arXiv subjects

Souvik Maity

Publications and source records attributed to Souvik Maity.

2 recordsLinked to original sources

arXiv2025

Design and Performance Simulation of the Electromagnetic Calorimeter at EicC

The electromagnetic calorimeter (ECAL) is a key detector component for precise electron and photon measurements in electron-ion collision experiments. At the Electron-Ion Collider in China (EicC), high-performance calorimetry is essential for exploring the internal structure of nucleons and studying the dynamics of quarks and gluons within quantum chromodynamics (QCD). This paper presents the optimized design and performance simulation of the EicC ECAL system. The ECAL consists of three specialized sections tailored to distinct detection environments: (1) an electron-Endcap employing high-resolution pure Cesium Iodide (pCsI) crystals, (2) a central barrel, and (3) an ion-Endcap, both adopting a cost-effective Shashlik-style sampling calorimeter with improved light yield. Each segment's geometry and material composition have been systematically optimized through Geant4 simulations to achieve excellent energy and position resolutions as well as strong electron-pion discrimination. The simulated performance indicates that the ECAL can achieve energy resolutions of 2 percent divided by sqrt(E) for pCsI crystals and 5 percent divided by sqrt(E) for Shashlik modules, meeting the design goals of the EicC detector.

physics.ins-det↗

arXiv2022

A Machine Learning system to monitor student progress in educational institutes

In order to track and comprehend the academic achievement of students, both private and public educational institutions devote a significant amount of resources and labour. One of the difficult issues that institutes deal with on a regular basis is understanding the exam shortcomings of students. The performance of a student is influenced by a variety of factors, including attendance, attentiveness in class, understanding of concepts taught, the teachers ability to deliver the material effectively, timely completion of home assignments, and the concern of parents and teachers for guiding the student through the learning process. We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score that helps to comprehend the learning journeys of students and identify activities that lead to subpar performances. This would make it easier for educators and institute management to create guidelines for system development to increase productivity. The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System. In this article, we demonstrate the proof of the concept under simplified assumptions using simulated data.

cs.CY↗