HERA bound on x-ray luminosity when accounting for population III stars
Bibliographic record
Abstract
Recent upper bounds from the Hydrogen Epoch of Reionization Array on the cosmological 21-cm power spectrum at redshifts $z\ensuremath{\approx}8$, 10, have been used to constrain ${L}_{\mathrm{X}<2\text{ }\text{ }\mathrm{keV}}/\mathrm{SFR}$, the soft-band x-ray luminosity measured per unit star formation rate (SFR), strongly disfavoring values lower than $\ensuremath{\approx}{10}^{39.5}\text{ }\text{ }\mathrm{erg}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{M}_{\ensuremath{\bigodot}}^{\ensuremath{-}1}\text{ }\text{ }\mathrm{yr}$. This conclusion is derived from seminumerical models of the 21-cm signal, specifically focusing on contributions from atomic cooling galaxies that host population II stars. In this work, we first reproduce the bounds on ${L}_{\mathrm{X}<2\text{ }\text{ }\mathrm{keV}}/\mathrm{SFR}$ and other parameters using a pipeline that combines machine learning emulators for the power spectra and the intergalactic medium characteristics, together with a standard Markov chain Monte Carlo parameter fit. We then use this approach when including molecular cooling galaxies that host population III stars in the cosmic dawn 21-cm signal, and show that lower values of ${L}_{\mathrm{X}<2\text{ }\text{ }\mathrm{keV}}/\mathrm{SFR}$ are hence no longer strongly disfavored. The revised Hydrogen Epoch of Reionization Array bound does not require high-redshift x-ray sources to be significantly more luminous than high-mass x-ray binaries observed at low redshift.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".