A flexible event reconstruction based on machine learning and likelihood principles

TL;DR

This paper introduces a neural network-based approach to approximate complex likelihood functions in particle physics event reconstruction, enabling fast, flexible, and near-optimal parameter inference and detector design optimization.

Contribution

It presents a novel method to decompose the likelihood into smaller terms and train neural networks to approximate these terms from simulations, improving efficiency and flexibility.

Findings

Achieves fast and accurate likelihood approximation

Enables joint parameter inference and detector optimization

Demonstrates effectiveness on realistic neutrino detector simulations

Abstract

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding likelihood function is often intractable, and approximations need to be constructed. In our work, we first show how the full likelihood for a many-sensor detector can be broken apart into smaller terms, and secondly how we can train neural networks to approximate all terms solely based on forward simulation. Our technique results in a fast, flexible, and close-to-optimal surrogate model proportional to the likelihood and can be used in conjunction with standard inference techniques allowing for a consistent treatment of uncertainties. We illustrate our technique for parameter inference in neutrino telescopes based on maximum likelihood and Bayesian posterior sampling. Given its great flexibility, we also showcase our method for geometry optimization enabling to learn optimal detector designs. Lastly, we apply our method to realistic simulation of a ton-scale water-based liquid scintillator detector.