Since the Higgs discovery, the LHC has been gathering massive data to tackle open questions in physics, and the past decade’s machine‑learning boom is now powering everything from event tagging to generative modeling in collider analysis. In the first part of the talk, I will talk about DeeLeMa, which is a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. And the second part of my talk will be about a multi-modal network. In collider experiments, an event is characterized by two distinct yet mutually complementary features: the `global features' and the `local features'. We propose a simple but effective neural network architecture that seamlessly integrates information from both kinematics and QCD to enhance the signal sensitivity at colliders.
