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Record W3106227518

Rejuvenation in z ∼ 0.8 Quiescent Galaxies in LEGA-C

2019· article· en· W3106227518 on OpenAlexaff
Priscilla Chauké, Arjen van der Wel, Camilla Pacifici, Rachel Bezanson, Po-Feng Wu, Anna Gallazzi, Caroline M. S. Straatman, Marijn Franx, Ivana Barišić, Eric F. Bell, Josha van Houdt, Michael V. Maseda, Adam Muzzin, David Sobral, Justin Spilker

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsYork University
Fundersnot available
KeywordsPhysicsAstrophysicsAstronomyGalaxyStellar massStar formationPopulationMedicine
DOInot available

Abstract

fetched live from OpenAlex

We use reconstructed star formation (SF) histories of quiescent galaxies at z = 0.6─1 in the Large Early Galaxy Astrophysics Census survey to identify secondary SF episodes that, after an initial period of quiescence, moved the galaxies back to the star-forming main sequence (blue cloud). 16 ± 3% of the z ∼ 0.8 quiescent population has experienced such rejuvenation events in the redshift range of 0.7 < z < 1.5 after reaching quiescence at some earlier time. On average, these galaxies first became quiescent at z = 1.2, and those that rejuvenated, remained quiescent for ∼1 Gyr before their secondary SF episode, which lasted ∼0.7 Gyr. The stellar mass attributed to rejuvenation is on average 10% of the galaxy stellar mass, with rare instances of an increase of more than a factor of 2. Overall, rejuvenation events only contribute ∼2% of the total stellar mass in z ∼ 0.8 quiescent galaxies and we conclude that rejuvenation is not an important evolutionary channel when considering the growth of the red sequence. However, our results complicate the interpretation of galaxy demographics in color space: the galaxies with rejuvenation events tend to lie in the so-called “green valley,” yet their progenitors were quiescent at z ∼ 2.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

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

Citations48
Published2019
Admission routes1
Has abstractyes

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