Early structure formation from cosmic string loops in light of early JWST observations
Bibliographic record
Abstract
Cosmic strings, if they exist, source nonlinear and non-Gaussian perturbations all the way back to the time of equal matter and radiation (and earlier). Here, we compute the mass function of halos seeded by a scaling distribution of cosmic string loops, and we compare the results with the predictions of the standard Gaussian $\mathrm{\ensuremath{\Lambda}}$ cold dark matter model. Assuming a simple linear relation between stellar mass and halo mass, we also compute the stellar mass function. The contribution of cosmic strings dominates at sufficiently high redshifts $z>{z}_{c}$ where ${z}_{c}$ depends on the mass of the halo and on the mass per unit length $\ensuremath{\mu}$ of the strings and is of the order ${z}_{c}\ensuremath{\sim}12$ for $G\ensuremath{\mu}={10}^{\ensuremath{-}8}$. We find that strings with this value of $G\ensuremath{\mu}$ can explain the preliminary James Webb Space Telescope (JWST) data on the high-redshift stellar mass density. Based on an extreme value statistic, we find that the mass of the heaviest expected string-seeded galaxy for the current JWST sky coverage is compatible with the heaviest detected galaxy. Given the uncertainties in the interpretation of the JWST data, we discuss predictions for higher redshift observations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".