| Title: | Mass composition of Telescope Array's surface detectors events using deep learning |
|---|
| Authors: | ID Kharuk, I. (Author)
ID Lundquist, Jon Paul (Author), et al. |
|---|
| Files: |  ICRC2021_384.pdf (788,87 KB)
MD5: BA78663A7EA8CACD24AB401D8122F062
 https://pos.sissa.it/395/384/
https://pos.sissa.it/395/384/pdf
|
|---|
| Language: | English |
|---|
| Work type: | Not categorized |
|---|
| Typology: | 1.08 - Published Scientific Conference Contribution |
|---|
| Organization: | UNG - University of Nova Gorica
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks |
|---|
| Publication status: | Published |
|---|
| Year of publishing: | 2022 |
|---|
| PID: | 20.500.12556/RUNG-8479  |
|---|
| COBISS.SI-ID: | 166306563 |
|---|
| DOI: | 10.22323/1.395.0384 |
|---|
| NUK URN: | URN:SI:UNG:REP:N24WRR9E |
|---|
| Publication date in RUNG: | 29.09.2023 |
|---|
| Views: | 1673 |
|---|
| Downloads: | 5 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
