The VIMOS Public Extragalactic Redshift Survey (VIPERS)
Bibliographic record
Abstract
Aims. We analyse the properties of the host galaxies of a [NeV]-selected sample to investigate whether and how they are affected by the AGN. Methods. We have selected a sample of galaxies at 0.62 < z < 1.2 from the VIMOS Public Extragalactic Redshift Survey (VIPERS) and divided it in blue cloud galaxies, red passive galaxies and green valley galaxies using the NUVrK diagram. Within each category, galaxies with AGN activity were identified based on the detection of the high-ionisation [NeV]λ3426 emission line. For each galaxy we derived several properties (stellar age and mass, the (r−K) colour, the [OII] luminosity) and compared them between active and inactive galaxies matched in stellar mass and redshift. Results. We find statistically significant differences in the properties between active and inactive galaxies. These differences imply that the AGN is more often found in galaxies with younger stellar populations and more recent star-forming activity than their parent samples. Interestingly, the AGN identified through the [NeV]λ3426 emission line is not commonly found by traditional AGN-selection techniques based on shallow X-ray data, mid-IR colours, and classical line diagnostic diagrams, and might thus reveal a specific evolutionary phase. The spectral analysis reveals a sub-set of AGN within the blue cloud that has spectral signatures implying a sudden suppression of star formation activity similar to post-starburst galaxies. Conclusion. Using the rich dataset of the large VIPERS sample we identify a novel class of active post-starburst galaxies that would be missed by traditional selection techniques. These galaxies belong to the blue cloud, but their star-formation activity has been recently suppressed, possibly by the AGN identified through the presence of the [NeV]λ3426 emission line in their spectra. Our results support the idea that AGN feedback may be responsible for halting star-formation in active blue galaxies and for their transition into the red sequence, at least in the 0.6–1.2 redshift range and for stellar masses greater than 5 × 1010 ℳ⊙. Our results are based on a complete spectroscopic sample and limited by the [NeV] observability, and the AGN can be variable and with a relatively short duty cycle. Considering this, AGN feedback that makes blue galaxies quickly transition to the red sequence may be even more common than previously believed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.002 |
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".