First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.

[1]  Patrick Lambert,et al.  Indexed Operations for Non-rectangular Lattices Applied to Convolutional Neural Networks , 2019, VISIGRAPP.

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  R. D. Parsons,et al.  Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks , 2019, The European Physical Journal C.

[4]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  D. Nieto,et al.  Exploring deep learning as an event classification method for the Cherenkov Telescope Array , 2017, 1709.05889.

[6]  A. Hillas Cerenkov light images of EAS produced by primary gamma , 1985 .

[7]  Juan José Rodríguez-Vázquez,et al.  Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data , 2018, ANNPR.

[8]  R. Mukherjee,et al.  Reconstruction of IACT events using deep learning techniques with CTLearn , 2021, 2101.07626.

[9]  T. Lohse,et al.  Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data , 2018, Astroparticle Physics.

[10]  Bo Wang,et al.  SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation , 2020, MICCAI.

[11]  R. D. Parsons,et al.  HESS II Data Analysis with ImPACT , 2015, 1509.06322.

[12]  Ti-Pei Li,et al.  Analysis methods for results in gamma-ray astronomy , 1983 .

[13]  K. Bernlohr,et al.  Simulation of Imaging Atmospheric Cherenkov Telescopes with CORSIKA and sim_telarray , 2008, 0808.2253.

[14]  A.Fiasson,et al.  Optimization of multivariate analysis for IACT stereoscopic systems , 2010, 1004.3375.

[15]  J. Knapp,et al.  CORSIKA: A Monte Carlo code to simulate extensive air showers , 1998 .

[16]  Patrick Lambert,et al.  Multi-Task Architecture with Attention for Imaging Atmospheric Cherenkov Telescope Data Analysis , 2021, VISIGRAPP.

[17]  G. Deleglise,et al.  Development of the camera for the large size telescopes of the Cherenkov Telescope Array , 2014, Astronomical Telescopes and Instrumentation.

[18]  Mathieu de Naurois,et al.  A high performance likelihood reconstruction of γ-rays for imaging atmospheric Cherenkov telescopes , 2009, 0907.2610.