RT Conference Proceedings
T1 Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes
A1 Brill, A.
A1 Feng, Q.
A1 Humensky, T. B.
A1 Kim, B.
A1 Nieto Castaño, Daniel
A1 Miener, Tjark
AB Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity, gamma-ray showers must be efficiently distinguished from the dominant background of cosmic-ray showers using images from multiple telescopes. A combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) has been proposed to perform this task. Using CTLearn, an open source Python package using deep learning to analyze data from IACTs, with simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement a CNN-RNN network and find no evidence that sorting telescope images by total amplitude improves background rejection performance.
SN 978-1-7281-5235-6
YR 2019
FD 2019
LK https://hdl.handle.net/20.500.14352/88529
UL https://hdl.handle.net/20.500.14352/88529
LA eng
NO A. Brill, Q. Feng, T. B. Humensky, B. Kim, D. Nieto, and T. Miener, in 2019 New York Scientific Data Summit (NYSDS) (IEEE, New York, NY, USA, 2019), pp. 1–4.
NO Se deposita versión preprint de la ponencia
NO National Science Foundation
NO Ministerio de Economía, Comercio y Empresa (España)
DS Docta Complutense
RD 9 abr 2025