Observations of X-ray binaries indicate a dearth of compact objects in the mass range from ∼2 − 5 M ⊙ . The existence of this (first mass) gap has been used to discriminate between proposed engines behind core-collapse supernovae. From LIGO/Virgo observations of binary compact remnant masses, several candidate first mass gap objects, either neutron stars (NSs) or black holes (BHs), were identified during the O3 science run. Motivated by these new observations, we study the formation of BH-NS mergers in the framework of isolated classical binary evolution, using population synthesis methods to evolve large populations of binary stars (Population I and II) across cosmic time. We present results on the NS to BH mass ratios ( q = M NS / M BH ) in merging systems, showing that although systems with a mass ratio as low as q = 0.02 can exist, typically BH-NS systems form with moderate mass ratios q = 0.1 − 0.2. If we adopt a delayed supernova engine, we conclude that ∼30% of BH-NS mergers may host at least one compact object in the first mass gap (FMG). Even allowing for uncertainties in the processes behind compact object formation, we expect the fraction of BH-NS systems ejecting mass during the merger to be small (from ∼0.6 − 9%). In our reference model, we assume: (i) the formation of compact objects within the FMG, (ii) natal NS/BH kicks decreased by fallback, (iii) low BH spins due to Tayler-Spruit angular momentum transport in massive stars. We find that ≲1% of BH-NS mergers will have any mass ejection and about the same percentage will produce kilonova bright enough to have a chance of being detected with a large (Subaru-class) 8 m telescope. Interestingly, all these mergers will have both a BH and an NS in the FMG.
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A Comparative Study of Machine-learning Methods for X-Ray Binary Classification
X-ray binaries (XRBs) consist of a compact object that accretes material from an orbiting secondary star. The most secure method we have for determining if the compact object is a black hole is to determine its mass: This is limited to bright objects and requires substantial time-intensive spectroscopic monitoring. With new X-ray sources being discovered with different X-ray observatories, developing efficient, robust means to classify compact objects becomes increasingly important. We compare three machine-learning classification methods (Bayesian Gaussian Processes (BGPs), K-Nearest Neighbors (KNN), Support Vector Machines) for determining whether the compact objects are neutron stars or black holes (BHs) in XRB systems. Each machine-learning method uses spatial patterns that exist between systems of the same type in 3D color–color–intensity diagrams. We used lightcurves extracted using 6 yr of data with MAXI/GSC for 44 representative sources. We find that all three methods are highly accurate in distinguishing pulsing from nonpulsing neutron stars (NPNS) with 95% of NPNS and 100% of pulsars accurately predicted. All three methods have high accuracy in distinguishing BHs from pulsars (92%) but continue to confuse BHs with a subclass of NPNS, called bursters, with KNN doing the best at only 50% accuracy for predicting BHs. The precision of all three methods is high, providing equivalent results over 5–10 independent runs. In future work, we will suggest a fourth dimension be incorporated to mitigate the confusion of BHs with bursters. This work paves the way toward more robust methods to efficiently distinguish BHs, NPNS, and pulsars.
more » « less- NSF-PAR ID:
- 10368677
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 933
- Issue:
- 1
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 116
- Size(s):
- ["Article No. 116"]
- Sponsoring Org:
- National Science Foundation
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Abstract Mass measurements from low-mass black hole X-ray binaries (LMXBs) and radio pulsars have been used to identify a gap between the most massive neutron stars (NSs) and the least massive black holes (BHs). BH mass measurements in LMXBs are typically only possible for transient systems: outburst periods enable detection via all-sky X-ray monitors, while quiescent periods enable radial velocity measurements of the low-mass donor. We quantitatively study selection biases due to the requirement of transient behavior for BH mass measurements. Using rapid population synthesis simulations (
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