Neutron stars in the light of SKA: Data, statistics, and science

TL;DR

The paper discusses how the SKA will vastly improve neutron star data collection, enabling new scientific insights, and reviews current statistical methods used in neutron star research to prepare for future richer datasets.

Contribution

It provides a meta-analysis of existing statistical methods in neutron star research and discusses potential scientific advancements with SKA data.

Findings

Current statistical models are shaped by existing data limitations.

SKA will significantly increase neutron star detections and data quality.

Preparedness for richer datasets will enhance future scientific analyses.

Abstract

The Square Kilometre Array (SKA), when it becomes functional, is expected to enrich neutron star (NS) catalogues by at least an order of magnitude over their current state. This includes the discovery of new NS objects leading to better sampling of under-represented NS categories, precision measurements of intrinsic properties such as spin period and magnetic field, as also data on related phenomena such as microstructure, nulling, glitching, etc. This will present a unique opportunity to seek answers to interesting and fundamental questions about the extreme physics underlying these exotic objects in the universe. In this paper, we first present a meta-analysis (from a methodological viewpoint) of statistical analyses performed using existing NS data, with a two-fold goal: First, this should bring out how statistical models and methods are shaped and dictated by the science problem being addressed. Second, it is hoped that these analyses will provide useful starting points for deeper analyses involving richer data from SKA whenever it becomes available. We also describe a few other areas of NS science which we believe will benefit from SKA which are of interest to the Indian NS community.