Deep Extragalactic VIsible Legacy Survey (DEVILS): first data release covering the D10 (COSMOS) region
Bibliographic record
Abstract
ABSTRACT The Deep Extragalactic VIsible Legacy Survey (DEVILS) is a deep, high-completeness multiwavelength survey based around spectroscopic observations using the Anglo-Australian Telescope’s AAOmega spectrograph. The survey covers $\sim 4.5$ deg$^{2}$ over three extragalactic fields to Y$_{\mathrm{ AB}}< 21.2$ mag and probes sources at $0< z< 1.2$, with a median redshift of $z=0.53$. Here, we describe the DEVILS spectroscopic observations, data reduction, and redshift analysis. We then describe and release to the community all DEVILS data in the 10h [D10, COSMOS (Cosmological Evolution Survey)] region including: (i) catalogues of redshifts, photometry, spectral energy distribution fitting for physical properties, visual morphologies, structural decompositions, and group environments/halo masses, (ii) matched imaging in 28 bands from X-rays to radio continuum, and (iii) reduced 1D spectra. All data are made publicly available through Data Central (datacentral.org.au). Within D10, we obtain 5442 new high-quality spectroscopic redshifts. When combined with existing, lower quality, redshift information ($\mathrm{ {\mathrm i.e.}}$ photometric redshifts), this is increased to 7946. Of these, 3122 have a spectroscopic redshift from another source (many that was not available at the time of the DEVILS observations). As such, DEVILS provides new unique high-quality spectroscopic redshifts for 4824 faint sources in COSMOS. This increases the spectroscopic completeness at Y-mag $\sim$ 21 from $\sim$50 per cent in other samples to $\sim$90 per cent in DEVILS. Finally, we show the power of this data set by exploring the suppression of star formation in overdense environments, split by morphology and stellar mass, and highlighting the ubiquitous nature of environmental quenching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".