Speaker
Description
Axion-like particles (ALPs) arise in beyond the Standard Model theories with global symmetry breaking. Several experiments have been constructed and proposed to look for them at different energy scales. We focus here on beam-dump experiments looking for GeV scale ALPs with macroscopic decay lengths. In this work we show that using ML we can reconstruct the ALP properties (mass and lifetime) even from inaccurate detector observations. We use a simulation-based inference approach based on conditional invertible neural networks to reconstruct the posterior probability of the ALP parameters. This neural network outperforms parameter reconstruction from conventional high-level observables while at the same time providing reliable uncertainty estimates. Moreover, the neural network can be quickly trained for different detector properties, making it an ideal framework for optimizing experimental design.
