Photometric Objects Around Cosmic Webs (PAC) Delineated in a Spectroscopic Survey. III. Accurate Measurement of Galaxy Stellar Mass Function with the Aid of Cosmological Redshift Surveys
Bibliographic record
Abstract
Abstract We present a novel method to accurately measure the galaxy stellar mass function (GSMF) based upon the Photometric objects Around Cosmic webs (PAC) method developed in our first paper (Paper I) of the series. The method allows us to measure the GSMF to a lower-mass end that is not accessible to the spectroscopic sample used in the PAC. Compared with Paper I, the current measurement of GSMF is direct and model independent. We measure the GSMFs in the redshift ranges of z s 3 3 Throughout the paper, we use z s for spectroscopic redshift, z for the z-band magnitude. < 0.2, 0.2 < z s < 0.4, and 0.5 < z s < 0.7 down to stellar masses of M * = 108.2, 1010.6, and 1010.6 M ⊙, using the data from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys and the spectroscopic samples of Slogan Digital Sky Survey (i.e., Main, LOWZ, and CMASS samples). Our results show that there is no evolution of GSMF from z s = 0.6 to z s = 0.1 for M * > 1010.6 M ⊙, and that there is a clear upturn at M * ≈ 109.5 M ⊙ toward smaller galaxies in the local GMSF at z s = 0.1. We provide an accurate double Schechter fit to the local GSMF for the entire range of M * and a table of our measurements at the three redshifts, which can be used to test theories of galaxy formation. Our method can achieve an accurate measurement of GSMF to the stellar mass limit where the spectroscopic sample is already highly incomplete (e.g., ∼ 10−3) for its target selection.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".