High-energy particles from the sky, so-called cosmic rays, were first revealed by Victor Hess in 1913. However, even with its long history, we still do not clearly know the origin of cosmic rays because the magnetic field deflects those charged particles. High-energy gamma-ray observations may solve these mysterious origins because gamma rays are created when cosmic rays interact with materials near their acceleration sites. They are electrically neutral; the gamma rays produced in cosmic-ray interactions are not perturbed by the magnetic fields filling our galaxy and intergalactic space. By studying the spatial and spectral distribution of gamma rays, we can identify the locations of cosmic ray accelerators.

Related publications

• 10.3847/2041-8213/ab96cc

• 10.1126/science.aan4880

Particle sampling arrays detecting extensive air showers are often used to study Very-High-Energy (VHE; multi-TeV) gamma-ray astrophysics. To study gamma-ray spectra precisely, classifying gamma-ray-induced air showers from a massive amount of cosmic-ray-induced air showers is crucial. 

Since the people in our lab often use deep learning techniques for particle identification or reconstruction, we are trying to implement deep learning models for gamma/hadron discrimination. Moreover, we are also developing deep learning models to reconstruct physical quantities such as the incoming angle and energy of gamma rays.

People here are also working on modeling the gamma-ray sources based on statistical methodology. 

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