Main Image
Volume 358 - 36th International Cosmic Ray Conference (ICRC2019) - CRI - Cosmic Ray Indirect
Study Cosmic Ray Mass Composition using Deep Learning in Telescope Array Surface Array Detector
O. Kalashev,* M. Kuznetsov
*corresponding author
Full text: pdf
Pre-published on: 2019 July 22
Published on:
The ultra-high-energy cosmic rays mass composition study with the Telescope Array surface detector is discussed. We present the new analysis based on deep convolutional neural network using detector signal time series as an input and trained on a large Monte-Carlo dataset. We compare the sensitivity of the new technique and the previously presented boosted decision tree multivariate analysis built upon 14 observables as well as the mass composition study using the depth of the extensive air shower maximum. Possible systematic errors of the method are discussed.
Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.