2026-06-08T01:05:03Zhttps://docta.ucm.es/rest/oai/request

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

00925njm 22002777a 4500 dc Brill, A. author Feng, Q. author Humensky, T. B. author Kim, B. author Nieto Castaño, Daniel author Miener, Tjark author 2019 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. 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 Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes