A neural network clustering algorithm for the ATLAS silicon pixel detector

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10286977" target="_blank" >RIV/00216208:11320/14:10286977 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68378271:_____/14:00435222 RIV/68407700:21220/14:00226987 RIV/68407700:21340/14:00226987 RIV/68407700:21670/14:00226987 RIV/61989592:15310/14:33152022

Výsledek na webu

<a href="http://dx.doi.org/10.1088/1748-0221/9/09/P09009" target="_blank" >http://dx.doi.org/10.1088/1748-0221/9/09/P09009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1748-0221/9/09/P09009" target="_blank" >10.1088/1748-0221/9/09/P09009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A neural network clustering algorithm for the ATLAS silicon pixel detector

Popis výsledku v původním jazyce

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

Název v anglickém jazyce

A neural network clustering algorithm for the ATLAS silicon pixel detector

Popis výsledku anglicky

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BF - Elementární částice a fyzika vysokých energií

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

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

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Svazek periodika

9

Číslo periodika v rámci svazku

15.zari

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

35

Strana od-do

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Kód UT WoS článku

000343281300046

EID výsledku v databázi Scopus

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