Unbinned deep learning jet substructure measurement in high Q2 ep collisions at HERA

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F23%3A00582507" target="_blank" >RIV/68378271:_____/23:00582507 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/23:10474989 RIV/61989592:15310/23:73622971

Výsledek na webu

<a href="https://hdl.handle.net/11104/0350569" target="_blank" >https://hdl.handle.net/11104/0350569</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.physletb.2023.138101" target="_blank" >10.1016/j.physletb.2023.138101</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unbinned deep learning jet substructure measurement in high Q2 ep collisions at HERA

Popis výsledku v původním jazyce

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities.

Název v anglickém jazyce

Unbinned deep learning jet substructure measurement in high Q2 ep collisions at HERA

Popis výsledku anglicky

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physics Letters. B

ISSN

0370-2693

e-ISSN

1873-2445

Svazek periodika

844

Číslo periodika v rámci svazku

Sept

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

138101

Kód UT WoS článku

001068892700001

EID výsledku v databázi Scopus

2-s2.0-85167782568