Decoding Heavy Top-Philic Resonances at the HL-LHC: From Parton Kinematics to Deep Learning Signatures
Abstract
The stabilization of the electroweak scale strongly motivates the search for new heavy resonances and top-partner states. We investigate the discovery potential of exotic resonances mediating the production of vector-like quarks at the High-Luminosity LHC (√s = 13.6 TeV), focusing on the pp → X → t t̅p cascade decay. Using a model-independent Effective Field Theory approach, we classify the intermediate mediator X by its spin (0 and 1) and color (singlet and octet) representations. Parton-level kinematics demonstrate that the normalized differential cross-section with respect to the transverse momentum provides a robust observable for spin discrimination. To address the experimental challenges of highly boosted, semi-resolved hadronic decays, we introduce a cut-flow strategy based on an inclusive four-jet invariant mass reconstruction, M(4j). Because this standard approach remains sensitive to systematic uncertainties, we implement a Generalized BSM Tagger based on Deep Neural Networks (DNNs). By exploiting non-linear multi-jet correlations, the DNN achieves robust background rejection factors ranging from 𝒪(600) to 𝒪(1000) while preserving the underlying partonic signatures. Projecting to an integrated luminosity of 3000 fb−1, this combined strategy yields a projected statistical significance of 3.74σ for the dominant color-octet vector channel, establishing strong evidence potential and offering a robust phenomenological baseline for future HL-LHC searches.
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BibTeX
@misc{boukhrouf2026decoding,
title = {{Decoding Heavy Top-Philic Resonances at the HL-LHC: From Parton Kinematics to Deep Learning Signatures}},
author = {Hadjer Boukhrouf and Zouina Belghobsi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.10130},
eprint = {2609.10130},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10130},
}
RIS
TY - UNPB
TI - Decoding Heavy Top-Philic Resonances at the HL-LHC: From Parton Kinematics to Deep Learning Signatures
AU - Hadjer Boukhrouf
AU - Zouina Belghobsi
PY - 2026
DA - 2026/09/09
PB - arXiv
DO - 10.48550/arxiv.2609.10130
UR - https://arxiv.org/abs/2609.10130
AB - The stabilization of the electroweak scale strongly motivates the search for new heavy resonances and top-partner states. We investigate the discovery potential of exotic resonances mediating the production of vector-like quarks at the High-Luminosity LHC (√s = 13.6 TeV), focusing on the pp → X → t t̅_p cascade decay. Using a model-independent Effective Field Theory approach, we classify the intermediate mediator X by its spin (0 and 1) and color (singlet and octet) representations. Parton-level kinematics demonstrate that the normalized differential cross-section with respect to the transverse momentum provides a robust observable for spin discrimination. To address the experimental challenges of highly boosted, semi-resolved hadronic decays, we introduce a cut-flow strategy based on an inclusive four-jet invariant mass reconstruction, M(4j). Because this standard approach remains sensitive to systematic uncertainties, we implement a Generalized BSM Tagger based on Deep Neural Networks (DNNs). By exploiting non-linear multi-jet correlations, the DNN achieves robust background rejection factors ranging from 𝒪(600) to 𝒪(1000) while preserving the underlying partonic signatures. Projecting to an integrated luminosity of 3000 fb^−1, this combined strategy yields a projected statistical significance of 3.74σ for the dominant color-octet vector channel, establishing strong evidence potential and offering a robust phenomenological baseline for future HL-LHC searches.
ER -