A deep survey of short GRB host galaxies over <i>z</i> ∼ 0–2: implications for offsets, redshifts, and environments
Bibliographic record
Abstract
ABSTRACT A significant fraction (30 per cent) of well-localized short gamma-ray bursts (sGRBs) lack a coincident host galaxy. This leads to two main scenarios: (i) that the progenitor system merged outside of the visible light of its host, or (ii) that the sGRB resided within a faint and distant galaxy that was not detected by follow-up observations. Discriminating between these scenarios has important implications for constraining the formation channels of neutron star mergers, the rate and environments of gravitational wave sources, and the production of heavy elements in the Universe. In this work, we present the results of our observing campaign targeted at 31 sGRBs that lack a putative host galaxy. Our study effectively doubles the sample of well-studied sGRB host galaxies, now totaling 72 events of which $28{{\ \rm per\ cent}}$ lack a coincident host to deep limits (r ≳ 26 or F110W ≳ 27 AB mag), and represents the largest homogeneously selected catalogue of sGRB offsets to date. We find that 70 per cent of sub-arcsecond localized sGRBs occur within 10 kpc of their host’s nucleus, with a median projected physical offset of 5.6 kpc. Using this larger population, we discover an apparent redshift evolution in their locations: bursts at low-z occur at 2 × larger offsets compared to those at z > 0.5. This evolution could be due to a physical evolution of the host galaxies themselves or a bias against faint high-z galaxies. Furthermore, we discover a sample of hostless sGRBs at z ≳ 1 that are indicative of a larger high-z population, constraining the redshift distribution and disfavoring lognormal delay time models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".