28 citations · 55 across the 8 of their papers we have counts for
8 papers
Strong CWoLa: Binary Classification Without Background Simulation
Samuel Klein, Matthew Leigh, Stephen Mulligan +1
Supervised deep learning methods have been successful in the field of high energy physics, and the trend within the field is to move away from high level reconstructed variables to…
Enhancing generalization in high energy physics using white-box adversarial attacks
Franck Rothen, Samuel Klein, Matthew Leigh +1
Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most…
Is Tokenization Needed for Masked Particle Modelling?
Matthew Leigh, Samuel Klein, François Charton +5
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets releva…
Accelerating template generation in resonant anomaly detection searches with optimal transport
Matthew Leigh, Debajyoti Sengupta, Benjamin Nachman +1
We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact…
PIPPIN: Generating variable length full events from partons
Guillaume Quétant, John Andrew Raine, Matthew Leigh +2
This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To ad…
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
Tobias Golling, Lukas Heinrich, Michael Kagan +4
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high e…
Improving new physics searches with diffusion models for event observables and jet constituents
Debajyoti Sengupta, Matthew Leigh, John Andrew Raine +2
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…