Seminar

One of the significant tasks (at the offline analysis stage) in the MPD experiment is charged particle identification (PID). There are conventional PID algorithms based on direct measurements of energy loss in the Time Projection Chamber and mass measurements provided by information from the Time-of-Flight system. Over the last ten years, machine learning approaches have become widely used in high energy physics problems in general and in PID in particular. This is due to the fact that conventional PID algorithms have poor performance in the high momentum range. This work is devoted to the machine learning application for PID in the MPD experiment. Current research results will be demonstrated.

The creation and development of various digital services contribute to enhancing the efficiency of scientific research and expediting the achievement of new significant results. The report presents the experience of elaborating and developing services to support scientific activities at JINR. Among the developments are the cloud service for scientific computing using the resources of the JINR Multifunctional Information and Computing Complex (saas.jinr.ru), the IFA Database system for grant proposal, and the SciDocsCloud service for scientific documentation storage. In addition, a series of works were performed to integrate JINR SSO with various JINR services, including wiki.jinr.ru, disk.jinr.ru, and jinrex.jinr.ru.