Higgs Physics with the XFEL Compton $\boldsymbol{\gamma\gamma}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV

TL;DR

This paper explores Higgs boson production at a novel XFEL gamma-gamma collider using advanced deep learning techniques, demonstrating high sensitivity and potential for new physics insights.

Contribution

It introduces a transformer-based deep learning framework combined with a genetic algorithm for improved signal-background discrimination in Higgs studies at the XFEL collider.

Findings

High sensitivity in Higgs detection achieved

Deep learning framework outperforms traditional methods

Potential for new physics exploration at XFEL collider

Abstract

We investigate single Higgs production in $\sqrt{s}=125$ GeV $\gamma\gamma$ collisions at the X-Ray Free Electron (XFEL) Compton Collider (XCC) concept and present an analysis targeting the major hadronic, semi-leptonic, and fully leptonic final states of the Higgs boson, including $H\to s\overline{s}$. In addition to studying Higgs production at a novel collider concept, our approach couples a novel set transformer-based deep learning framework that acts on particle-flow object point clouds with a genetic algorithm optimizer for signal-background discrimination, yielding significantly higher sensitivity than traditional methods. Our results demonstrate that an XFEL $\gamma\gamma$ collider can probe the Higgs sector with extremely high precision and enable new physics opportunities, complementary to proposed $e^+e^-$ machines.