The SWELLS survey - III. Disfavouring ‘heavy’ initial mass functions for spiral lens galaxies
Bibliographic record
Abstract
We present gravitational lens models for 20 strong gravitational lens systems observed as part of the Sloan WFC Edge-on Late-type Lens Survey (SWELLS) project. 15 of the lenses are taken from Paper I, while five are newly discovered systems. The systems are galaxy–galaxy lenses where the foreground deflector has an inclined disc, with a wide range of morphological types, from late-type spiral to lenticular. For each system, we compare the total mass inside the critical curve inferred from gravitational lens modelling to the stellar mass inferred from stellar population synthesis (SPS) models, computing the stellar mass fraction f*≡MSPS/Mlens. We find that, for the lower mass SWELLS systems, adoption of a Salpeter stellar initial mass function (IMF) leads to estimates of f* that exceed 1. This is unphysical and provides strong evidence against the Salpeter IMF being valid for these systems. Taking the lower mass end of the SWELLS sample (σSIE < 230 km s−1), we find that the IMF is lighter (in terms of stellar mass-to-light ratio) than Salpeter with 98 per cent probability, and consistent with the Chabrier IMF and IMFs between the two. This result is consistent with previous studies of spiral galaxies based on independent techniques. In combination with recent studies of massive early-type galaxies that have favoured a heavier Salpeter-like IMF, this result strengthens the evidence against a universal stellar IMF.
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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) | 0.000 | 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) | 0.001 | 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".