The 2.4 μm Galaxy Luminosity Function as Measured Using WISE. III. Measurement Results
Bibliographic record
Abstract
Abstract The Widefield Infrared Survey Explorer (WISE) satellite surveyed the entire sky multiple times in four infrared wavelengths (3.4, 4.6, 12, and 22 μm). The unprecedented combination of coverage area and depth gives us the opportunity to measure the luminosity function (LF) of galaxies, one of the fundamental quantities in the study of them, at 2.4 μm to an unparalleled level of formal statistical accuracy in the near-infrared. The big advantage of measuring LFs at wavelengths in the window of ≈2–3.5 μm is that it correlates more closely to the total stellar mass in galaxies than others. In this paper we report on the parameters of the 2.4 μm LF of galaxies obtained from applying the spectro-luminosity-functional-based methods to datasets from the previous papers in this series using the mean and covariance of 2.4 μm normalized spectral energy distributions (SEDs) from our previous work. In terms of the single Schechter function parameters evaluated at the present epoch, the combined result is: , (M ⋆ = −21.67 ± [0.02stat, 0.05sys] AB mag), and α = −1.050 ± [0.004stat, 0.04sys]. The high statistical accuracy comes from combining public redshift surveys with the wide coverage from WISE, and the unevenness in statistical accuracy is a result of our efforts to work around biases of uncertain origin that affect resolved and marginally resolved galaxies. With further refinements, the techniques applied in this work promise to advance the study of the SED of the universe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".