Particle identification in ground-based gamma-ray astronomy using convolutional neural networks
This work addresses the problem of distinguishing gamma-rays from background particles for astronomers, but it is incremental as it applies existing deep learning methods to a specific domain.
The study tackled particle identification in ground-based gamma-ray astronomy by implementing convolutional neural networks (CNNs) to analyze images from air Cherenkov telescopes, achieving an identification accuracy estimated through Monte Carlo simulation. It tested PyTorch and TensorFlow as software platforms for this task.
Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.