RODEM Jet Datasets

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Zoch, Knut, Raine, John Andrew, Sengupta, Debajyoti, Golling, Tobias
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866917754874888192
author Zoch, Knut
Raine, John Andrew
Sengupta, Debajyoti
Golling, Tobias
author_facet Zoch, Knut
Raine, John Andrew
Sengupta, Debajyoti
Golling, Tobias
contents We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.
format Preprint
id arxiv_https___arxiv_org_abs_2408_11616
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle RODEM Jet Datasets
Zoch, Knut
Raine, John Andrew
Sengupta, Debajyoti
Golling, Tobias
High Energy Physics - Phenomenology
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.
title RODEM Jet Datasets
topic High Energy Physics - Phenomenology
url https://arxiv.org/abs/2408.11616