Catalog of Outbursts of Neutron Star Low-mass X-Ray Binaries
Bibliographic record
Abstract
Abstract Many X-ray binaries (XRBs) are transiently accreting. Having statistics on their recurrence times is helpful to address questions related to binary evolution and populations, as well as the physics of binary systems. We compile a catalog of known outbursts of 87 transient neutron stars (identified through bursts or pulsations) and low-mass XRBs until mid-2025. Most outbursts are taken from the literature, but we also identify some outbursts from public X-ray monitoring lightcurves. We find 109 outbursts not previously identified in the literature; most are from the frequent transients GRS 1747-312 and the Rapid Burster MXB 1730-335, though we suspect that two outbursts from Liller 1 may be from another transient besides the Rapid Burster. We also find new outbursts for 10 other systems, and verify substantial quiescent intervals for XMM J174457-2850.3, XMMU J174716.1-281048, and AX J1754.2-2754. Outburst detection has been relatively efficient since 1996 for outbursts above F X (2–10 keV) = 3 × 10 −10 erg cm −2 s −1 . While several systems have many known outbursts, 40 of the 87 systems we track have zero or one recorded outburst between 1996 and 2023. This suggests that many faint Galactic center XRBs may be neutron star XRBs, though we cannot completely rule out the proposition that most neutron star XRBs undergo frequent outbursts below all-sky monitor detection limits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".