Far-infrared observations of an unbiased sample of gamma-ray burst host galaxies
Bibliographic record
Abstract
Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe; believed to result from the collapse and subsequent explosion of massive stars. Even though it has profound consequences for our understanding of their nature and selection biases, little is known about the dust properties of the galaxies hosting GRBs. We present analysis of the far-infrared properties of an unbiased sample of 20 BeppoSAX and Swift GRB host galaxies (at an average redshift of z = 3.1) located in the Herschel Astrophysical Terahertz Large Area Survey, the Herschel Virgo Cluster Survey, the Herschel Fornax Cluster Survey, the Herschel Stripe 82 Survey and the Herschel Multi-tiered Extragalactic Survey, totalling 880 deg2, or ∼3 per cent of the sky in total. Our sample selection is serendipitous, based only on whether the X-ray position of a GRB lies within a large-scale Herschel survey – therefore our sample can be considered completely unbiased. Using deep data at wavelengths of 100–500 μm, we tentatively detected 1 out of 20 GRB hosts located in these fields. We constrain their dust masses and star formation rates (SFRs), and discuss these in the context of recent measurements of submillimetre galaxies and ultraluminous infrared galaxies. The average far-infrared flux of our sample gives an upper limit on SFR of <114 M⊙ yr−1. The detection rate of GRB hosts is consistent with that predicted assuming that GRBs trace the cosmic SFR density in an unbiased way, i.e. that the fraction of GRB hosts with SFR > 500 M⊙ yr−1 is consistent with the contribution of such luminous galaxies to the cosmic star formation density.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".