Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA

International audienceWhen very-high-energy gamma rays interact high in the Earth's atmosphere, they produce cascades of particles that induce flashes of Cherenkov light. Imaging Atmospheric Cherenkov Telescopes (IACTs) detect these flashes and convert them into shower images that can be analyzed to extract the properties of the primary gamma ray. The dominant background for IACTs is comprised of air shower images produced by cosmic hadrons, with typical noise-to-signal ratios of several orders of magnitude. The standard technique adopted to differentiate between images initiated by gamma rays and those initiated by hadrons is based on classical machine learning algorithms, such as Random Forests, that operate on a set of handcrafted parameters extracted from the images. Likewise, the inference of the energy and the arrival direction of the primary gamma ray is performed using those parameters. State-of-the-art deep learning techniques based on convolutional neural networks (CNNs) have the potential to enhance the event reconstruction performance, since they are able to autonomously extract features from raw images, exploiting the pixel-wise information washed out during the parametrization process. Here we present the results obtained by applying deep learning techniques to the reconstruction of Monte Carlo simulated events from a single, next-generation IACT, the Large-Sized Telescope (LST) of the Cherenkov Telescope Array (CTA). We use CNNs to separate the gamma-ray-induced events from hadronic events and to reconstruct the properties of the former, comparing their performance to the standard reconstruction technique. Three independent implementations of CNN-based event reconstruction models have been utilized in this work, producing consistent results

GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data

The Cherenkov Telescope Array (CTA) is the next generation of ground-based gamma-ray telescopes for gamma-ray astronomy. Two arrays will be deployed composed of 19 telescopes in the Northern hemisphere and 99 telescopes in the Southern hemisphere. Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. Moreover, the vast majority of triggered events come from protons that represent a background for gamma-ray astronomy. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data in many domains. Thanks to the huge amount of simulated data and later of real data, produced by CTA, these algorithms look well-suited and very promising. Moreover, the trained neural networks show very good computing performances during execution. Here we present a first study of deep learning architectures applied to CTA simulated data to perform the reconstruction of the particles energy and incoming direction and the development of a specific framework, GammaLearn, to accomplish this task

lstchain: An Analysis Pipeline for LST-1, the First Prototype Large-Sized Telescope of CTA

International audienceThe future Cherenkov Telescope Array (CTA) will have telescopes of different sizes, the Large-Sized Telescopes (LSTs) being the largest ones. Located on the island of La Palma, the LST-1, the prototype of the first LST, started taking astronomical data in November 2019, detecting the first gamma-ray sources right afterwards. The analysis pipeline, that processes data from raw inputs until high level products is called lstchain and is heavily based in the CTA prototype pipeline framework ctapipe. In this presentation I'll show the pipeline that performs signal integration, image cleaning, image parameter calculation, and machine learning methods for true parameter reconstruction

lstchain: An Analysis Pipeline for LST-1, the First Prototype Large-Sized Telescope of CTA

International audienceThe future Cherenkov Telescope Array (CTA) will have telescopes of different sizes, the Large-Sized Telescopes (LSTs) being the largest ones. Located on the island of La Palma, the LST-1, the prototype of the first LST, started taking astronomical data in November 2019, detecting the first gamma-ray sources right afterwards. The analysis pipeline, that processes data from raw inputs until high level products is called lstchain and is heavily based in the CTA prototype pipeline framework ctapipe. In this presentation I'll show the pipeline that performs signal integration, image cleaning, image parameter calculation, and machine learning methods for true parameter reconstruction

lstchain: An Analysis Pipeline for LST-1, the First Prototype Large-Sized Telescope of CTA

International audienceThe future Cherenkov Telescope Array (CTA) will have telescopes of different sizes, the Large-Sized Telescopes (LSTs) being the largest ones. Located on the island of La Palma, the LST-1, the prototype of the first LST, started taking astronomical data in November 2019, detecting the first gamma-ray sources right afterwards. The analysis pipeline, that processes data from raw inputs until high level products is called lstchain and is heavily based in the CTA prototype pipeline framework ctapipe. In this presentation I'll show the pipeline that performs signal integration, image cleaning, image parameter calculation, and machine learning methods for true parameter reconstruction