arXiv subjects

Khawla Salime

Publications and source records attributed to Khawla Salime.

3 recordsLinked to original sources

arXiv2026

Probing Heavy Neutral Higgs Bosons via Single Vector-Like Bottom Quark Production at the HL-LHC

We investigate the discovery prospects of a singly produced vector-like bottom quark in the Type-II Two-Higgs-Doublet Model extended by an $SU(2)_L$ vector-like $(T,B)$ doublet. We focus on the non-standard decay chain $B \to ϕb$, followed by $ϕ\to t\bar{t}$, where $ϕ= H$ or $A$, leading to a final state with one charged lepton, missing transverse energy, and multiple $b$-jets. We perform a full simulation of both signal and Standard Model backgrounds at $\sqrt{s}=14$ TeV. We show that the exotic channels $B \to ϕb$ can dominate over the conventional decay modes, reaching branching ratios of order $50\%$ for both neutral scalars in the alignment limit. A conventional cut-based analysis provides a $5σ$ discovery significance only at sufficiently high integrated luminosity. By contrast, an XGBoost-based multivariate analysis substantially improves the signal-background discrimination and extends the discovery reach up to $m_B \simeq 1.3$ TeV with $600~\mathrm{fb}^{-1}$ and up to $m_B \simeq 1.6$ TeV with $3~\mathrm{ab}^{-1}$, even in the presence of systematic uncertainties as large as $15\%$.

hep-ph↗

arXiv2026

Impact of hidden heavy Higgs channels of VLB-Quarks below 1 TeV in 2HDM

We investigate the phenomenological impact of incorporating vector-like bottom (VLB) quarks into the Type-II Two-Higgs-Doublet Model (2HDM-II). This framework introduces novel beyond-Standard-Model (BSM) decay channels $B \to Hb$, $B \to Ab$, and $B \to H^-t$, which are typically ignored by LHC pair-production searches focused on Standard Model (SM) final states ($B \to Zb$, $B \to hb$, $B \to Wt$). Our analysis reveals that these BSM pathways significantly weaken current VLB mass constraints. In the 2HDM-II alignment limit, the mass limit for a singlet $B$ shifts from approximately 1.5 TeV down to 1.34 TeV. For $(T, B)$ and $(B, Y)$ doublet configurations, the mass limits relax further to approximately 0.98 TeV, driven by the dominance of $B \to Hb$ and $B \to Ab$ decays, which can reach combined branching ratios of nearly 100\%.

hep-ph↗

arXiv2024

Vector-Like Quarks at the LHC: A Unified Perspective from ATLAS and CMS Exclusion Limits

In this work, we present a comprehensive review of the most up-to-date exclusion limits on Vector-Like Quarks (VLQs) derived from ATLAS and CMS data at the Large Hadron Collider (LHC). Our analysis encompasses both pair and single production modes, systematically comparing results from the two collaborations to identify and employ the most stringent bounds at each mass point. We evaluate the excluded parameter space for VLQs under singlet, doublet, and triplet representations. For top-like VLQs ($T$), the exclusion limits rule out masses up to 1.49 TeV in singlet scenarios, while single production constrains the mixing parameter $κ$ to values below 0.26 at $m_T \sim 1.5$ TeV and up to 0.42 for $m_T \sim 2$ TeV. For bottom-like VLQs ($B$), the strongest exclusion limits from pair production exclude masses up to 1.52 TeV in doublet configurations, with single production constraining $κ$ values between 0.2 and 0.7 depending on the mass. For exotic VLQs, such as $X$ and $Y$, pair production excludes masses up to 1.46 TeV and 1.7 TeV, respectively. The constraints on $κ$ from these analyses become increasingly restrictive at higher masses, reflecting the enhanced sensitivity of single production channels in this regime. For $X$, $κ$ is constrained below 0.16 for masses between 0.8 and 1.6 TeV and further tightens to $κ< 0.2$ as the mass approaches 1.8 TeV. Similarly, for $Y$, $κ$ values are constrained below 0.26 around $m_Y \sim 1.7$ TeV, with exclusions gradually relaxing at higher masses. These exclusion regions, derived from the most stringent LHC search results, offer a unified and up-to-date perspective on VLQ phenomenology. The results were computed using \texttt{VLQBounds}, a new Python-based tool specifically developed for this purpose.

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