A new technique based on convolutional neural networks to measure the energy of protons and electrons with a single Timepix detector

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21670%2F21%3A00350009" target="_blank" >RIV/68407700:21670/21:00350009 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TNS.2021.3071583" target="_blank" >https://doi.org/10.1109/TNS.2021.3071583</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TNS.2021.3071583" target="_blank" >10.1109/TNS.2021.3071583</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A new technique based on convolutional neural networks to measure the energy of protons and electrons with a single Timepix detector

Popis výsledku v původním jazyce

The Timepix chip has been exposed to the outer space for the first time with the Space Application of Timepix-based Radiation Monitor (SATRAM) instrument on Project for On-Board Autonomy Vegetation (Proba-V), a European Space Agency's (ESA) satellite. The objective of this study is to develop a new technique to improve the separation of protons and electrons, which are detected by the single-layer Timepix detector in SATRAM. The current identification method proposed by Gohl et al. (2019) is based on pattern recognition and stopping power measurements. In this article, the limitations of this method are discussed. A new method based on neural network trained with Geant4 data is proposed. Its validation with SATRAM data is presented. Similarly, a neural network trained with Geant4 data is introduced. Its purpose is to deduce the particles' incident energy using the energy deposited in the Timepix.

Název v anglickém jazyce

A new technique based on convolutional neural networks to measure the energy of protons and electrons with a single Timepix detector

Popis výsledku anglicky

The Timepix chip has been exposed to the outer space for the first time with the Space Application of Timepix-based Radiation Monitor (SATRAM) instrument on Project for On-Board Autonomy Vegetation (Proba-V), a European Space Agency's (ESA) satellite. The objective of this study is to develop a new technique to improve the separation of protons and electrons, which are detected by the single-layer Timepix detector in SATRAM. The current identification method proposed by Gohl et al. (2019) is based on pattern recognition and stopping power measurements. In this article, the limitations of this method are discussed. A new method based on neural network trained with Geant4 data is proposed. Its validation with SATRAM data is presented. Similarly, a neural network trained with Geant4 data is introduced. Its purpose is to deduce the particles' incident energy using the energy deposited in the Timepix.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Nuclear Science

ISSN

0018-9499

e-ISSN

1558-1578

Svazek periodika

68

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

1746-1753

Kód UT WoS článku

000687247300030

EID výsledku v databázi Scopus

2-s2.0-85103875554