RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$α$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data
Bibliographic record
Abstract
The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), delivering deep, multi-band ($ugrizy$) imaging data across 18,000 square degrees over the next decade. Before this ultra-wide-field survey, we constructed a broad-band Ly$α$ imaging toward 483 SDSS/BOSS quasars at $z=$ 1.9-3.0, using deep, wide-field ultraviolet to near-infrared ($u$-to-$K$) data from the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP), the CFHT Large Area U-band Deep Survey (CLAUDS), the Deep UKIRT Near-Infrared Steward Survey (DUNES$^2$), and additional public data covering 13 square degrees. Our broad-band selection allowed us to select 24 candidate quasar nebulae that exhibit $u$ or $g$ band excess over 50-170 kpc, some of which exhibit asymmetrical extended features similar to those seen in previously discovered giant nebulae. We then investigated whether the Ly$α$ morphology of quasar nebulae differs between two redshift intervals, $z=$ 1.9-2.3 and $z=$ 2.3-3.0, and examined environmental dependence based on a control sample. Comparison results show no significant difference in asymmetry within Ly$α$ nebulae between the two redshift intervals. Furthermore, we found no systematic differences in overdensities around the complete quasar samples, quasars with large Ly$α$ nebulae, and control samples, while the most extended nebula appears to be located in the high-density region. Further verification analyses are required since the current dataset lacks spectroscopic confirmation for both quasar nebulae and their surrounding neighbours. Nevertheless, the results demonstrate the great potential of the Rubin LSST to discover giant Ly$α$ nebulae on an unprecedented scale.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".