The Chandra High-resolution X-Ray Spectrum of Quiescent Emission from Sgr A*
Bibliographic record
Abstract
Abstract In quiescence, Sgr A* is surprisingly dim, shining 100,000 times less than expected for its environment. This problem has motivated a host of theoretical models to explain radiatively inefficient accretion flows. The Chandra Galactic Center X-ray Visionary Program obtained approximately 3 Ms (1 month) of Chandra high-energy transmission grating (HETG) data, offering the only opportunity to examine the quiescent X-ray emission of Sgr A* with high-resolution spectroscopy. Utilizing custom background regions and filters for removing overlapping point sources, this work provides the first-ever look at stacked HETG spectra of Sgr A*. We model the background data sets with a cubic spline and fit the unbinned Sgr A* spectra with a simple parametric model of a power law plus Gaussian lines under the effects of interstellar extinction. We detect a strong 6.7 keV iron emission line in the HEG spectra and a 3.1 keV emission line in the MEG spectra. In all cases, the line centroids and equivalent widths are consistent with those measured from low-resolution CCD spectra. An examination of the unbinned, stacked HEG ± 1 spectrum reveals fine structure in the iron line complex. In addition to resolving the resonant and forbidden lines from He-like iron, there are apparent emission features arising with higher statistical significance at lower energy, potentially associated with Fe xx–xxiv ions in a ∼1 keV plasma arising near the Bondi radius of Sgr A*. With this work, we release the cleaned and stacked Sgr A* and background HETG spectra to the public as a special legacy data set.
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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) | 0.002 | 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".