Chandra and Hubble Space Telescope observations of dark gamma-ray bursts and their host galaxies
Bibliographic record
Abstract
Abstract We present a study of 21 dark gamma-ray burst (GRB) host galaxies, predominantly using X-ray afterglows obtained with the Chandra X-Ray Observatory (CXO) to precisely locate the burst in deep Hubble Space Telescope (HST) imaging of the burst region. The host galaxies are well-detected in F160W in all but one case and in F606W imaging in 60 per cent of cases. We measure magnitudes and perform a morphological analysis of each galaxy. The asymmetry, concentration, and ellipticity of the dark burst hosts are compared against the host galaxies of optically bright GRBs. In agreement with other studies, we find that dark GRB hosts are redder and more luminous than the bulk of the GRB host population. The distribution of projected spatial offsets for dark GRBs from their host galaxy centroids is comparable to that of optically bright bursts. The dark GRB hosts are physically larger, more massive and redder, but are morphologically similar to the hosts of bright GRBs in terms of concentration and asymmetry. Our analysis constrains the fraction of high redshift (z > 5) GRBs in the sample to 14 per cent, implying an upper limit for the whole long-GRB population of ≤4.4 per cent. If dust is the primary cause of afterglow darkening amongst dark GRBs, the measured extinction may require a clumpy dust component in order to explain the observed offset and ellipticity distributions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".