Deep learning techniques for Imaging Air Cherenkov Telescopes
arXiv:2206.05296 · doi:10.1103/PhysRevD.107.083026
Abstract
Very High Energy (VHE) gamma rays and charged cosmic rays (CCRs) provide an observational window into the acceleration mechanisms of extreme astrophysical environments. One of the major challenges at Imaging Air Cherenkov Telescopes (IACTs) designed to look for VHE gamma rays, is the separation of air showers initiated by CCRs which form a background to gamma ray searches. Two other less well studied problems at IACTs are a) the classification of different primary nuclei among the CCR events and b) identification of anomalous events initiated by Beyond Standard Model particles that could give rise to shower signatures which differ from the standard images of either gamma rays or CCR showers. The problems of categorizing the primary particle that initiates a shower image, or the problem of tagging anomalous shower events in a model independent way, are problems that are well suited to a machine learning (ML) approach. Traditional studies that have explored gamma ray/CCR separation have used a multivariate analysis based on derived shower properties, which contains significantly reduced information about the shower. In our work, we address the problems outlined above by using ML architectures trained on full simulated shower images, as opposed to training on just a few derived shower properties. We illustrate the techniques of binary and multi-category classification using convolutional neural networks, and we also pioneer the use of autoencoders for anomaly detection at VHE gamma ray experiments. As a case study, we apply our techniques to the H.E.S.S. experiment. However, the real strength of the techniques that we broach here in the context of VHE gamma ray observatories, is that these methods can be applied broadly to any other IACT, such as the upcoming Cherenkov Telescope Array (CTA), or can even be suitably adapted to CCR experiments.
43 pages, 16 figures, 9 tables - updated in response to referee comments, main results unchanged
References in corpus (23)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- Techniques for improved heavy particle searches with jet substructure
- Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
- Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
- The origin of galactic cosmic rays
- Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre
- The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
- Autoencoders for unsupervised anomaly detection in high energy physics
- The Hunt for Pevatrons: The Case of Supernova Remnants
- Cosmic ray production in supernovae
- A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes
- Improved /hadron separation for the detection of faint gamma-ray sources using boosted decision trees
- Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
- Very-high energy gamma-ray astronomy: A 23-year success story in high-energy astroparticle physics
- Les Houches Lectures on Indirect Detection of Dark Matter
- HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch
- Shared Data and Algorithms for Deep Learning in Fundamental Physics
- Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
- Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks
- Galactic and Extragalactic Sources of Very High Energy Gamma-rays
- Tagging Boosted Ws with Wavelets
- Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data
- Avenues to new-physics searches in cosmic ray air showers