How to GAN Higher Jet Resolution

no code implementations • 22 Dec 2020 • Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson

QCD-jets at the LHC are described by simple physics principles.

Super-Resolution High Energy Physics - Phenomenology

Deep-Learning-Based Kinematic Reconstruction for DUNE

no code implementations • 11 Dec 2020 • Junze Liu, Jordan Ott, Julian Collado, Benjamin Jargowsky, Wenjie Wu, Jianming Bian, Pierre Baldi

To precisely reconstruct these kinematic characteristics of detected interactions at DUNE, we have developed and will present two CNN-based methods, 2-D and 3-D, for the reconstruction of final state particle direction and energy, as well as neutrino energy.

Learning to Identify Electrons

1 code implementation • 3 Nov 2020 • Julian Collado, Jessica N. Howard, Taylor Faucett, Tony Tong, Pierre Baldi, Daniel Whiteson

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information.

Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology

Sherpa: Robust Hyperparameter Optimization for Machine Learning

1 code implementation • 8 May 2020 • Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, Pierre Baldi

Sherpa is a hyperparameter optimization library for machine learning models.

BIG-bench Machine Learning Hyperparameter Optimization