Issue |
A&A
Volume 649, May 2021
|
|
---|---|---|
Article Number | L2 | |
Number of page(s) | 6 | |
Section | Letters to the Editor | |
DOI | https://doi.org/10.1051/0004-6361/202140792 | |
Published online | 30 April 2021 |
Letter to the Editor
Population synthesis on ultra-luminous X-ray sources with an accreting neutron star: Wind Roche-lobe overflow cases
School of Physics, Xi’an Jiaotong University, Xi’an 710049, PR China
e-mail: [email protected]
Received:
12
March
2021
Accepted:
16
April
2021
Very recently, wind Roche-lobe overflow (WRLOF) has been suggested as a possible mass transfer mechanism for ultra-luminous X-ray sources (ULXs) and, to date, two neutron-star (NS) ULXs (i.e., NGC 7793 P13 and NGC 300 ULX-1) are remarkable and hard to understand in the current, usual RLOF picture. In this work, we test if the two sources could fit into the WRLOF paradigm. By using an evolutionary population synthesis method, we modeled the population of NS ULXs with (super)giant donors, taking the WRLOF accretion mode into account. We find that the population of wind-fed NS ULXs in the WRLOF mode is distinct in numbers and binary parameters from that in the traditional Bondi-Hoyle-Lyttleton mode, and it is strongly metallicity dependent. The number of NS ULXs with (super)giant donors can be enhanced greatly, by one or two orders of magnitude, depending on the metallicity adopted. Sources with massive (∼15−40 M⊙) (super)giant donors dominate wind-fed NS ULXs in the very low metallicities, while sources in near solar cases are dominated by a red supergiant with a lower mass M2 < 10 M⊙ instead. Moreover, the two NS ULXs can be well reproduced in the WRLOF paradigm, which significantly enriches our understanding of the nature of ULXs and the population. We also present the current distributions of binary parameters of wind-fed NS ULXs, which may be further testified by future high-resolution optical and X-ray observations of these populations.
Key words: stars: evolution / X-rays: binaries / stars: neutron / methods: statistical / binaries: close
© ESO 2021
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