Survey of gravitationally-lensed objects in HSC imaging (SuGOHI). III. Statistical strong lensing constraints on the stellar IMF of CMASS galaxies
Bibliographic record
Abstract
Context: The determination of the stellar initial mass function (IMF) of massive galaxies is one of the open problems in cosmology. Strong gravitational lensing is one of the few methods that allow us to constrain the IMF outside of the Local Group. Aims: The goal of this study is to statistically constrain the distribution in the IMF mismatch parameter, defined as the ratio between the true stellar mass of a galaxy and that inferred assuming a reference IMF, of massive galaxies from the BOSS CMASS sample. Methods: We take 23 strong lenses drawn from the CMASS sample, measure their Einstein radii and stellar masses using multi-band photometry from the Hyper Suprime-Cam survey, then fit a model distribution for the IMF mismatch parameter and dark matter halo mass to the whole sample. We use a prior on halo mass from weak lensing measurements and account for strong lensing selection effects in our model. Results: Assuming an NFW density profile for the dark matter distribution, we infer a value $μ_{\mathrm{IMF}} = -0.04\pm0.11$ for the average base-10 logarithm of the IMF mismatch parameter, defined with respect to a Chabrier IMF. A Salpeter IMF is in tension with our measurements. Conclusions: Our results are consistent with a scenario in which the region of massive galaxies where the IMF normalization is significantly heavier than that of the Milky Way is much smaller than the scales $5\sim10$~kpc probed by the Einstein radius of the lenses in our sample, as recent spatially resolved studies of the IMF in massive galaxies suggest. The MCMC chains describing the posterior probability distribution of the model are available online, together with the code used to obtain them.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".