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oai:docta.ucm.es:20.500.14352/885292025-08-28T17:39:23Zcom_20.500.14352_14col_20.500.14352_20

Brill, A. Feng, Q. Humensky, T. B. Kim, B. Nieto Castaño, Daniel Miener, Tjark 2023-11-02T09:55:11Z 2023-11-02T09:55:11Z 2019 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. 978-1-7281-5235-6 10.1109/nysds.2019.8909697 https://hdl.handle.net/20.500.14352/88529 https://doi.org/10.1109/nysds.2019.8909697 https://ieeexplore.ieee.org/document/8909697 https://arxiv.org/pdf/2001.03602 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. eng restricted access Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes conference paper