ALMA-IMF
Bibliographic record
Abstract
The stellar initial mass function (IMF) is critical to our understanding of star formation and the effects of young stars on their environment. On large scales, it enables us to use tracers such as UV or Hα emission to estimate the star formation rate of a system and interpret unresolved star clusters across the Universe. So far, there is little firm evidence of large-scale variations of the IMF, which is thus generally considered “universal”. Stars form from cores, and it is now possible to estimate core masses and compare the core mass function (CMF) with the IMF, which it presumably produces. The goal of the ALMA-IMF large programme is to measure the core mass function at high linear resolution (2700 au) in 15 typical Milky Way protoclusters spanning a mass range of 2.5 × 103 to 32.7 × 103 M⊙. In this work, we used two different core extraction algorithms to extract ≈680 gravitationally bound cores from these 15 protoclusters. We adopted a per core temperature using the temperature estimate from the point-process mapping Bayesian method (PPMAP). A power-law fit to the CMF of the sub-sample of cores above the 1.64 M⊙ completeness limit (330 cores) through the maximum likelihood estimate technique yields a slope of 1.97 ± 0.06, which is significantly flatter than the 2.35 Salpeter slope. Assuming a self-similar mapping between the CMF and the IMF, this result implies that these 15 high-mass protoclusters will generate atypical IMFs. This sample currently is the largest sample that was produced and analysed self-consistently, derived at matched physical resolution, with per core temperature estimates, and cores as massive as 150 M⊙. We provide both the raw source extraction catalogues and the catalogues listing the source size, temperature, mass, spectral indices, and so on in the 15 protoclusters.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.069 |
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".