31 citations · 55 across the 7 of their papers we have counts for
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Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries in Deep-Inelastic Scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +141
In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Pay Attention To Mean Fields For Point Cloud Generation
Benno Käch, Isabell Melzer-Pellmann, Dirk Krücker
Collider data generation with machine learning has become increasingly popular in particle physics due to the high computational cost of conventional Monte Carlo simulations, parti…
CaloPointFlow II Generating Calorimeter Showers as Point Clouds
Simon Schnake, Dirk Krücker, Kerstin Borras
The simulation of calorimeter showers presents a significant computational challenge, impacting the efficiency and accuracy of particle physics experiments. While generative ML mod…
Observation and differential cross section measurement of neutral current DIS events with an empty hemisphere in the Breit frame
The H1 collaboration, V. Andreev, M. Arratia +146
The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged bo…