6 citations · 9 across the 2 of their papers we have counts for
6 papers
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…
Learning to Isolate Muons
Julian Collado, Kevin Bauer, Edmund Witkowski +3
Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider ph…
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…
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…