The Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER). VI. The High-mass Stellar Initial Mass Function of M33
Bibliographic record
Abstract
Abstract We measure the high-mass stellar initial mass function (IMF) from resolved stars in M33 young stellar clusters. Leveraging the Hubble Space Telescope’s high resolving power, we fully model the IMF probabilistically. We first model the optical color–magnitude diagram of each cluster to constrain its power-law slope Γ, marginalized over other cluster parameters in the fit (e.g., cluster age, mass, and radius). We then probabilistically model the distribution of mass function (MF) slopes for a highly strict cluster sample of nine clusters more massive than log(Mass/M ⊙) = 3.6; above this mass, all clusters have well-populated main sequences of massive stars and should have accurate recovery of their MF slopes, based on extensive tests with artificial clusters. We find that the ensemble IMF is best described by a mean high-mass slope of Γ ¯ = 1.49 ± 0.18 , with an intrinsic scatter of σ Γ 2 = 0.02 0.00 + 0.16 , consistent with a universal IMF. We find no dependence of the IMF on environmental impacts such as the local star formation rate (SFR) or galactocentric radius within M33, which serves as a proxy for metallicity. This Γ ¯ measurement is consistent with similar measurements in M31, despite M33 having a much higher SFR intensity. While this measurement is formally consistent with the canonical Kroupa (Γ = 1.30) IMF, as well as the Salpeter (Γ = 1.35) value, it is the second Local Group cluster sample to show evidence for a somewhat steeper high-mass IMF slope. We explore the impacts a steeper IMF slope has on a number of astronomical subfields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 0.031 |
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".