Detecting an axion-like particle with machine learning at the LHC

TL;DR

This paper employs machine learning, specifically CNNs on jet images, to detect light axion-like particles at the LHC, extending sensitivity and surpassing existing bounds on ALP-photon coupling.

Contribution

It introduces a novel CNN-based jet image analysis method to identify boosted ALPs decaying into photon pairs at the LHC, enhancing detection sensitivity for masses 0.3 to 5 GeV.

Findings

CNN approach extends LHC sensitivity to ALP masses 0.3-5 GeV.

Method surpasses existing limits on ALP-photon coupling.

Jet image analysis effectively identifies collimated photon pairs.

Abstract

Axion-like particles (ALPs) appear in various new physics models with spontaneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investigate such light ALPs through the ALP-strahlung production processes $pp \to W^\pm a, Z a$ with the sequential decay $a \to \gamma\gamma$ at the 14 TeV LHC with an integrated luminosity of 3000 fb$^{-1}$ (HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3~GeV to 5~GeV. The obtained bounds are stronger than the existing limits on the ALP-photon coupling.