611 citations · 3k across the 87 of their papers we have counts for
9 papers · 1 filter
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…
Variational inference for pile-up removal at hadron colliders with diffusion models
Malte Algren, Tobias Golling, Christopher Pollard +1
In this paper, we present a novel method for pile-up removal of interactions using variational inference with diffusion models, called vipr. Instead of using classification me…
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…
RODEM Jet Datasets
Knut Zoch, John Andrew Raine, Debajyoti Sengupta +1
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in pa…
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…