Tobias Golling
Professor of Physics
Director of the Department of Particle Physics (DPNC)
University of Geneva
Introduction
My research:
Maximize science through machine learning
My testing ground: High Energy Physics (HEP)
My expertise is:
LHC: new physics, top, flavor-tagging, boosted jets
ML: surrogate models, flows, diffusion, GNNs, transformers, anomaly detection, optimal transport, foundation models, optimal design
A problem I’m grappling with:
Automating and accelerating scientific discovery
I’ve got my eyes on:
Multimodal foundation models & differential programing
I want to know more about:
AI & scientific understanding, effective & enriching interdisciplinary collaboration
Links
Automated General Searches for New Physics
In contemporary high-energy physics, the number of potential scenarios for physics beyond the Standard Model far exceeds the capacity of dedicated searches. This disparity stresses the need for model-agnostic approaches that explore a broad spectrum of possibilities within the theoretical signal landscape.
General searches with (GNN) AutoEncoders
Develop general Graph Neural Network (GNN) reconstruction algorithm(s) for all event types in experiment; based on this GNN architecture, use Variable AutoEncoder (VAE) to encode the dataset in a latent space (LS); apply the VAE to simulated data, also including theoretically motivated but unobserved physics signatures.
New Generative Models for Parton Distributions
The goal of the project is to switch from the current situation, in which a hyperoptimized machine learning model leads to results whose accuracy is tested a posteriori, to interpretable models in which the relation between experimental, theoretical and modeling uncertainty and the uncertainties in the final outcome can be traced using explainable AI tools.
