Fast Bayesian inference for neutrino non-standard interactions at dark matter direct detection experiments

2024-05-23

Machine Learning: Science and Technology

AI Summary

In direct dark matter detection, neutrino non-standard interactions (NSI) induce high-dimensional, complex posterior geometries, rendering conventional Bayesian inference methods—such as nested sampling and Hamiltonian Monte Carlo—computationally prohibitive and inefficient. Method: We present the first full-degree-of-freedom joint Bayesian inference over all NSI parameters, integrating GPU acceleration, automatic differentiation, and a neural-network-guided reparameterization strategy to overcome geometric pathologies and enhance sampling efficiency. Contribution/Results: Our approach achieves ∼100× and ∼60× speedups over nested sampling and HMC, respectively, while preserving accurate Bayesian evidence estimation. It enables, for the first time, simultaneous scanning of the complete NSI parameter space within a statistically rigorous framework. This establishes the first efficient, scalable, high-dimensional inference pipeline for joint NSI–dark matter analyses, significantly advancing statistical methodology in particle astrophysics.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

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.

Problem

• Enhance Bayesian inference for neutrino interactions.
• Optimize multi-dimensional parameter space exploration.
• Accelerate computational methods in astroparticle physics.

Innovation

• GPU acceleration speeds computation
• Automatic differentiation enhances precision
• Neural-network-guided reparameterization simplifies analysis