Speaker
Description
The past decade has witnessed a rapid expansion of machine learning applications in very-high-energy (VHE) gamma-ray astronomy. While many efforts have focused on Convolutional Neural Networks (CNNs), these approaches remain constrained by existing camera geometries and by the limited ability of Monte Carlo simulations to fully capture real telescope performance. In this work, we employ a simulated telescope optimization framework to investigate how advanced machine learning techniques can fundamentally reshape instrumental design requirements.
We present, to our knowledge, the first transformer-based analysis applied to data from Cherenkov Telescopes. Our analysis achieves an order-of-magnitude improvement in performance compared to a typical standard analysis pipeline. Our results indicate that, within this framework, a single 5-meter-diameter Imaging Atmospheric Cherenkov Telescope (IACT) operating in monoscopic mode can reach an energy threshold below 150GeV, three times lower than the one achieved with standard methods. These findings point toward a viable pathway for the development of cost-effective, potentially autonomous IACTs, capable of substantially increasing the temporal coverage and duty cycle of future gamma-ray observatories.