1 citations · 1 across the 2 of their papers we have counts for
4 papers · 1 filter
Revisiting the Electroweakino Sector of the Baryon Number Violating MSSM at the HL-LHC with Deep Neural Networks
Rahool Kumar Barman, Arghya Choudhury, Subhadeep Sarkar
We study the projected sensitivity of direct electroweakino production at the HL-LHC in a simplified framework with wino-like, mass degenerate…
Reconstructing Sparticle masses at the LHC using Generative Machine Learning
Rahool Kumar Barman, Arghya Choudhury, Subhadeep Sarkar
We explore a generative model framework to infer the masses of heavy particles from detector-level data over a broad parameter space. Our model combines a transformer-based detecto…
Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks
Arghya Choudhury, Arpita Mondal, Subhadeep Sarkar
Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), th…
Slepton searches in the trilinear RPV SUSY scenarios at the HL-LHC and HE-LHC
Arghya Choudhury, Arpita Mondal, Subhadeep Mondal +1
In this work we have studied a multi-lepton final state arising from sneutrino and left-handed slepton production at the high luminosity and high energy LHC in the context of R-par…