arXiv subjects

Shreecheta Chowdhury

Publications and source records attributed to Shreecheta Chowdhury.

5 recordsLinked to original sources

arXiv2026

Machine Learning Enhanced Detection of Higgs Chain Decays in Vector Boson Fusion

Over the years, Vector Boson Fusion (VBF) has established itself as one of the most robust production channels for studying the Higgs boson, while also serving as a promising pathway for exploring potential signatures of physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). Following the discovery of a SM-like Higgs boson, new opportunities have arisen to also investigate heavy resonances that decay into SM-like Higgs boson pairs, $hh$, thereby offering valuable insights into the structure of the Higgs sector and the dynamics governing Electro-Weak Symmetry Breaking (EWSB). In this work, we analyze a final state involving, alongside 2 forward/backward light quarks, 4 $b$-quarks emerging from the chain decay $h_2\to h_1h_1\to b\bar b b\bar b$ wherein the heavy CP-even Higgs state $h_2$ is produced in the VBF process $qq\to qqh_2$ and belongs to the Next-to-Minimal Supersymmetric SM (NMSSM). This BSM scenario is used as an illustrative example of the potential of using only low-level calorimeter information enhanced by advanced Deep Learning (DL) methodologies in searching for this channel, which can achieve a statistical significance of approximately $4.5σ$, for an integrated luminosity of 300 fb$^{-1}$ at the CERN machine.

hep-ph↗

arXiv2026

Search for Quadruplet Scalars using Boosted Decision Trees at the LHC

Beyond the Standard Model scenarios introduce additional scalar and fermion multiplets, which influence neutrino mass generation mechanisms and yield distinctive collider signatures. This work focuses on a particular scenario involving a fermion quintuplet and a scalar quadruplet. The study examines the production and decay of the scalar quadruplet components at the Large Hadron Collider (LHC), emphasizing how their decay patterns, fermiophobic versus fermiophilic, depend on mass differences and Yukawa couplings with the fermion multiplets. This study provides an overview of possible signals at the LHC, along with a detailed collider analysis focused on final states containing at least four leptons and two jets, in which the masses of the scalars and fermions are reconstructed successfully. Standard Model backgrounds are also incorporated in the study, with multivariate techniques leveraged via Boosted Decision Trees. Results indicate discovery potential for scalar masses around 600-700 GeV and exclusion sensitivity extending beyond 1 TeV, highlighting the promising experimental signatures of the model and its role in probing new physics at colliders.

hep-ph↗

arXiv2025

Probes of Anomalous Events at LHC with Self-Organizing Maps

We propose an Unsupervised Learning Algorithm, Self-Organizing Maps (SOM), built on a neural network architecture, for the probe of a rare top decay, mediated by Flavor Changing Neutral Current (FCNC), to charm and the Higgs boson, with the Higgs boson further decaying to a pair of b-quarks or a pair of gauge bosons ($W^{\pm}/Z$) in a boosted regime. Ideally, the particles originating from the decay of the boosted top lead to the reconstruction of a large-R jet, comprising three-prong substructures, with b- and c-tagged subjets. The SOM algorithm has been demonstrated as a model-agnostic anomaly-finder for probing the rare decay at the LHC, by mapping distinct signal and background regions to separate non-overlapping clusters on the Kohnen map. This helps to identify signal regions with higher signal significances. We also discuss the robustness of this algorithm, especially for other BSM probes with model-agnostic and model-dependent searches.

hep-ph↗

arXiv2025

Boosted Top Tagging through Flavour-violating interactions at the LHC

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 $t \to cH$ branching ratio is marginal, even at the high luminosity run of LHC, compared to the existing limits from the LHC 13 TeV data. Although the result seems negative from the point of view of BSM reach, it is significant enough to motivate further studies in the search for the rare top decays at future colliders with higher energy and luminosity. Additionally, the paper utilizes SHAP, a Game Theory-based method, to analyze the contribution of each observable to the classification of events, offering valuable insights into the classifier. The approach presented in this paper is robust in scope and can be effectively implemented for similar probes of physics beyond the SM.

hep-ph↗

arXiv2024

Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects

Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC). Our aim is to study the interplay of traditional jet substructure based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.

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