报告摘要

The Giant Radio Array for Neutrino Detection (GRAND) is a radio detector that is under contruction to search for ultra-high energy cosmic rays, gamma rays, and neutrinos. The initial stage of detector construction with 13 antennas has been completed at DunHuang, China. More detection sites across the world will be selected in future. This is an international joint effort of scientists across 12 countries spanning 4 continents. There are 42 institutions, with growing participating institutions, involved in R&D, antenna deployment, and data analysis and management. In this talk, I will introduce the collaborative efforts in GRAND, focusing primarily on data management and software development. With the deployment of 13 antennas, the GRAND will soon start accumulating colossal amount of data, thus, creating the need for software tools for data management and analyses. The software package is still under development primarily by groups in 4 countries and will be released in the next few weeks. I will discuss in detail the effort of these joint works which is intended to be used by all members for signal processing, coordinate transformation, topographical measurements, and geomagnetic field calculations, among others. Additionally, this talk will consist of how codes are stored in software hosting platforms such as GitHub. GitHub aids in international collaboration of developers by acting as a common platform to work on and share codes.

个人简介

Ramesh Koirala received his doctorate from the University of Delaware, USA in 2019. Prior to joining the GRAND collaboration in 2020, he worked in the IceCube collaboration primarily focusing on cosmic rays. Koirala's academic research has focused on developing and implementing a new trigger algorithm for measuring cosmic ray air showers with energies around 100 TeV and higher using IceTop. He also introduced a machine learning technique for the reconstruction of these cosmic ray air showers. He has authored and co-authored a number of peer-reviewed publications. He is currently working as a postdoctorate with the GRAND collaboration at Nanjing University in software tool development for signal processing and data analysis. He is also involved in developing a machine learning technique for air shower reconstruction detected by the GRAND.