31 citations · 55 across the 7 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
DeepTreeGAN: Fast Generation of High Dimensional Point Clouds
Moritz Alfons Wilhelm Scham, Dirk Krücker, Benno Käch +1
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generati…
Search for stealth supersymmetry in final states with two photons, jets, and low missing transverse momentum in proton-proton collisions at = 13 TeV
CMS Collaboration
The results of a search for stealth supersymmetry in final states with two photons and jets, targeting a phase space region with low missing transverse momentum ($p_\text{T}^\text{…
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows
Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann +3
Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally…
Point Cloud Generation using Transformer Encoders and Normalising Flows
Benno Käch, Dirk Krücker, Isabell Melzer-Pellmann
Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally ch…
Impact of jet-production data on the next-to-next-to-leading-order determination of HERAPDF2.0 parton distributions
H1, ZEUS Collaborations, : +234
The HERAPDF2.0 ensemble of parton distribution functions (PDFs) was introduced in 2015. The final stage is presented, a next-to-next-to-leading-order (NNLO) analysis of the HERA da…
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
Adam Elwood, Dirk Krücker
We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify eve…
New Particles Working Group Report of the Snowmass 2013 Community Summer Study
Y. Gershtein, M. Luty, M. Narain +92
This report summarizes the work of the Energy Frontier New Physics working group of the 2013 Community Summer Study (Snowmass).