Anthropic origin of the neutrino mass from cooling failure
Bibliographic record
Abstract
The sum of active neutrino masses is well constrained, $58\text{ }\text{ }\mathrm{meV}\ensuremath{\le}{m}_{\ensuremath{\nu}}\ensuremath{\lesssim}0.23\text{ }\text{ }\mathrm{eV}$, but the origin of this scale is not well understood. Here we investigate the possibility that it arises by environmental selection in a large landscape of vacua. Earlier work noted the detrimental effects of neutrinos on large-scale structure. However, using Boltzmann codes to compute the smoothed density contrast on Mpc scales, we find that dark matter halos form abundantly for ${m}_{\ensuremath{\nu}}\ensuremath{\gtrsim}10\text{ }\text{ }\mathrm{eV}$. This finding rules out an anthropic origin of ${m}_{\ensuremath{\nu}}$, unless a different catastrophic boundary can be identified. Here we argue that galaxy formation becomes inefficient for ${m}_{\ensuremath{\nu}}\ensuremath{\gtrsim}10\text{ }\text{ }\mathrm{eV}$. We show that in this regime, structure forms late and is dominated by cluster scales, as in a top-down scenario. This is catastrophic: baryonic gas will cool too slowly to form stars in an abundance comparable to our Universe. With this novel cooling boundary, we find that the anthropic prediction for ${m}_{\ensuremath{\nu}}$ agrees at better than $2\ensuremath{\sigma}$ with current observational bounds. A degenerate hierarchy is mildly preferred.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".