Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection Experiments

TL;DR

This paper demonstrates how recent computational innovations like GPU acceleration and neural networks significantly speed up Bayesian inference in complex, multi-dimensional physics models, specifically for neutrino interactions in dark matter detection.

Contribution

It introduces the application of advanced inference techniques to neutrino non-standard interactions, achieving substantial speed-ups over traditional methods and enabling the first comprehensive parameter space scan.

Findings

Inference speed increased by factors of ~100 and ~60 for nested sampling and Hamiltonian Monte Carlo.

These techniques improve model comparison by efficiently evaluating Bayesian evidence.

First simultaneous parameter space scan for neutrino non-standard interactions in direct detection experiments.

Abstract

Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to astroparticle physics. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to dark matter direct detection experiments in the context of non-standard neutrino interactions and benchmark their performances against traditional nested sampling techniques when conducting Bayesian inference. Compared to nested sampling alone, we find that these techniques increase performance for both nested sampling and Hamiltonian Monte Carlo, accelerating inference by factors of $\sim 100$ and $\sim 60$, respectively. As nested sampling also evaluates the Bayesian evidence, these advancements can be exploited to improve model comparison performance while retaining compatibility with existing implementations that are widely used in the natural sciences. Using these techniques, we perform the first scan in the neutrino non-standard interactions parameter space for direct detection experiments whereby all parameters are allowed to vary simultaneously. We expect that these advancements are broadly applicable to other areas of astroparticle physics featuring multi-dimensional parameter spaces.