Stellar Initial Mass Function (IMF) Probed with Supernova Rates and Neutrino Background: Cosmic-average IMF Slope Is ≃2–3 Similar to the Salpeter IMF
Bibliographic record
Abstract
Abstract The stellar initial mass function (IMF) is expressed by ϕ(m) ∝ m − α with the slope α, and known as a poorly constrained but very important function in studies of star and galaxy formation. There are no sensible observational constraints on the IMF slopes beyond the Milky Way and nearby galaxies. Here we combine two sets of observational results, (1) cosmic densities of core-collapse supernova (CCSN) explosion rates and (2) cosmic far-UV radiation (and infrared reradiation) densities, which are sensitive to massive (≃8–50 M ⊙) and moderately massive (≃2.5–7 M ⊙) stars, respectively, and constrain the IMF slope at m > 1 M ⊙ with a freedom of redshift evolution. Although no redshift evolution is identified beyond the uncertainties, we find that the cosmic-average IMF slope at z = 0 is α = 1.8–3.2 at the 95% confidence level that is comparable with the Salpeter IMF, α = 2.35, which marks the first constraint on the cosmic-average IMF. We show a forecast for the Nancy Grace Roman Space Telescope supernova survey that will provide significantly strong constraints on the IMF slope with δ α ≃ 0.5 over z = 0–2. Moreover, as for an independent IMF probe instead of (1), we suggest to use diffuse supernovae neutrino background (DSNB), relic neutrinos from CCSNe. We expect that the Hyper-Kamiokande neutrino observations over 20 yr will improve the constraints on the IMF slope and the redshift evolution significantly better than those obtained today, if the systematic uncertainties of DSNB production physics are reduced in the future numerical simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".