Isolated neutron stars as Science Validation for XMM2ATHENA: Ensuring robust data for future X-ray Astronomy

TL;DR

This paper explores isolated neutron stars as a means to validate X-ray astronomy instruments, using data from XMM-Newton and machine learning to improve candidate identification and characterization.

Contribution

It introduces a refined method combining catalog data, multiwavelength analysis, and machine learning for better identification of XINS candidates for future X-ray missions.

Findings

Enhanced characterization of XINS candidates.

Improved candidate selection techniques.

Foundation for future X-ray survey investigations.

Abstract

The discovery of radio-quiet, X-ray thermally emitting isolated neutron stars (XINSs) in the ROSAT All-Sky Survey revealed a previously overlooked component of the neutron star population. Advancements in X-ray instrumentation and the availability of deep, wide-area optical surveys now enable us to explore XINSs at fainter X-ray fluxes and greater distances. In this study, we investigated candidates selected from the 4XMM-DR9 catalogue using XMM-Newton, focusing on long-term flux stability, spectral characterisation, and astrometry. By leveraging resources from the XMM2ATHENA project -- including updated catalogues, multiwavelength characterisation and machine learning classification -- we refined our understanding of this sample of soft X-ray emitters. Our findings enhance the characterisation of XINS candidates, laying the groundwork for more targeted investigations and future catalogue searches.