<i>K</i>-Corrections and Filter Transformations in the Ultraviolet, Optical, and Near-Infrared
Bibliographic record
Abstract
Template fits to observed galaxy fluxes allow calculation of K -corrections and conversions among observations of galaxies at various wavelengths. We present a method for creating model-based template sets given a set of heterogeneous photometric and spectroscopic galaxy data. Our technique, nonnegative matrix factorization, is akin to principal component analysis (PCA), except that it is constrained to produce nonnegative templates, it can use a basis set of models (rather than the delta-function basis of PCA), and it naturally handles uncertainties, missing data, and heterogeneous data (including broadband fluxes at various redshifts). The particular implementation we present here is suitable for ultraviolet, optical, and near-infrared observations in the redshift range 0 < z < 1.5. Since we base our templates on stellar population synthesis models, the results are interpretable in terms of approximate stellar masses and star formation histories. We present templates fitted with this method to data from Galaxy Evolution Explorer , Sloan Digital Sky Survey spectroscopy and photometry, the Two Micron All Sky Survey, the Deep Extragalactic Evolutionary Probe, and the Great Observatories Origins Deep Survey. In addition, we present software for using such data to estimate K -corrections.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".