PoS - Proceedings of Science
Volume 358 - 36th International Cosmic Ray Conference (ICRC2019) - CRI - Cosmic Ray Indirect
Telescope Array FD Weather Classification using Machine Learning
G. Furlich* On behalf of the Telescope Array collaboration
*corresponding author
Full text: pdf
Pre-published on: August 22, 2019
Published on:
Abstract
Telescope Array (TA) cosmic ray observatory fluorescence detector (FD) sites have passed 10 years of operation. In order to select nights for further analysis, clear nights should be distinguished from cloudy or noisy nights in the FD data. Weather observations during the night are recorded every hour while the FDs are collecting data by on-site operators at the Middle Drum site. However a more robust and uniform weather classification method is desired for flagging bad weather in the FD data. A series of snapshots of the night sky was created using the detector’s photomultiplier tubes (PMTs) pedestals. We classified the night’s weather using the PMTs pedestal snapshots using a Recurrent Convolution Neural Network (RCNN).
DOI: https://doi.org/10.22323/1.358.0261
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.