The Northern Extragalactic WISE × Pan-STARRS (NEWS) catalogue
Bibliographic record
Abstract
This study involves two photometric catalogues, AllWISE and Pan-STARRS Data Release 1, which were cross-matched to identify extragalactic objects among the common sources of these catalogues. To separate galaxies and quasars from stars, we created a machine-learning model that is trained on photometric (in fact, colour-based) information from the optical and infrared wavelength ranges. The model is based on three important procedures: the construction of the autoencoder artificial neural network, separation of galaxies and quasars from stars with a support vector machine (SVM) classifier, and cleaning of the AllWISE × PS1 sample to remove sources with abnormal colour indices using a one-class SVM. As a training sample, we employed a set of spectroscopically confirmed sources from the Sloan Digital Sky Survey Data Release 14. Having applied the classification model to the data of crossing the AllWISE and Pan-STARRS DR1 samples, we created the Northern Extragalactic WISE × Pan-STARRS (NEWS) catalogue, containing 40 million extragalactic objects and covering 3/4 of celestial sphere up tog = 23m. Several independent classification quality tests, namely, the astrometric test along with others based on the use of data from spectroscopic surveys show similar results and indicate a high purity (∼98.0%) and completeness (> 98%) for the NEWS catalogue within the magnitude range of 19.0m < g < 22.5m. The classification quality still retains quite acceptable levels of 70% for purity and 97% for completeness for the brightest and faintest objects from this magnitude range. In addition, validation with external data sets has demonstrated the need for using only those sources in the NEWS catalogue that are outside the zone with the enhanced extinction. We show that the number of quasars from the NEWS catalogue identified inGaiaDR2 exceeds the number of quasars previously identified inGaiaDR2 with the use of the AllWISEAGN catalogue. These quasars may be used in future as an additional sample for testing and anchoring theGaiaCelestial Reference Frame.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.008 | 0.009 |
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".