  Axion-like particles (ALPs) that decay into photon pairs pose a challenge for
experiments that rely on the construction of a decay vertex in order to search
for long-lived particles. This is particularly true for beam-dump experiments,
where the distance between the unknown decay position and the calorimeter can
be very large. In this work we use machine learning to explore the possibility
to reconstruct the ALP properties, in particular its mass and lifetime, from
such inaccurate observations. We use a simulation-based inference approach
based on conditional invertible neural networks to reconstruct the posterior
probability of the ALP parameters for a given set of events. We find that for
realistic angular and energy resolution, such a neural network significantly
outperforms parameter reconstruction from conventional high-level variables
while at the same time providing reliable uncertainty estimates. Moreover, the
neural network can quickly be re-trained for different detector properties,
making it an ideal framework for optimizing experimental design.
