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

Understand

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.