182 citations · 248 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Deep-Learning-Based Kinematic Reconstruction for DUNE
Junze Liu, Jordan Ott, Julian Collado +4
In the framework of three-active-neutrino mixing, the charge parity phase, the neutrino mass ordering, and the octant of remain unknown. The Deep Underground Neutrino Expe…
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…
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…
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…