The Astrophysical Journal (Jan 2024)

An Empirical Framework Characterizing the Metallicity and Star-formation History Dependence of X-Ray Binary Population Formation and Emission in Galaxies

  • Bret D. Lehmer,
  • Erik B. Monson,
  • Rafael T. Eufrasio,
  • Amirnezam Amiri,
  • Keith Doore,
  • Antara Basu-Zych,
  • Kristen Garofali,
  • Lidia Oskinova,
  • Jeff J. Andrews,
  • Vallia Antoniou,
  • Robel Geda,
  • Jenny E. Greene,
  • Konstantinos Kovlakas,
  • Margaret Lazzarini,
  • Chris T. Richardson

DOI
https://doi.org/10.3847/1538-4357/ad8de7
Journal volume & issue
Vol. 977, no. 2
p. 189

Abstract

Read online

We present a new empirical framework modeling the metallicity and star formation history (SFH) dependence of X-ray luminous ( L ≳ 10 ^36 erg s ^−1 ) point-source population X-ray luminosity functions (XLFs) in normal galaxies. We expect that the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near the low luminosity end of our observations. Our framework is calibrated using the collective statistical power of 3731 X-ray detected point sources within 88 Chandra-observed galaxies at D ≲ 40 Mpc that span broad ranges of metallicity ( Z ≈ 0.03–2 Z _⊙ ), SFH, and morphology (dwarf irregulars, late types, and early types). Our best-fitting models indicate that the XLF normalization per unit stellar mass declines by ≈2–3 dex from 10 Myr to 10 Gyr, with a slower age decline for low-metallicity populations. The shape of the XLF for luminous X-ray sources ( L ≳ 10 ^38 erg s ^−1 ) significantly steepens with increasing age and metallicity, while the lower-luminosity XLF appears to flatten with increasing age. Integration of our models provides predictions for X-ray scaling relations that agree very well with past results presented in the literature, including, e.g., the L _X –SFR– Z relation for high-mass XRBs in young stellar populations as well as the L _X / M _⋆ ratio observed in early-type galaxies that harbor old populations of low-mass XRBs. The model framework and data sets presented in this paper further provide unique benchmarks that can be used for calibrating binary population synthesis models.

Keywords