https://doi.org/10.1140/epjc/s10052-024-12557-4
Regular Article - Theoretical Physics
Reconstructing axion-like particles from beam dumps with simulation-based inference
1
Institute for Astroparticle Physics (IAP), Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany
2
Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany
3
Institute for Theoretical Particle Physics (TTP), Karlsruhe Institute of Technology (KIT), 76131, Karlsruhe, Germany
a
alessandro.morandini@kit.edu
Received:
3
November
2023
Accepted:
14
February
2024
Published online:
27
February
2024
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.
© The Author(s) 2024
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Funded by SCOAP3.
