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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".