SuperNest: accelerated nested sampling applied to astrophysics and cosmology

TL;DR

This paper introduces SuperNest, an enhanced nested sampling method that improves efficiency and accuracy in astrophysics and cosmology by using posterior repartitioning with a suitable proposal distribution, demonstrated on a Gaussian posterior example.

Contribution

It presents a novel approach to accelerate nested sampling through posterior repartitioning and provides an open-source Python implementation.

Findings

Reduced computational time in nested sampling

Improved accuracy of posterior estimation

Open-source Python package available

Abstract

We present a method for improving the performance of nested sampling as well as its accuracy. Building on previous work by Chen et al., we show that posterior repartitioning may be used to reduce the amount of time nested sampling spends in compressing from prior to posterior if a suitable ``proposal'' distribution is supplied. We showcase this on a cosmological example with a Gaussian posterior, and release the code as an LGPL licensed, extensible Python package https://gitlab.com/a-p-petrosyan/sspr.