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Record W2019016642 · doi:10.1088/0004-637x/782/1/33

THE PAN-STARRS1 MEDIUM-DEEP SURVEY: THE ROLE OF GALAXY GROUP ENVIRONMENT IN THE STAR FORMATION RATE VERSUS STELLAR MASS RELATION AND QUIESCENT FRACTION OUT TO<i>z</i>∼ 0.8

2014· article· en· W2019016642 on OpenAlexfundno aff
Lihwai Lin, Hung-Yu Jian, S. Foucaud, P. Norberg, R. G. Bower, Shaun Cole, P. Arnalte-Mur, Chin-Wei Chen, Jean Coupon, Bau-Ching Hsieh, S. Heinis, S. Phleps, Wenping Chen, Chien‐Hsiu Lee, W. S. Burgett, K. C. Chambers, L. Denneau, P. W. Draper, H. Flewelling, K. W. Hodapp, M. E. Huber, N. Kaiser, R. P. Kudritzki, E. A. Magnier, N. Metcalfe, P. A. Price, J. Tonry, R. J. Wainscoat, C. Waters

Bibliographic record

VenueThe Astrophysical Journal · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilPlanetary Science DivisionScience Mission DirectorateSmithsonian Astrophysical ObservatoryMax-Planck-Institut für AstronomieQueen's UniversityJohns Hopkins UniversityQueen's University BelfastNational Aeronautics and Space AdministrationEötvös Loránd TudományegyetemNational Central UniversitySpace Telescope Science InstituteNational Science CouncilDurham UniversitySmithsonian InstitutionNational Science Foundation
KeywordsAstrophysicsPhysicsStellar massStar formationGalaxyQuenching (fluorescence)Astronomy

Abstract

fetched live from OpenAlex

Using a large optically selected sample of field and group galaxies drawn from the Pan-STARRS1 Medium-Deep Survey (PS1/MDS), we present a detailed analysis of the specific star formation rate (SSFR)—stellar mass ( M * ) relation, as well as the quiescent fraction versus M * relation in different environments. While both the SSFR and the quiescent fraction depend strongly on stellar mass, the environment also plays an important role. Using this large galaxy sample, we confirm that the fraction of quiescent galaxies is strongly dependent on environment at a fixed stellar mass, but that the amplitude and the slope of the star-forming sequence is similar between the field and groups: in other words, the SSFR–density relation at a fixed stellar mass is primarily driven by the change in the star-forming and quiescent fractions between different environments rather than a global suppression in the star formation rate for the star-forming population. However, when we restrict our sample to the cluster-scale environments ( M > 10 14 M ☉ ), we find a global reduction in the SSFR of the star-forming sequence of 17% at 4σ confidence as opposed to its field counterpart. After removing the stellar mass dependence of the quiescent fraction seen in field galaxies, the excess in the quiescent fraction due to the environment quenching in groups and clusters is found to increase with stellar mass, although deeper and larger data from the full PS1/MDS will be required to draw firm conclusions. We argue that these results are in favor of galaxy mergers to be the primary environment quenching mechanism operating in galaxy groups whereas strangulation is able to reproduce the observed trend in the environment quenching efficiency and stellar mass relation seen in clusters. Our results also suggest that the relative importance between mass quenching and environment quenching depends on stellar mass—the mass quenching plays a dominant role in producing quiescent galaxies for more massive galaxies, while less massive galaxies are quenched mostly through the environmental effect, with the transition mass around 1–2 × 10 10 M ☉ in the group/cluster environment.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.198
Teacher spread0.191 · 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

Citations96
Published2014
Admission routes1
Has abstractyes

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