Fires in the deep
Bibliographic record
Abstract
Context.Gamma-ray bursts (GRBs) are ideal probes of the Universe at high redshift (z), pinpointing the locations of the earliest star-forming galaxies and providing bright backlights with simple featureless power-law spectra that can be used to spectrally fingerprint the intergalactic medium and host galaxy during the period of reionization.Future missions such as Gamow Explorer (hereafter Gamow) are being proposed to unlock this potential by increasing the rate of identification of high-z (z > 5) GRBs in order to rapidly trigger observations from 6 -10 m ground telescopes, the James Webb Space Telescope (JWST), and the upcoming Extremely Large Telescopes (ELTs).Aims.Gamow was proposed to the NASA 2021 Medium-Class Explorer (MIDEX) program as a fast-slewing satellite featuring a wide-field lobster-eye X-ray telescope (LEXT) to detect and localize GRBs with arcminute accuracy, and a narrow-field multi-channel photo-z infrared telescope (PIRT) to measure their photometric redshifts for > 80% of the LEXT detections using the Lyman- dropout technique.We use a large sample of observed GRB afterglows to derive the PIRT sensitivity requirement.Methods.We compiled a complete sample of GRB optical-near-infrared (optical-NIR) afterglows from 2008 to 2021, adding a total of 66 new afterglows to our earlier sample, including all known high-z GRB afterglows.This sample is expanded with over 2837 unpublished data points for 40 of these GRBs.We performed full light-curve and spectral-energy-distribution analyses of these afterglows to derive their true luminosity at very early times.We compared the high-z sample to the comparison sample at lower redshifts.For all the light curves, where possible, we determined the brightness at the time of the initial finding chart of Gamow, at different high redshifts and in different NIR bands.This was validated using a theoretical approach to predicting the afterglow brightness.We then followed the evolution of the luminosity to predict requirements for groundand space-based follow-up.Finally, we discuss the potential biases between known GRB afterglow samples and those to be detected by Gamow.Results.We find that the luminosity distribution of high-z GRB afterglows is comparable to those at lower redshift, and we therefore are able to use the afterglows of lower-z GRBs as proxies for those at high z.We find that a PIRT sensitivity of 15 Jy (21 mag AB) in a 500 s exposure simultaneously in five NIR bands within 1000s of the GRB trigger will meet the Gamow mission requirements.Depending on the z and NIR band, we find that between 75% and 85% of all afterglows at z > 5 will be recovered by Gamow at 5 detection significance, allowing the determination of a robust photo-z.As a check for possible observational biases and selection effects, we compared the results with those obtained through population-synthesis models, and find them to be consistent.Conclusions.Gamow and other high-z GRB missions will be capable of using a relatively modest 0.3m onboard NIR photo-z telescope to rapidly identify and report high-z GRBs for further follow-up by larger facilities, opening a new window onto the era of reionization and the high-redshift Universe.
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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".