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 $\sim 100$ 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.

应用 GPU 加速、自动差分和神经网络引导重新参数化等新技术，我们研究了多维参数空间中的天体粒子物理实验结果，并将其性能与传统嵌套采样技术进行了对比，发现这些技术可以加速推理过程，提高模型比较性能。