Probing cosmic chemical enrichment with next-generation gravitational-wave observatories
Bibliographic record
Abstract
Abstract By observing binary black hole (BBH) mergers out to the edge of the Universe, next-generation (XG) ground-based gravitational-wave detectors like Cosmic Explorer and Einstein Telescope will map the BBH merger rate across all of cosmic history. This merger rate traces the formation rate of their progenitor stars convolved with a delay time distribution. Given theoretically-motivated priors on the delay time distribution, we show how XG observations can measure the BBH progenitor formation rate, probing the star formation rate (SFR) up to z > 15. However, the progenitor formation rate does not directly give a measurement of the SFR, but rather a combination of the SFR and its metallicity distribution as a function of redshift. Fortunately, the metallicity-dependence of BBH formation likely varies as a function of BBH mass and/or formation channel. We find that if different BBH subpopulations with distinct metallicity biases can be identified, comparing their rates as a function of redshift yields a simultaneous measurement of the SFR and its metallicity distribution. Given optimistic theoretical priors and one year of observation, this may provide a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>∼</mml:mo> <mml:mn>10</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:mrow> </mml:math> measurement of the SFR at its peak and a 0.2 dex (0.7 dex) measurement of the median metallicity out to z = 10 ( z = 15) at 90% credibility, although the uncertainties scale with theoretical uncertainties on BBH delay times and formation efficiencies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".