The <i>Swift</i> Bulge Survey: optical and near-IR follow-up featuring a likely symbiotic X-ray binary and a focused wind CV
Bibliographic record
Abstract
ABSTRACT The nature of very faint X-ray transients (VFXTs) – transient X-ray sources that peak at luminosities $L_X\lesssim 10^{36} {\rm \, erg \, s^{-1}}$ – is poorly understood. The faint and often short-lived outbursts make characterizing VFXTs and their multiwavelength counterparts difficult. In 2017 April we initiated the Swift Bulge Survey, a shallow X-ray survey of ∼16 square degrees around the Galactic centre with the Neil Gehrels Swift Observatory. The survey has been designed to detect new and known VFXTs, with follow-up programmes arranged to study their multiwavelength counterparts. Here we detail the optical and near-infrared follow-up of four sources detected in the first year of the Swift Bulge Survey. The known neutron star binary IGR J17445-2747 has a K4III donor, indicating a potential symbiotic X-ray binary nature and the first such source to show X-ray bursts. We also find one nearby M-dwarf (1SXPS J174215.0-291453) and one system without a clear near-IR counterpart (Swift J175233.9-290952). Finally, 3XMM J174417.2-293944 has a subgiant donor, an 8.7 d orbital period, and a likely white dwarf accretor; we argue that this is the first detection of a white dwarf accreting from a gravitationally focused wind. A key finding of our follow-up campaign is that binaries containing (sub)giant stars may make a substantial contribution to the VFXT population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".