Accretion properties and estimation of spin of galactic black hole candidate Swift J1728.9–3613with <i>NuSTAR</i> during its 2019 outburst
Bibliographic record
Abstract
ABSTRACT Black hole X-ray binaries (BHXRBs) play a crucial role in understanding the accretion of matter onto a black hole. Here, we focus on exploring the transient BHXRB Swift J1728.9–3613 discovered by Swift/BAT and MAXI/GSC during its January 2019 outburst. We present measurements on its accretion properties, long time-scale variability, and spin. To probe these properties, we make use of several NICER observations and an unexplored data set from NuSTAR, as well as long-term light curves from MAXI/GSC. In our timing analysis, we provide estimates of the cross-correlation functions between light curves in various energy bands. In our spectral analysis, we employ numerous phenomenological models to constrain the parameters of the system, including flavours of the relativistic reflection model Relxill to model the Fe Kα line and the >15 keV reflection hump. Our analysis reveals that: (i) Over the course of the outburst, the total energy released was ∼5.2 × 1044 ergs, corresponding to roughly 90 per cent of the mass of Mars being devoured. (ii) We find a continuum lag of 8.4 ± 1.9 d between light curves in the 2–4 and 10–20 keV bands, which could be related to the viscous inflow time-scale of matter in the standard disc. (iii) Spectral analysis reveals a spin parameter of ∼0.6–0.7 with an inclination angle of ∼45°–70° and an accretion rate during the NuSTAR observation of ${\sim}17\ \hbox{per cent}\ L_{\rm Edd}$.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".