Bibhuti Parida | Searcharxiv

arXiv2024

Description of $Z$ Boson Mass and $p_T$ Spectrum at LHC Using Leading-Order Event Generators at $\sqrt{s} = 13.6$ TeV

In this paper, we report a study of $Z$ boson kinematic distributions in the $Z$+ 1-jet events from proton-proton ($pp$) collisions at $\sqrt{s}$ = 13.6 TeV at the Large Hadron Collider (LHC) using Leading-Order (LO) event generators. Three LO event generators, namely Pythia8, Herwig7 and Sherpa2, are explored with the implementation of appropriate Parton Shower (PS) and Matrix Element (ME) corrections. The $Z$ boson is reconstructed from oppositely charged di-lepton pairs ($μ^+μ^-$ and $e^+e^-$), and the associate leading jet is reconstructed with the anti-$k_T$ algorithm with radius $r$=0.4. The di-lepton invariant mass, $M_{\ell^+\ell^-}$, and the transverse momentum of the reconstructed $Z$ boson, $p_{T}^Z$, distributions are extensively studied by comparing three of the Monte Carlo (MC) predictions. In addition to this, we also explore several other kinematic distributions associated with the candidate lepton and the jet to distinguish the modelling of the LO event generators in depth. Furthermore, various showering modules within these event generators are explored for $Z$ + 1-jet production. While the three of these MC frameworks reproduce the $Z$ + 1-jet kinematics similarly, certain discrepancies in their predictions are identified in some variables. We analyzed them using different statistical tests. The best match of the $p_{T}^Z$ spectra was found between the Pythia8 and Sherpa2 results.

hep-ph↗

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↗