Publications (9)
Safety of Quark/Gluon Jet Classification
Alexis Romero, Daniel Whiteson, Michael Fenton +2
The classification of jets as quark- versus gluon-initiated is an important yet challenging task in the analysis of data from high-energy particle collisions and in the search for…
How to GAN Higher Jet Resolution
Pierre Baldi, Lukas Blecher, Anja Butter +6
QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the res…
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Lars Hertel, Julian Collado, Peter Sadowski +2
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations…
Learning to Identify Electrons
Julian Collado, Jessica N. Howard, Taylor Faucett +3
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable informa…
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System
Julian Collado, Kevin Stangl
Recent approaches in machine learning often solve a task using a composition of multiple models or agentic architectures. When targeting a composed system with adversarial attacks,…
SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics
Yadong Lu, Julian Collado, Daniel Whiteson +1
Generation of simulated data is essential for data analysis in particle physics, but current Monte Carlo methods are very computationally expensive. Deep-learning-based generative…