MEASURING THE CLUMP MASS FUNCTION IN THE AGE OF SCUBA2,<i>HERSCHEL</i>, AND ALMA
Bibliographic record
Abstract
We use simulated images of star-forming regions to explore the effects of various image acquisition techniques on the derived clump mass function. In particular, we focus on the effects of finite image angular resolution, the presence of noise, and spatial filtering. We find that, even when the image has been so heavily degraded with added noise and lowered angular resolution that the clumps it contains clearly no longer correspond to pre-stellar cores, still the clump mass function is typically consistent with the stellar initial mass function within their mutual uncertainties. We explain this result by suggesting that noise, source blending, and spatial filtering all randomly perturb the clump masses, biasing the mass function toward a lognormal form whose high-mass end mimics a Salpeter power law. We argue that this is a consequence of the central limit theorem and that it strongly limits our ability to accurately measure the true mass function of the clumps. We support this conclusion by showing that the characteristic mass scale of the clump mass function, represented by the "break mass," scales as a simple function of the angular resolution of the image from which the clump mass function is derived. This strongly constrains our ability to use the clump mass function to derive a star formation efficiency. We discuss the potential and limitations of the current and next generation of instruments for measuring the clump mass function.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".