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Naslov:Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data
Avtorji:ID Aschersleben, Jann (Avtor)
ID Bhattacharyya, Saptashwa (Avtor)
ID MARČUN, Barbara (Avtor)
ID Pérez Romero, Judit (Avtor)
ID Stanič, Samo (Avtor)
ID Vodeb, Veronika (Avtor)
ID Vorobiov, Serguei (Avtor)
ID Zaharijas, Gabrijela (Avtor)
ID Zavrtanik, Marko (Avtor)
ID Zavrtanik, Danilo (Avtor)
ID Živec, Miha (Avtor), et al.
Datoteke:.pdf ICRC2021_697.pdf (1,24 MB)
MD5: 222319897107B317CDD5DDD3D7F93283
 
URL https://pos.sissa.it/395/
 
URL https://pos.sissa.it/395/697/pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:UNG - Univerza v Novi Gorici
Opis:The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, o˙ering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is di˙erent from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance.
Ključne besede:Cherenkov Telescope Array, very-high-energy astronomy, convolutional neural networks
Status publikacije:Objavljeno
Leto izida:2021
PID:20.500.12556/RUNG-8421 Novo okno
COBISS.SI-ID:164774659 Novo okno
NUK URN:URN:SI:UNG:REP:OFRW7TZ8
Datum objave v RUNG:18.09.2023
Število ogledov:1972
Število prenosov:5
Metapodatki:XML DC-XML DC-RDF
:
ASCHERSLEBEN, Jann, BHATTACHARYYA, Saptashwa, MARČUN, Barbara, PÉREZ ROMERO, Judit, STANIČ, Samo, VODEB, Veronika, VOROBIOV, Serguei, ZAHARIJAS, Gabrijela, ZAVRTANIK, Marko, ZAVRTANIK, Danilo in ŽIVEC, Miha, 2021, Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data. V : 37th International Cosmic Ray Conference [na spletu]. Objavljeni znanstveni prispevek na konferenci. Trst, Italija. 2021. [Dostopano 12 april 2025]. Pridobljeno s: https://repozitorij.ung.si/IzpisGradiva.php?lang=slv&id=8421
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Gradivo je del monografije

Naslov:37th International Cosmic Ray Conference : ICRC2021
Kraj izida:Trst, Italija
Leto izida:2021

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Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
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