Fast Bayesian Inference for Neutrino Non-Standard Interactions at Dark Matter Direct Detection Experiments
This work addresses the problem of slow inference in astroparticle physics for researchers, enabling faster parameter scans and model comparisons, though it is incremental as it adapts existing computational innovations to a specific domain.
The paper tackled the challenge of expensive Bayesian inference in multi-dimensional parameter spaces for physics beyond the Standard Model, such as neutrino non-standard interactions in dark matter direct detection experiments, by applying GPU acceleration, automatic differentiation, and neural-network-guided reparameterization, resulting in performance accelerations of ~100x for nested sampling and ~60x for Hamiltonian Monte Carlo compared to traditional methods.
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.