Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI)
Bibliographic record
Abstract
Strong gravitationally lensed quasars provide a powerful means to study galaxy evolution and cosmology. We use CHITAH, which is an algorithm used to hunt for new lens systems, particularly lensed quasars, in the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) S16A. We present 46 lens candidates, of which 3 are previously known. We select four high-grade candidates from CHITAHfor spectroscopic follow-up observations, and include two additional lenses found by YATTALENS, an algorithm used to classify lensed galaxies. We obtain X-shooter spectra of these six promising candidates for lens confirmation and redshift measurements. We report new spectroscopic redshift measurements for both the lens and source galaxies in four lens systems. We apply the lens modeling software GLEEto model our six X-shooter lenses uniformly. Through our analysis of the HSC images, we find that HSCJ022622−042522, HSCJ115252+004733, and HSCJ141136−010216 have point-like lensed images, and that the lens light distribution is well aligned with the lens mass distribution within 6 deg. We estimate the fluxes of the lensed source emission lines using X-shooter spectra, and use line ratio as a diagnostic on the Baldwin-Phillips-Terlevich (BPT) diagram. As a result, we find that HSCJ022622−042522 has a probable quasar source based on the upper limit of the [N II] flux intensity. We also measure the FWHM of Lyαemission of HSCJ141136−010216 to be ∼233 km s−1, showing that it is a probable Lyman-αemitter.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".