54 citations · 248 across the 33 of their papers we have counts for
3 papers · 1 filter
Unsupervised quark/gluon jet tagging with Poissonian Mixture Models
Ezequiel Alvarez, Michael Spannowsky, Manuel Szewc
The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modelling the data…
Bayesian Probabilistic Modelling for Four-Tops at the LHC
Ezequiel Alvarez, Barry M. Dillon, Darius A. Faroughy +3
Monte Carlo (MC) generators are crucial for analyzing data in particle collider experiments. However, often even a small mismatch between the MC simulations and the measurements ca…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…