![Bites of FM4S: [1] Physics-inspired representations](/event/1473554/logo-3452117835.png){kind=logo}
We introduce a novel experimental methodology, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), designed to enhance the LHC resonance search program in the Lorentz-boosted regime. Sophon leverages the principles of “large models for large-scale classification”, employing the advanced deep learning algorithm to train a classifier across an extensive (o(100)) set of...
Discovering the occurrence of unexpected physical processes in collider data could unveil new fundamental laws governing our Universe. However, the extreme size, rate and complexity of the datasets generated at the Large Hadron Collider (LHC) pose unique challenges to detect them. Typically, this is addressed by transforming high-dimensional, low-level detector data into physically meaningful...
With large amounts of data, a Higgs boson discovery, and world-leading constraints on an enormous amount of parameters and interactions, the Large Hadron Collider has been a phenomenal tool. We show new results built on contrastive learning and semi-supervised learning strategies where, through physics-motivated choices, we teach an AI to visualize many processes simultaneously, allowing it to...
The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate...
Particle jets are collimated flows of partons which evolve into tree-like structures through stochastic parton showering and hadronization. The hierarchical nature of particle jets aligns naturally with hyperbolic space, a non-Euclidean geometry that captures hierarchy intrinsically. To leverage the benefits of non-Euclidean geometries, we develop jet analysis in product manifold (PM) spaces,...
