A NuSTAR Census of the X-ray Binary Population of the M31 Disk

TL;DR

This study uses NuSTAR's hard X-ray observations to identify and classify X-ray binaries in M31, differentiating neutron stars from black holes and analyzing their luminosity functions.

Contribution

It provides the first NuSTAR-based census of M31's X-ray binary population, including classifications and luminosity functions for the first time.

Findings

Identified 14 XRB candidates in M31's disk.

Classified 3 black holes and 6 neutron stars among the sources.

Luminosity functions follow power-law distributions consistent with Milky Way models.

Abstract

Using hard (E>10 keV) X-ray observations with NuSTAR, we are able to differentiate between accretion states, and thus compact object types, of neutron stars and black holes in X-ray binaries (XRBs) in M31, our nearest Milky Way-type neighbor. Using ten moderate-depth (20-50 ks) observations of the disk of M31 covering a total of ~0.45 deg$^{2}$, we detect 20 sources at 2$\sigma$ in the 4-25 keV band pass, 14 of which we consider to be XRB candidates. This complements an existing deeper (100-400 ks) survey covering ~0.2 deg$^{2}$ of the bulge and the northeastern disk. We make tentative classifications of 9 of these sources with the use of diagnostic color-intensity and color-color diagrams, which separate sources into various neutron star and black hole regimes, identifying 3 black holes and 6 neutron stars. In addition, we create X-ray luminosity functions for both the full (4-25 keV) and hard (12-25 keV) band, as well as sub-populations of the full band based on compact object type and association with globular clusters. Our best fit globular cluster XLF is shallower than the field XLF, and preliminary BH and NS XLFs suggest a difference in shape based on compact object type. We find that the cumulative disk XLFs in the full and hard band are best fit by power laws with indices of 1.32 and 1.28 respectively. This is consistent with models of the Milky Way XLF from Grimm et al. (2002), Voss & Ajello (2010), and Doroshenko et al. (2014).