Interference Effects in Resonant Standard Model di-Higgs Production and Decay into $4b$ Final States: the Role of Machine Learning Analysis

TL;DR

This paper investigates the impact of interference effects in resonant di-Higgs production and decay into four b-quarks, employing machine learning techniques to improve background suppression and signal significance in the context of the NMSSM.

Contribution

It introduces a novel ML-based analysis using a multi-modal Transformer to better account for interference effects in resonant di-Higgs searches, improving detection sensitivity.

Findings

Transformer-based ML outperforms traditional algorithms in significance.

Interference effects significantly influence the signal interpretation.

Neglecting interference can lead to incorrect experimental conclusions.

Abstract

The final state with four $b$-quarks has generally the largest event rate in Standard Model (SM)-like Higgs ($h_{\rm SM}$) pair production, but also the largest backgrounds. We study such a final state using the $gg\to h_{\rm SM}h_{\rm SM}$ production mechanism and Benchmarks Points (BPs) derived from the Next-to-Minimal Supersymmetric SM (NMSSM) in the boosted case, leading to two (fat) 'Higgs jets'. To suppress the backgrounds we use a combination of both kinematical cuts and jet substructure features exploiting Machine Learning (ML) analysis. We simulate the signal BPs both with and without the interference of the resonant $s$-channel diagram with the non-resonant topologies emerging from both the SM and NMSSM. The ML architecture of choice here is based on a multi-modal Transformer, which performs significantly better than traditional ML algorithms, in two respects: firstly, it enables to achieve higher significances and, secondly, it adapts better to the analysis dataset with interferences even if it was trained on one without these. However, neglecting the effect of the latter in experimental searches could lead to grossly mistaken results.