The final SDSS-IV/SPIDERS X-ray point source spectroscopic catalogue
Bibliographic record
Abstract
Aims. We look to provide a detailed description of the SPectroscopic IDentification of ERosita Sources (SPIDERS) survey, an SDSS-IV programme aimed at obtaining spectroscopic classification and redshift measurements for complete samples of sufficiently bright X-ray sources. Methods. We describe the SPIDERS X-ray Point Source Spectroscopic Catalogue, considering its store of 11 092 observed spectra drawn from a parent sample of 14 759 ROSAT and XMM sources over an area of 5129 deg 2 covered in SDSS-IV by the eBOSS survey. Results. This programme represents the largest systematic spectroscopic observation of an X-ray selected sample. A total of 10 970 (98.9%) of the observed objects are classified and 10 849 (97.8%) have secure redshifts. The majority of the spectra (10 070 objects) are active galactic nuclei (AGN), 522 are cluster galaxies, and 294 are stars. Conclusions. The observed AGN redshift distribution is in good agreement with simulations based on empirical models for AGN activation and duty cycle. Forming composite spectra of type 1 AGN as a function of the mass and accretion rate of their black holes reveals systematic differences in the H-beta emission line profiles. This study paves the way for systematic spectroscopic observations of sources that are potentially to be discovered in the upcoming eROSITA survey over a large section of the sky.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.027 |
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".