The Second LHAASO Collaboration Conference in 2026 & The Academic Symposium for the 10th Anniversary of the LHAASO collaboration

LHAASO-AI explores deep-learning-based approaches for event reconstruction and classification in LHAASO. We have developed models for four major tasks: gamma/hadron discrimination, arrival-direction reconstruction, shower-core reconstruction, and energy reconstruction. The models are trained and systematically evaluated using large-scale Monte Carlo simulations. Validation with experimental...

This report presents recent progress in the application of machine learning techniques to data analysis with the Kilometer Square Array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO), focusing on two key tasks: gamma–proton discrimination and air-shower direction reconstruction. We have developed deep learning models for these tasks and conducted extensive validation using...

Precisely identifying the astrophysical sources of cosmic rays remains challenging. Further improvements in the sensitivity to gamma-ray sources are therefore required. We present a Transformer-based deep learning model that uses two separate branches to extract features from heterogeneous data recorded by the electromagnetic particle detectors (EDs) and muon detectors (MDs) of LHAASO-KM2A,...

GammaBull is an LLM-driven agent platform that gives astroparticle physicists conversational, end-to-end access to the routine work of a large ground-based gamma-ray observatory. Rather than a single chatbot, it couples a reasoning orchestration core with a growing registry of domain skills, letting it chain multi-step tasks across the LHAASO data system.