Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics

Multi-dimensional parameter spaces are commonly encountered when searching for physics beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using traditional techniques. 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 results from liquid noble element detectors to search for neutrino non-standard interactions and benchmark their performances against traditional nested sampling. 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 ∼100 and ∼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.