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Description
Monolithic liquid scintillator detectors are searching for neutrinoless double beta decay, a theorized process that would confirm the Majorana nature of neutrinos. The production and propagation of photons in the detection medium depend on various optical properties, such as light yield, attenuation lengths and re-emission/absorption spectra. These must all be separately characterized, resulting in several independent optical parameters, some of which are highly correlated. This, in addition to the high dimensionality of the data, makes this an ideal problem for machine learning.
The Eos experiment is a tonne-scale optical detector operating at UC Berkeley. Commissioned in 2024, Eos serves as a testbed for next generation detector technologies for neutrino experiments. This work presents the use of surrogate models via simulation-based inference (SBI) to tune Eos optical parameters. Trained on simulations generated via the RATPAC2 framework, the model learns the conditional likelihoods of key detector observables conditioned on the optical parameters; light yield, scattering length, and absorption length. With the learned conditional likelihood, Bayesian sampling of the posterior allows us to perform inference on parameter values using extensive calibration data. In the future, we hope to apply this method to larger detectors such as SNO+.
