| Issue |
EPJ Web Conf.
Volume 283, 2023
Ultra High Energy Cosmic Rays (UHECR 2022)
|
|
|---|---|---|
| Article Number | 02011 | |
| Number of page(s) | 6 | |
| Section | Energy Spectrum and Mass Composition | |
| DOI | https://doi.org/10.1051/epjconf/202328302011 | |
| Published online | 28 April 2023 | |
https://doi.org/10.1051/epjconf/202328302011
A machine learning approach for mass composition analysis with TALE-SD data
Graduate School of Science, Osaka City University, Osaka, Japan
* Corresponding author: m21sa003@st.osaka-cu.ac.jp
Published online: 28 April 2023
The TALE experiment is a Telescope Array Low-energy Extension constructed to observe cosmic rays with energies down to 1016.5 to clarify the origin of the second knee and the energy of the galactic to extragalactic CRs transition. TALE consists of 10 high-elevation fluorescence detectors and 80 scintillation counters in an area of 21 km2. The key of data interpretation is the mass composition of cosmic rays, and we report on a machine learning approach of mass composition analysis that utilizes waveform data of TALE scintillation counters.
© The Authors, published by EDP Sciences, 2023
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