Deep generative models for fast photon shower simulation in ATLAS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00616540" target="_blank" >RIV/68378271:_____/24:00616540 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/24:10494659

Výsledek na webu

<a href="https://hdl.handle.net/11104/0363523" target="_blank" >https://hdl.handle.net/11104/0363523</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s41781-023-00106-9" target="_blank" >10.1007/s41781-023-00106-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep generative models for fast photon shower simulation in ATLAS

Popis výsledku v původním jazyce

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Název v anglickém jazyce

Deep generative models for fast photon shower simulation in ATLAS

Popis výsledku anglicky

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computing and Software for Big Science

ISSN

2510-2036

e-ISSN

2510-2044

Svazek periodika

8

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

40

Strana od-do

7

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85189330049