Dr Amit Chakraborty, Assistant Professor in the Department of Physics and Ms Shreecheta Chowdhury, PhD Scholar, have co-authored a research paper titled “Boosted top tagging through flavour-violating interactions at the LHC”, which has been published in the European Physical Journal C (Nature Index), Q1 Journal having an impact factor 4.2.
Their research focuses on identifying a rare process involving the top quark, one of the heaviest particles. In this rare decay, the top quark transforms into a charm quark and a Higgs boson, which then breaks down into two b quarks. The team uses the data collected at the Large Hadron Collider (LHC), the world’s largest particle smasher, and employs Machine Learning techniques to investigate the possibility of understanding these collision events and identifying signatures of physics Beyond the Standard Model.
Abstract
This paper describes a method for detecting a rare top quark decay into a charm quark and a Higgs boson (H), which decays further into b quarks, at the Large Hadron Collider (LHC), and introduces a tagging algorithm to identify boosted tops using large-R jets containing b- and c-tagged elements. We consider the associated production of the top quark with a W-boson and identify different observables to discriminate the signal from the Standard Model (SM) background events. Although our model with improved jet substructure methods outperforms existing approaches to tag such rare decay tops, the improvement in the New Physics reach in terms of t → cH branching ratio is marginal, even at the high luminosity run of LHC, compared to the existing limits from the LHC 13 TeV data. Additionally, the paper utilizes SHAP, a Game Theory-based method, to analyse the contribution of each observable to the classification of events, offering valuable insights into the classifier.
Future Research Plans
The team plans to explore Beyond Standard Model (BSM) physics through collider phenomenology. Leveraging the Higgs boson as a portal, they aim to investigate its potential role in dark matter interactions, neutrino mass generation, and possibly being part of a larger scalar sector. Additionally, Dr Chakraborty will delve into ultra-light particle searches and develop novel jet physics techniques using advanced Machine Learning (ML) algorithms for particle identification and classification. A crucial aspect of his work will be creating testable search strategies, ensuring direct comparison with experimental data from current and future colliders.
