Physics Colloquium

At the CERN Large Hadron Collider (LHC), protons collide 40 million times per second at the highest energies achievable in the lab, probing the microscopic nature of subatomic particles on the smallest length scales. Each collision gives rise to tens of thousands of particles, whose energy deposits and hits are measured by massive detectors and read out as hundreds of millions of data channels. With this data, we can test the validity of the standard model and search for the existence of new particles or interactions, including the all-important Higgs boson self-interaction. This avalanche of data will continue to grow in the next generation of experiments, posing significant challenges. Machine learning (ML) methods are already essential for analyzing this data and overcoming these challenges. In this talk, I will cover how ML approaches have advanced in recent years to reconstruct particles from multimodal detector measurements, filter collisions in real time on FPGAs, identify energetic Higgs boson decays, and enable the discovery of new interactions. Finally, I will describe emerging strategies to build "foundation models" for particle physics.

Join via Zoom:
https://caltech.zoom.us/j/89860951893
Meeting ID: 818 6692 9019

The colloquium is held in Feynman Lecture Hall, 201 E. Bridge.