18.8266 49.9094 1.65 3.16 BOTH 5 RBS 0958 20.2269 169.3050 1.68 3.16 MAGIC 6 RX J0805.4+7534 75.5878 121.3421 2.56 3.16 MAGIC 7 S5 0716+71 71.3496 110.4757 2.53 3.16 BOTH 8 TXS 1055+567 56.48010 164.6656 2.37 3.16 BOTH 9 W Comae 28.2391 185.3740 1.86 3.16 BOTH 10 1ES 1215+303 30.1093 184.4672 1.90 3.16 BOTH 11 1ES 1959+650 65.1572 300.0204 2.47 3.16 BOTH 12 1ES 2321+419 42.2001 350.9539 2.14 3.16 BOTH 13 3C 66A 43.0358 35.6617 2.16 3.16 BOTH 14 GB6 J1542+6129 61.4887 235.7294 2.43 3.16 MAGIC 15 GB6 J1838+4802 47.9939 279.6958 2.24 3.16 MAGIC 16 MS 1458.8+2249 22.6388 225.2749 1.74 3.16 MAGIC 17 Mkn 421 38.2134 166.1199 2.07 3.16 BOTH 18 Mkn 501 39.7631 253.4814 2.10 3.16 BOTH 19 PMN J0948+0022 0.3740 147.2390 1.21 3.63 VERITAS 20 BL 0414+009 1.0900 64.2188 1.23 3.63 VERITAS 21 PKS B0906+015 1.3600 137.2920 1.23 3.63 VERITAS 22 RGB J0152+017 1.7779 28.1396 1.24 3.63 VERITAS 23 3C 273 2.0525 187.2779 1.25 3.63 VERITAS 24 MG1 J050533+0415 4.2650 76.3950 1.30 3.63 VERITAS 25 J123939+044409 4.7000 189.9000 1.31 3.63 VERITAS 26 HESS J0632+057 5.8056 98.2429 1.34 3.63 VERITAS 27 4C+09.57 9.6300 267.8900 1.43 3.63 VERITAS 28 PKS 0754+100 9.9400 119.3100 1.44 3.63 VERITAS 29 PKS 1502+106 10.4940 226.1040 1.45 3.63 VERITAS 30 CGRaBS J0211+1051 10.8600 32.8050 1.47 3.63 VERITAS 31 PKS 2032+107 11.0000 308.8600 1.47 3.63 VERITAS 32 CTA 102 11.7310 338.1520 1.48 3.63 VERITAS 33 1ES 1440+122 12.0111 220.7010 1.49 3.63 VERITAS 34 M 87 12.3975 187.6970 1.50 3.63 VERITAS 35 PKS 0528+134 13.5320 82.7350 1.53 3.63 VERITAS 36 4C 14.23 14.4200 111.3200 1.55 3.63 VERITAS 37 RGB 0648+151 15.2736 102.1983 1.57 3.63 VERITAS 38 3C 454.3 16.1480 343.4910 1.59 3.63 VERITAS 39 AO 0235+164 16.616 39.6621 1.60 3.63 VERITAS 40 OX 169 17.7300 325.8980 1.63 3.63 VERITAS 41 MG2 J071354+1934 19.5830 108.4820 1.67 3.63 VERITAS 42 1ES 1741+196 19.5858 265.9908 1.67 3.63 VERITAS 43 OJ 287 20.1108 133.7033 1.68 3.63 VERITAS 44 1ES 0229+200 20.2881 38.2025 1.69 3.63 VERITAS 45 RGB 0521+211 21.2142 80.4413 1.71 3.63 VERITAS 46 PKS1222+21 21.3794 186.2271 1.71 3.63 VERITAS – 39 – 47 Crab Pulsar 22.0140 83.6330 1.72 3.63 VERITAS 48 0827+243 24.2200 127.4900 1.77 3.63 VERITAS 49 Ton 599 29.2460 179.8830 1.88 3.63 VERITAS 50 1ES 1218+304 30.1769 185.3413 1.90 3.63 VERITAS 51 B2 1520+31 31.7370 230.5420 1.94 3.63 VERITAS 52 4C 31.03 32.1380 18.2100 1.94 3.63 VERITAS 53 CGRaBS J1848+3219 32.3170 282.0920 1.95 3.63 VERITAS 54 B2 0619+33 33.4360 95.7180 1.97 3.63 VERITAS 55 B2 2308+34 34.4200 347.7720 1.99 3.63 VERITAS 56 1633+382 38.1350 248.8150 2.06 3.63 VERITAS 57 B3 2247+381 38.4103 342.5238 2.07 3.63 VERITAS 58 0FGL J1641.4+3939 39.6660 250.3550 2.09 3.63 VERITAS 59 IC 310 41.3247 49.1792 2.12 3.63 VERITAS 60 TeV J2032+4130 41.5100 308.0830 2.13 3.63 VERITAS 61 NGC1275 41.5117 49.9504 2.13 3.63 VERITAS 62 BLLac 42.2778 330.6804 2.14 3.63 VERITAS 63 B3 0814+425 42.3800 124.5500 2.14 3.63 VERITAS 64 1ES 1426+428 42.6725 217.1358 2.15 3.63 VERITAS 65 B3 1708+433 43.3120 257.4210 2.16 3.63 VERITAS 66 MG4J200112+4352 43.8814 300.3038 2.17 3.63 VERITAS 67 B3 1343+451 44.8830 206.3880 2.19 3.63 VERITAS 68 GB6 B1310+4844 48.4750 198.1810 2.25 3.63 VERITAS 69 1ES 1011+496 49.4336 153.7675 2.26 3.63 VERITAS 70 1150+497 49.5190 178.3520 2.26 3.63 VERITAS 71 1ES 2344+514 51.7050 356.7700 2.30 3.63 VERITAS 72 1ES 0806+524 52.3000 122.4542 2.31 3.63 VERITAS 73 BZU J0742+5444 54.7400 115.6660 2.34 3.63 VERITAS 74 exo 0706+5913 59.1389 107.6250 2.40 3.63 VERITAS 75 S4 1030+61 60.8520 158.4640 2.42 3.63 VERITAS 76 LSI +61 303 61.2290 40.1310 2.43 3.63 VERITAS 77 CGRaBS J1849+6705 67.0950 282.3170 2.49 3.63 VERITAS 78 1ES 0502+675 67.6233 76.9842 2.49 3.63 VERITAS 79 GB6 J1700+6830 68.5020 255.0390 2.50 3.63 VERITAS 80 HB89 1749+701 70.0975 267.1367 2.52 3.63 VERITAS 81 Mkn 180 70.1575 174.1100 2.52 3.63 VERITAS 82 S5 0836+71 70.8950 130.3520 2.52 3.63 VERITAS 83 S5 1803+78 78.4680 270.1900 2.57 3.63 VERITAS Table 7: List of sources used by NToO for IC-2013 and IC-2014 season. – 40 – 1 Introduction 2 Selection of target sources 3 The IceCube detector and IACT partners 4 Neutrino event selection 4.1 Muon Filter 4.2 Online Level 2 Filter 4.3 NToO selection variables 4.4 NToO cut optimization 4.5 Properties of the neutrino sample 5 The time-clustering algorithm 5.1 Alert rate, detection probability 6 Data stability monitoring 6.1 Rate measurements and data quality assessment 6.2 Stability-score calculation 6.3 Implementation of the stability-score calculation 7 Technical design of the alert system 8 Monitoring of alert system 9 Results of NToO program 10 Recent and upcoming improvements 11 Summary and Conclusions 12 Appendix