Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks

In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of the latest techniques in machine learning, namely recurrent and convolutional neural networks, to the background rejection problem. Use of these machine-learning techniques addresses some of the key challenges encountered in the currently implemented algorithms and helps to significantly increase the background rejection performance at all energies. We apply these machine learning techniques to the H.E.S.S. telescope array, first testing their performance on simulated data and then applying the analysis to two well known gamma-ray sources. With real observational data we find significantly improved performance over the current standard methods, with a 20-25\% reduction in the background rate when applying the recurrent neural network analysis. Importantly, we also find that the convolutional neural network results are strongly dependent on the sky brightness in the source region which has important implications for the future implementation of this method in Cherenkov telescope analysis.Comment: 11 pages, 7 figures. To be submitted to The European Physical Journal

Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes

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.Comment: 4 pages, 4 figures, Proceedings of the 2019 New York Scientific Data Summit (NYSDS

Signal-background separation and energy reconstruction of gamma rays using pattern spectra and convolutional neural networks for the Small-Sized Telescopes of the Cherenkov Telescope Array

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very high-energy gamma rays from ground level by capturing the Cherenkov light of the induced particle showers. Convolutional neural networks (CNNs) can be trained on IACT camera images of such events to differentiate the signal from the background and to reconstruct the energy of the initial gamma ray. Pattern spectra provide a 2-dimensional histogram of the sizes and shapes of features comprising an image and they can be used as an input for a CNN to significantly reduce the computational power required to train it. In this work, we generate pattern spectra from simulated gamma-ray and proton images to train a CNN for signal-background separation and energy reconstruction for the Small-Sized Telescopes (SSTs) of the Cherenkov Telescope Array (CTA). A comparison of our results with a CNN directly trained on CTA images shows that the pattern spectra-based analysis is about a factor of three less computationally expensive but not able to compete with the performance of the CTA images-based analysis. Thus, we conclude that the CTA images must be comprised of additional information not represented by the pattern spectra.Comment: 10 pages, 9 figures, submitted to Nuclear Instruments and Methods in Physics Research - section

Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks

Extensive air showers created by high-energy particles interacting with the Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes (IACTs). The IACT images can be analyzed to distinguish between the events caused by gamma rays and by hadrons and to infer the parameters of the event such as the energy of the primary particle. We use convolutional neural networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of the TAIGA experiment. The analysis includes selection of the images corresponding to the showers caused by gamma rays and estimating the energy of the gamma rays. We compare performance of the CNNs using images from a single telescope and the CNNs using images from two telescopes as inputs.Comment: In Proceedings of 5th International Workshop on Deep Learning in Computational Physics (DLCP2021), 28-29 June, 2021, Moscow, Russi

Convolutional Neural Network Single-Telescope Reconstruction for the Large Size Telescope of CTA

In this thesis I propose a full CNN Single-Telescope Reconstruction analysis chain, for the LST of CTA. I compare both simple and state-of-the-art architectures for gamma/hadron separation, energy and direction reconstruction, showing that our analysis chain significantly outperforms the RF algorithm in all three tasks

Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 - 10 x better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance

Analysis Methods for Gamma-ray Astronomy

The launch of the Fermi satellite in 2008, with its Large Area Telescope (LAT) on board, has opened a new era for the study of gamma-ray sources at GeV ($10^9$ eV) energies. Similarly, the commissioning of the third generation of imaging atmospheric Cherenkov telescopes (IACTs) - H.E.S.S., MAGIC, and VERITAS - in the mid-2000's has firmly established the field of TeV ($10^{12}$ eV) gamma-ray astronomy. Together, these instruments have revolutionised our understanding of the high-energy gamma-ray sky, and they continue to provide access to it over more than six decades in energy. In recent years, the ground-level particle detector arrays HAWC, Tibet, and LHAASO have opened a new window to gamma rays of the highest energies, beyond 100 TeV. Soon, next-generation facilities such as CTA and SWGO will provide even better sensitivity, thus promising a bright future for the field. In this chapter, we provide a brief overview of methods commonly employed for the analysis of gamma-ray data, focusing on those used for Fermi-LAT and IACT observations. We describe the standard data formats, explain event reconstruction and selection algorithms, and cover in detail high-level analysis approaches for imaging and extraction of spectra, including aperture photometry as well as advanced likelihood techniques.Comment: 56 pages, 12 figures. Invited chapter for "Handbook of X-ray and Gamma-ray Astrophysics" (Eds. C. Bambi and A. Santangelo, Springer Singapore, expected in 2023