Denoising 3D Time Projection Chamber data using convolutional neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F25%3A00390190" target="_blank" >RIV/68407700:21670/25:00390190 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1088/1748-0221/20/05/C05014" target="_blank" >https://doi.org/10.1088/1748-0221/20/05/C05014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1748-0221/20/05/C05014" target="_blank" >10.1088/1748-0221/20/05/C05014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Denoising 3D Time Projection Chamber data using convolutional neural networks

Popis výsledku v původním jazyce

Spurious signals caused by microdischarges are a known effect inherent to all gaseous detectors. During the reconstruction in imaging and tracking detectors, such as time projection chambers, these signals are added to the actual track-generated signal as extra pixels or clusters, compromising the performance of the detector. The usual approach to remove these noise patterns is by hardware-dependent heuristics and conditions. In this work, we study the usage of denoising convolutional neural networks (NN) to clean the signals from a Time Projection Chamber (TPC) prototype. We show that this denoising provides also a tool for the selection and rejection of detector events that do not contain any track. The output provided by the neural network is compared with the results obtained using a conventional algorithm. The Physics of the events measured by the detector (such as the shape of the tracks) is used to assess and compare the quality of the two algorithms and how much they improve the existing data set.

Název v anglickém jazyce

Denoising 3D Time Projection Chamber data using convolutional neural networks

Popis výsledku anglicky

Spurious signals caused by microdischarges are a known effect inherent to all gaseous detectors. During the reconstruction in imaging and tracking detectors, such as time projection chambers, these signals are added to the actual track-generated signal as extra pixels or clusters, compromising the performance of the detector. The usual approach to remove these noise patterns is by hardware-dependent heuristics and conditions. In this work, we study the usage of denoising convolutional neural networks (NN) to clean the signals from a Time Projection Chamber (TPC) prototype. We show that this denoising provides also a tool for the selection and rejection of detector events that do not contain any track. The output provided by the neural network is compared with the results obtained using a conventional algorithm. The Physics of the events measured by the detector (such as the shape of the tracks) is used to assess and compare the quality of the two algorithms and how much they improve the existing data set.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10300 - Physical sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

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

20

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

2

Strana od-do

1-2

Kód UT WoS článku

001490735100001

EID výsledku v databázi Scopus

2-s2.0-105005203949