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Record W2780803684 · doi:10.1051/0004-6361/201732495

The VIMOS Public Extragalactic Redshift Survey (VIPERS)

2018· article· en· W2780803684 on OpenAlexaff
D. Vergani, B. Garilli, M. Polletta, P. Franzetti, M. Scodeggio, G. Zamorani, C. P. Haines, M. Bolzonella, L. Guzzo, B. R. Granett, S. de la Torre, U. Abbas, C. Adami, D. Bottini, A. Cappi, O. Cucciati, I. Davidzon, G. De Lucia, A. Fritz, A. Gargiulo, A. J. Hawken, A. Iovino, J. Krywult, V. Le Brun, O. Le Fèvre, D. Maccagni, K. Małek, F. Marulli, A. Pollo, L. A. M. Tasca, Rita Tojeiro, A. Zanichelli, S. Arnouts, J. Bel, E. Branchini, J. Coupon, O. Ilbert, T. Moutard, L. Moscardini

Bibliographic record

VenueAstronomy and Astrophysics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsSaint Mary's University
FundersInstitut national des sciences de l'UniversInstitut Universitaire de FranceCentre National de la Recherche ScientifiqueMinistero dell’Istruzione, dell’Università e della RicercaScience and Technology Facilities CouncilAgence Nationale de la Recherche
KeywordsPhysicsAstrophysicsGalaxyActive galactic nucleusRedshiftAstronomyLuminous infrared galaxyLuminosity

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.012
GPT teacher head0.212
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2018
Admission routes1
Has abstractyes

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