79 citations · 514 across the 32 of their papers we have counts for
4 papers · 1 filter
Les Houches guide to reusable ML models in LHC analyses
Jack Y. Araz, Andy Buckley, Gregor Kasieczka +10
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis p…
Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
Jack Y. Araz, Michael Spannowsky, Matthew Wingate
This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle…
Spey: smooth inference for reinterpretation studies
Jack Y. Araz
Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilise differe…
Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks
Jack Y. Araz, Andy Buckley, Benjamin Fuks
High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their larg…