  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.
