Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC

TL;DR

This paper introduces a deep neural network based on Particle Transformer with attention mechanisms to enhance the sensitivity of Higgs pair production searches in the 4b channel at the LHC, improving measurement precision.

Contribution

It presents a novel Transformer-based deep learning approach that processes full event information for Higgs pair analysis, outperforming traditional methods.

Findings

Constrains Higgs self-coupling parameter with over 40% improved precision.

Demonstrates superior performance of Transformer model over other machine learning architectures.

Highlights the effectiveness of attention mechanisms in collider data analysis.

Abstract

The Higgs self-coupling is crucial for understanding the structure of the scalar potential and the mechanism of electroweak symmetry breaking. In this work, utilizing deep neural network based on Particle Transformer that relies on attention mechanism, we present a comprehensive analysis of the measurement of the trilinear Higgs self-coupling through the Higgs pair production with subsequent decay into four $b$-quarks ($HH\to b\bar{b}b\bar{b}$) at the LHC. The model processes full event-level information as input, bypassing explicit jet pairing and can serves as an event classifier. At HL-LHC, our approach constrains the $\kappa_\lambda$ to $(-0.53,6.01)$ at 68\% CL achieving over 40\% improvement in precision over conventional cut-based analyses. Comparison against alternative machine learning architectures also shows the outstanding performance of the Transformer-based model, which is mainly due to its ability to capture the correlations in the high-dimensional collision data with the help of attention mechanism. The result highlights the potential of attention-based networks in collider phenomenology.