Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F23%3A00583128" target="_blank" >RIV/68378271:_____/23:00583128 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21220/23:00373798 RIV/68407700:21340/23:00373798 RIV/68407700:21670/23:00373798 RIV/00216208:11320/23:10476413 RIV/61989592:15310/23:73628928

Výsledek na webu

<a href="https://hdl.handle.net/11104/0351119" target="_blank" >https://hdl.handle.net/11104/0351119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1748-0221/18/11/P11006" target="_blank" >10.1088/1748-0221/18/11/P11006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

Popis výsledku v původním jazyce

The ATLAS experiment relies on real-time hadronic jetreconstruction and b-tagging to record fully hadronic eventscontaining b-jets. These algorithms require track reconstruction,which is computationally expensive and could overwhelm thehigh-level-trigger farm, even at the reduced event rate that passesthe ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAShas mitigated these computational demands by introducing a fastneural-network-based b-tagger, which acts as a low-precisionfilter using input from hadronic jets and tracks. It runs after ahardware trigger and before the remaining high-level-triggerreconstruction. This design relies on the negligible cost ofneural-network inference as compared to track reconstruction, andthe cost reduction from limiting tracking to specific regions of thedetector.

Název v anglickém jazyce

Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

Popis výsledku anglicky

The ATLAS experiment relies on real-time hadronic jetreconstruction and b-tagging to record fully hadronic eventscontaining b-jets. These algorithms require track reconstruction,which is computationally expensive and could overwhelm thehigh-level-trigger farm, even at the reduced event rate that passesthe ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAShas mitigated these computational demands by introducing a fastneural-network-based b-tagger, which acts as a low-precisionfilter using input from hadronic jets and tracks. It runs after ahardware trigger and before the remaining high-level-triggerreconstruction. This design relies on the negligible cost ofneural-network inference as compared to track reconstruction, andthe cost reduction from limiting tracking to specific regions of thedetector.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

<a href="/cs/project/LM2023040" target="_blank" >LM2023040: Výzkumná infrastruktura pro experimenty v CERN</a><br>

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

Journal of Instrumentation

ISSN

1748-0221

e-ISSN

1748-0221

Svazek periodika

18

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

37

Strana od-do

P11006

Kód UT WoS článku

001123791900004

EID výsledku v databázi Scopus

2-s2.0-85180406982