The nature of very-faint X-ray binaries: near-infrared spectroscopy of 1RXH J173523.7−354013 reveals a giant companion
Bibliographic record
Abstract
ABSTRACT Very-faint X-ray binaries (VFXBs) are a subclass of black holes and neutron stars in binaries that appear to be accreting at a very low rate. In addition to providing interesting constraints on poorly understood forms of accretion, elucidating the nature of VFXBs is particularly interesting for binary evolution and population modelling. Through near-infrared (NIR) spectroscopy, we here investigate the nature of the bursting neutron star and VFXB 1RXH J173523.7−354013 (J1735), which persistently accretes at an X-ray luminosity of LX ∼ 1034–1035 ergs−1. Our analysis shows that the NIR emission is dominated by that of the companion star, which we find to be a late G or early K-type giant, making this the second neutron star identified as a VFXB found to have a giant companion. We discuss how several of the system properties are difficult to reconcile with a wind-fed symbiotic X-ray binary. We therefore also propose an alternative scenario wherein J1735 is a wide binary system (supported by the discovery of a 7.5 d modulation in the NIR light curves) with a quiescent luminosity of LX ∼ 1034–1035 ergs−1, in which the donor star is overflowing its Roche lobe. This raises the possibility that J1735 may, every century or more, exhibit very long and very bright outbursts during which it reaches accretion rates around the Eddington limit like the neutron star Z sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".