Optically detected galaxy cluster candidates in the <i>AKARI</i> North Ecliptic Pole field based on photometric redshift from the Subaru Hyper Suprime-Cam
Bibliographic record
Abstract
ABSTRACT Galaxy clusters provide an excellent probe in various research fields in astrophysics and cosmology. However, the number of galaxy clusters detected so far in the AKARI North Ecliptic Pole (NEP) field is limited. In this work, we provide galaxy cluster candidates in the AKARI NEP field with the minimum requisites based only on the coordinates and photometric redshift (photo-z) of galaxies. We used galaxies detected in five optical bands (g, r, i, z, and Y) by the Subaru Hyper Suprime-Cam (HSC), with additional data from the u band obtained from the Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and from the IRAC1 and IRAC2 bands from the Spitzer space telescope for photo-z estimation. We calculated the local density around every galaxy using the 10th-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. A total of 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg2 were identified. The reliability of our method was examined through false-detection tests, redshift-uncertainty tests, and applications on the Cosmic Evolution Survey (COSMOS) data, giving false-detection rates of 0.01 to 0.05 and a recovery rate of 0.9 at high richness. Three X-ray clusters previously observed by ROSAT and Chandra were recovered. The cluster galaxies show a higher stellar mass and lower star formation rate compared with the field galaxies in two-sample Z-tests. These cluster candidates are useful for environmental studies of galaxy evolution and future astronomical surveys in the NEP, where AKARI has performed unique nine-band mid-infrared photometry for tens of thousands of galaxies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.002 | 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".