Meisam Ghasemi Bostanabad

arXiv2026

Application of Deep Learning to Jet Charge Discrimination

The Large Hadron Collider (LHC) produces an enormous volume of data in which the identification and characterization of hadronic jets is a central challenge. Determining the electric charge of the parton initiating a light-quark jet; a task known as jet-charge discrimination; is highly valuable for both precision tests of the Standard Model (SM) and searches for physics beyond it. In this work, we benchmark a range of classical and quantum machine-learning models for the task of distinguishing up-quark from anti-up-quark jets in a controlled QCD environment. Among the approaches tested, a Graph Neural Network achieved the best performance, with an AUC of 0.883. Jet-charge tagging of this kind has broad phenomenological applications, from improving measurements of charge asymmetries to enhancing sensitivity in searches for new particles from beyond the SM where quark versus antiquark discrimination is essential. Our study provides a methodological foundation for deploying modern machine-learning techniques in jet-charge analyses at the LHC experiments.

hep-ph↗

arXiv2025

Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC

We explore the top quark flavor-changing 4-Fermi interactions ($tuee$ and $tcee$) with scalar, vector, and tensor structures using machine learning models to analyze tri-lepton processes at the LHC. The study is performed using $t\bar{t}$ and $tW$ processes, where a top quark decays into $u/c+e^{+}+e^{-}$. The analysis incorporates both reducible and irreducible backgrounds while accounting for realistic detector effects. The dominant backgrounds for these trilepton signatures arise from $t\bar{t}$ production, single top quark production in association with $V$, and $VV$ production (where $V = W, Z$). These backgrounds are significantly reduced using machine learning-based classification models, which optimize event selection and improve signal sensitivity. For an integrated luminosity of 3000 fb$^{-1}$ at the LHC, we find that the expected $95\%$ confidence level (CL) limits on the scale of 4-Fermi FCNC interactions reach $Λ\leq 5.5$ TeV for $tuee$ and $Λ\leq 5.7$ TeV for $tcee$ in the $t\bar{t}$ channel, and $Λ\leq 1.9$ TeV ($tuee$) and $Λ\leq 2.0$ TeV ($tcee$) in the $tW$ channel. We also provide an interpretation of our EFT analysis in the context of a specific $Z'$ model, illustrating how the derived constraints translate into bounds on the parameter space of a heavy neutral gauge boson mediating flavor-changing interactions.

hep-ph↗