Boosting probes of $$ \mathcal{CP} $$ violation in the top Yukawa coupling with Deep Learning

A bstract The precise measurement of the top-Higgs coupling is crucial in particle physics, offering insights into potential new physics Beyond the Standard Model (BSM) carrying $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> Violation (CPV) effects. In this paper, we explore the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> properties of a Higgs boson coupling with a top quark pair, focusing on events where the Higgs state decays into a pair of b -quarks and the top-antitop system decays leptonically. The novelty of our analysis resides in the exploitation of two conditional Deep Learning (DL) networks: a Multi-Layer Perceptron (MLP) and a Graph Convolution Network (GCN). These models are trained for selected CPV phase values and then used to interpolate all possible values ranging from 0 to π /2. This enables a comprehensive assessment of sensitivity across all $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> phase values, thereby streamlining the process as the models are trained only once. Notably, the conditional GCN exhibits superior performance over the conditional MLP, owing to the nature of graph-based Neural Network (NN) structures. Specifically, for Higgs top coupling modifier set to 1, with $$ \sqrt{s} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:math> = 13.6 TeV and integrated luminosity of 3 ab −1 GCN excludes the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> phase larger than 5 ° at 95 . 4% Confidence Level (C.L). Our Machine Learning (ML) informed findings indicate that assessment of the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> properties of the Higgs coupling to the $$ t\overline{t} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> </mml:math> pair can be within reach of the High Luminosity Large Hadron Collider (HL-LHC), quantitatively surpassing the sensitivity of more traditional approaches.

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Journal
Journal of High Energy Physics
Published
2025-12-05
DOI
https://doi.org/10.1007/jhep12(2025)050
Citations
2
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
3.74

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article

Boosting probes of $$ \mathcal{CP} $$ violation in the top Yukawa coupling with Deep Learning

Stefano Moretti, Waleed Esmail, Adil Jueid, A. Hammad
2 citations
Journal of High Energy Physics
Particle physics theoretical and experimental studies
3.74
article

Boosting probes of $$ \mathcal{CP} $$ violation in the top Yukawa coupling with Deep Learning

Stefano Moretti, Waleed Esmail, Adil Jueid, A. Hammad
article en
2 citations

Abstract

A bstract The precise measurement of the top-Higgs coupling is crucial in particle physics, offering insights into potential new physics Beyond the Standard Model (BSM) carrying $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> Violation (CPV) effects. In this paper, we explore the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> properties of a Higgs boson coupling with a top quark pair, focusing on events where the Higgs state decays into a pair of b -quarks and the top-antitop system decays leptonically. The novelty of our analysis resides in the exploitation of two conditional Deep Learning (DL) networks: a Multi-Layer Perceptron (MLP) and a Graph Convolution Network (GCN). These models are trained for selected CPV phase values and then used to interpolate all possible values ranging from 0 to π /2. This enables a comprehensive assessment of sensitivity across all $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> phase values, thereby streamlining the process as the models are trained only once. Notably, the conditional GCN exhibits superior performance over the conditional MLP, owing to the nature of graph-based Neural Network (NN) structures. Specifically, for Higgs top coupling modifier set to 1, with $$ \sqrt{s} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:math> = 13.6 TeV and integrated luminosity of 3 ab −1 GCN excludes the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> phase larger than 5 ° at 95 . 4% Confidence Level (C.L). Our Machine Learning (ML) informed findings indicate that assessment of the $$ \mathcal{CP} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CP</mml:mi> </mml:math> properties of the Higgs coupling to the $$ t\overline{t} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> </mml:math> pair can be within reach of the High Luminosity Large Hadron Collider (HL-LHC), quantitatively surpassing the sensitivity of more traditional approaches.

Journal of High Energy PhysicsVol. 2025(12)
Uppsala University (SE), University of Münster (DE), Center for Theoretical Physics (PL), Institute for Basic Science (KR), University of Southampton (GB)
Bundesministerium für Bildung und Forschung, Science and Technology Development Fund, Institute for Basic Science, Science and Technology Facilities Council
Openalex Percentile: Top 6%
Particle physics theoretical and experimental studies
3.74
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