Optical characterization of WISE selected blazar candidates
Bibliographic record
Abstract
Context. Over the last decade more than five thousand γ-ray sources have been detected by the Large Area Telescope (LAT) onboard the Fermi Gamma-ray Space Telescope. Given the positional uncertainty of the telescope, nearly 30% of these sources remain without an obvious counterpart at lower energies. This has motivated the release of new catalogs of γ-ray counterpart candidates and several follow up campaigns in the last decade. Aims. Recently, two new catalogs of blazar candidates were released. These are the improved and expanded version of the WISE Blazar-Like Radio-Loud Sources (WIBRaLS2) catalog and the Kernel Density Estimation selected candidate BL Lacs (KDEBLLACS) catalog, both selecting blazar-like sources based on their infrared colors from the Wide-field Infrared Survey Explorer (WISE). In this work we characterize these two catalogs, clarifying the true nature of their sources based on their optical spectra from SDSS data release 15, thus testing their efficiency in selecting true blazars. Methods. We first selected all WIBRaLS2 and KDEBLLACS sources with available optical spectra in the footprint of Sloan Digital Sky Survey data release 15. We then analyzed these spectra to verify the nature of each selected candidate and to measure the fraction of the catalogs composed by spectroscopically confirmed blazars. Finally, we evaluated the impact of selection effects, especially those related to optical colors of WIBRaLS2/KDEBLLACS sources and their optical magnitude distributions. Results. We found that at least ∼30% of each catalog is made up of confirmed blazars, with quasars being the major contaminants in the case of WIBRaLS2 (≈58%) and normal galaxies in the case of KDEBLLACS (≈38.2%). The spectral analysis also allowed us to identify the nature of 11 blazar candidates of uncertain type (BCUs) from the Fermi-LAT fourth Point Source Catalog (4FGL) and to find 25 new BL Lac objects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".