Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics
CoRR(2024)
摘要
Multi-dimensional parameter spaces are commonly encountered in astroparticle
physics theories that attempt to capture novel phenomena. However, they often
possess complicated posterior geometries that are expensive to traverse using
techniques traditional to this community. Effectively sampling these spaces is
crucial to bridge the gap between experiment and theory. 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 astroparticle physics
experimental results in the context of novel neutrino physics and benchmark
their performances against traditional nested sampling techniques. 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要