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

Reconstructing axion-like particles from beam dumps with simulation-based inference