| Naslov: | Mass composition of Telescope Array's surface detectors events using deep learning |
|---|
| Avtorji: | ID Kharuk, I. (Avtor)
ID Lundquist, Jon Paul (Avtor), et al. |
|---|
| Datoteke: |  ICRC2021_384.pdf (788,87 KB)
MD5: BA78663A7EA8CACD24AB401D8122F062
 https://pos.sissa.it/395/384/
https://pos.sissa.it/395/384/pdf
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Delo ni kategorizirano |
|---|
| Tipologija: | 1.08 - Objavljeni znanstveni prispevek na konferenci |
|---|
| Organizacija: | UNG - Univerza v Novi Gorici
|
|---|
| Opis: | We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events. |
|---|
| Ključne besede: | Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks |
|---|
| Status publikacije: | Objavljeno |
|---|
| Leto izida: | 2022 |
|---|
| PID: | 20.500.12556/RUNG-8479  |
|---|
| COBISS.SI-ID: | 166306563 |
|---|
| DOI: | 10.22323/1.395.0384 |
|---|
| NUK URN: | URN:SI:UNG:REP:N24WRR9E |
|---|
| Datum objave v RUNG: | 29.09.2023 |
|---|
| Število ogledov: | 738 |
|---|
| Število prenosov: | 4 |
|---|
| Metapodatki: |     |
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
