DPF - PHENO 2024

Probing a GeV-scale Scalar Boson and a TeV-scale Vector-like Quark Associated with U(1)T3R at the Large Hadron Collider using Machine Learning

13 May 2024, 15:15

15m

David Lawrence Hall 105 (University of Pittsburgh)

Machine Learning & AI Machine Learning & AI

Speaker

Umar Sohail Qureshi (Vanderbilt University)

Description

A model based on a $U(1{)}_{T3R}$ extension of the Standard Model can address the mass hierarchy between the third and the first two generations of fermions, explain thermal dark matter abundance, and the muon $g-2$ and $R_{K^{(\ast )}}$ anomalies. The model contains a light scalar boson ${\varphi }^{\prime }$ and a heavy vector-like quark ${\chi }_u$ that can be probed at CERN's Large Hadron Collider (LHC). We perform a phenomenology study on the production of ${\varphi }^{\prime }$ and ${\chi }_u$ particles from proton-proton $(pp)$ collisions at the LHC at $\sqrt{s}=13$ TeV primarily through $g-g$ and $t-{\chi }_u$ fusion. We work adopt a phenomenological framework, an effective field theory approach, in which the ${\chi }_u$ and ${\varphi }^{\prime }$ masses are free parameters and consider the final states of the ${\chi }_u$ decaying to $b$-quarks, muons, and MET from neutrinos and the ${\varphi }^{\prime }$ decaying to ${\mu }^{+}{\mu }^{-}$. The analysis is performed using machine learning algorithms, over traditional methods, to maximize the signal sensitivity with integrated luminosities of of $150,300$, and $3000$ fb${}^{-1}$. Further, we note the proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.

Presentation materials

DPF_Pheno_U1T3R_Slides_Final.pdf

DPF_Pheno_U1T3R_Slides.pdf