Ensemble Models for Calorimeter Simulations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F23%3A00374144" target="_blank" >RIV/68407700:21340/23:00374144 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1088/1742-6596/2438/1/012080" target="_blank" >https://doi.org/10.1088/1742-6596/2438/1/012080</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/2438/1/012080" target="_blank" >10.1088/1742-6596/2438/1/012080</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble Models for Calorimeter Simulations

Popis výsledku v původním jazyce

Foreseen increasing demand for simulations of particle transport through detectors in High Energy Physics motivated the search for faster alternatives to Monte Carlo-based simulations. Deep learning approaches provide promising results in terms of speed up and accuracy, among which generative adversarial networks (GANs) appear to be particularly successful in reproducing realistic detector data. However, the GANs tend to suffer from different issues such as not reproducing the full variability of the training data, missing modes problem, and unstable convergence. Various ensemble techniques applied to image generation proved that these issues can be moderated either by deploying multiple generators or multiple discriminators. This work follows a development of a GAN with two-dimensional convolutions that reproduces 3D images of an electromagnetic calorimeter. We build on top of this model and construct an ensemble of generators. With each new generator, the ensemble shows better agreement with the Monte Carlo images in terms of shower shapes and the sampling fraction.

Název v anglickém jazyce

Ensemble Models for Calorimeter Simulations

Popis výsledku anglicky

Foreseen increasing demand for simulations of particle transport through detectors in High Energy Physics motivated the search for faster alternatives to Monte Carlo-based simulations. Deep learning approaches provide promising results in terms of speed up and accuracy, among which generative adversarial networks (GANs) appear to be particularly successful in reproducing realistic detector data. However, the GANs tend to suffer from different issues such as not reproducing the full variability of the training data, missing modes problem, and unstable convergence. Various ensemble techniques applied to image generation proved that these issues can be moderated either by deploying multiple generators or multiple discriminators. This work follows a development of a GAN with two-dimensional convolutions that reproduces 3D images of an electromagnetic calorimeter. We build on top of this model and construct an ensemble of generators. With each new generator, the ensemble shows better agreement with the Monte Carlo images in terms of shower shapes and the sampling fraction.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2023061" target="_blank" >LM2023061: Výzkumná infrastruktura pro experimenty ve Fermilab</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Journal of Physics Conference Series

ISBN

—

ISSN

1742-6588

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IOP Institute of Physics

Místo vydání

Bristol

Místo konání akce

ELECTR NETWORK

Datum konání akce

29. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001026601300080