Public Lecture | Expecting the Unexpected with AI at Particle Colliderspresented by Julia Gonski

In particle physics, we search for new elementary particles that signal extensions of the fundamental interactions of nature. Experiments at the CERN Large Hadron Collider (LHC) produce massive datasets, which physicists scour for evidence of hypothetical particles that have been suggested by theorists. But it is impossible to predict exactly what nature has in store. Can we discover something new without knowing in advance what we are looking for?
Progress in data science gives us new ways to mine the datasets from the LHC. Using artificial intelligence, we can search in a general way for events that are anomalous, signaling behavior outside of our current laws of physics. Coupled with advanced silicon microelectronics, we can apply AI to scan and sort data in real time, at the enormous rates at which the LHC collides protons. These game-changing technologies will work even more powerfully at future particle colliders, where era-defining discoveries might be around every corner.
About the Speaker:
A New Jersey native, Julia Gonski did her undergraduate studies at Rutgers University and her graduate studies at Harvard, receiving her Ph.D. in 2019. After a postdoctoral fellowship at Columbia University, Julia joined SLAC in 2023 as a Panofsky Fellow. Her research focuses on novel approaches to searching for new elementary particles in collider datasets, in particular incorporating machine learning and anomaly detection. She also works on real-time AI/ML with advanced data acquisition systems based on microelectronics. Outside of her research, Julia is involved in community organizing, outreach, and global inclusivity for the advanced particle colliders of the future.