Afterglows, Redshifts, and Properties of<i>Swift</i>Gamma‐Ray Bursts
Bibliographic record
Abstract
We present optical, near-IR, and radio follow-up of 16 Swift bursts, including our discovery of nine afterglows and a redshift determination for three. These observations, supplemented by data from the literature, provide an afterglow recovery rate of 52% in the optical/near-IR, much higher than in previous missions (BeppoSAX, HETE-2, INTEGRAL, and IPN). The optical/near-IR afterglows of Swift events are on average 1.8 mag fainter at t 12 hr than those of previous missions. The X-ray afterglows are similarly fainter than those of pre-Swift bursts. In the radio the limiting factor is the VLA threshold, and the detection rate for Swift bursts is similar to that for past missions. The redshift distribution of pre-Swift bursts peaked at z $ 1, whereas the six Swift bursts with measured redshifts are distributed evenly between 0.7 and 3.2. From these results we conclude that (1) the pre-Swift distributions were biased in favor of bright events and low-redshift events, (2) the higher sensitivity and accurate positions of Swift result in a better representation of the true burst redshift and brightness distributions (which are higher and dimmer, respectively), and (3) $10% of the bursts are optically dark, as a result of a high redshift and/or dust extinction. We remark that the apparent lack of low-redshift, low-luminosity Swift bursts and the lower event rate than prelaunch estimates (90 vs. 150 per year) are the result of a threshold that is similar to that of BATSE. In view of these inferences, afterglow observers may find it advisable to make significant changes in follow-up strategies of Swift events. The faintness of the afterglows means that large telescopes should be employed as soon as the burst is localized. Sensitive observations in RIz and near-IR bands will be needed to discriminate between a typical z $ 2 burst with modest extinction and a high-redshift event. Radio observations will be profitable for a small fraction ($10%) of events. Finally, we suggest that a search for bright host galaxies in untriggered BAT localizations may increase the chance of finding nearby lowluminosity GRBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".