GLACE survey: OSIRIS/GTC tuneable filter H<i>α</i>imaging of the rich galaxy cluster ZwCl 0024.0+1652 at<i>z</i> = 0.395
Bibliographic record
Abstract
The cores of clusters at 0 ≲ z ≲ 1 are dominated by quiescent early-type galaxies, whereas the field is dominated by star-forming late-type galaxies. Clusters grow through the accretion of galaxies and groups from the surrounding field, which implies that galaxy properties, notably the star formation ability, are altered as they fall into overdense regions. The critical issues for understanding this evolution are how the truncation of star formation is connected to the morphological transformation and what physical mechanism is responsible for these changes. The GaLAxy Cluster Evolution Survey (GLACE) is conducting a thorough study of the variations in galaxy properties (star formation, AGN activity, and morphology) as a function of environment in a representative and well-studied sample of clusters. To address these questions, the GLACE survey is making a deep panoramic survey of emission line galaxies (ELG), mapping a set of optical lines ([O ii], [O iii], Hβ andHα/[N ii] when possible) in several galaxy clusters at z ~ 0.40, 0.63, and 0.86. Using the tunable filters (TF) of the OSIRIS instrument at the 10.4 m GTC telescope, the GLACE survey applies the technique of TF tomography: for each line, a set of images are taken through the OSIRIS TF, each image tuned at a different wavelength (equally spaced), to cover a rest frame velocity range of several thousand km s-1 centred on the mean cluster redshift, and scanned for the full TF field of view of an 8 arcmin diameter. Here we present the first results of the GLACE project, targeting the Hα/[N ii] lines in the intermediate-redshift cluster ZwCl 0024.0+1652 at z = 0.395. Two pointings have been performed that cover ~2 × rvir. We discuss the specific techniques devised to process the TF tomography observations in order to generate the catalogue of cluster Hα emitters, which contains more than 200 sources down to a star formation rate (SFR) ≲1 M⊙/yr. An ancillary broadband catalogue is constructed, allowing us to discriminate line interlopers by means of colour diagnostics. The final catalogue contains 174 unique cluster sources. The AGN population is distinguished using different diagnostics and found to be ~37% of the ELG population. The median SFR of the star-forming population is 1.4 M⊙/yr. We studied the spatial distribution of ELG and confirm the existence of two components in the redshift space. Finally, we exploited the outstanding spectral resolution of the TF, attempting to estimate the cluster mass from ELG dynamics, finding M200 = (4.1 ± 0.2) × 1014 M⊙ h-1, in agreement with previous weak-lensing estimates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".