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

Abstract

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.

34 pages, 18 figures, 2 columns, matches published version

References in corpus (13)

• Revised constraints and Belle II sensitivity for visible and invisible axion-like particles
• A taste of dark matter: Flavour constraints on pseudoscalar mediators
• A New Flavor of Searches for Axion-Like Particles
• Machine Learning and LHC Event Generation
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Invertible Networks or Partons to Detector and Back Again
• Generative Networks for Precision Enthusiasts
• The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• Charming ALPs
• Measuring QCD Splittings with Invertible Networks
• Inference of cosmic-ray source properties by conditional invertible neural networks
• Leading Logs in QCD Axion Effective Field Theory

Cited by in corpus (3)

• BitHEP -- The Limits of Low-Precision ML in HEP
• Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference
• Finding excesses in model parameter space