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Record W4205242510 · doi:10.1051/0004-6361/202142643

The quenching of galaxies, bulges, and disks since cosmic noon

2022· article· en· W4205242510 on OpenAlexafffund
Asa F. L. Bluck, R. Maiolino, Simcha Brownson, Christopher J. Conselice, Sara L. Ellison, Joanna M. Piotrowska, Mallory Thorp

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
FundersLawrence Berkeley National LaboratorySmithsonian Astrophysical ObservatoryOffice of ScienceMax-Planck-Institut für AstronomieFondation MeracMinistério da Ciência, Tecnologia e InovaçãoChinese Academy of SciencesUniversity of OxfordMax-Planck-GesellschaftRoyal SocietyNatural Sciences and Engineering Research Council of CanadaYork UniversityCarnegie Institution for ScienceScience and Technology Facilities CouncilUniversity of Colorado BoulderUniversität BaselLeibniz-GemeinschaftUniversity of Notre DameFermilabNational Science FoundationCase Western Reserve UniversityCarnegie Mellon UniversityUniversity of PittsburghLos Alamos National LaboratoryUniversity of WashingtonPrinceton UniversityAlfred P. Sloan FoundationJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahOhio State UniversityVanderbilt UniversityDrexel UniversityU.S. Naval ObservatoryU.S. Department of EnergySmithsonian InstitutionNational Aeronautics and Space AdministrationNew Mexico State UniversityUniversity of PortsmouthYale University
KeywordsBulgePhysicsAstrophysicsRedshiftGalaxyQuenching (fluorescence)Stellar massStar formationCOSMIC cancer databaseAstronomy

Abstract

fetched live from OpenAlex

We present an analysis of the quenching of star formation in galaxies, bulges, and disks throughout the bulk of cosmic history, from z = 2 − 0. We utilise observations from the Sloan Digital Sky Survey and the Mapping Nearby Galaxies at Apache Point Observatory survey at low redshifts. We complement these data with observations from the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey at high redshifts. Additionally, we compare the observations to detailed predictions from the LGalaxies semi-analytic model. To analyse the data, we developed a machine learning approach utilising a Random Forest classifier. We first demonstrate that this technique is extremely effective at extracting causal insight from highly complex and inter-correlated model data, before applying it to various observational surveys. Our primary observational results are as follows: at all redshifts studied in this work, we find bulge mass to be the most predictive parameter of quenching, out of the photometric parameter set (incorporating bulge mass, disk mass, total stellar mass, and B / T structure). Moreover, we also find bulge mass to be the most predictive parameter of quenching in both bulge and disk structures, treated separately. Hence, intrinsic galaxy quenching must be due to a stable mechanism operating over cosmic time, and the same quenching mechanism must be effective in both bulge and disk regions. Despite the success of bulge mass in predicting quenching, we find that central velocity dispersion is even more predictive (when available in spectroscopic data sets). In comparison to the LGalaxies model, we find that all of these observational results may be consistently explained through quenching via preventative ‘radio-mode’ active galactic nucleus feedback. Furthermore, many alternative quenching mechanisms (including virial shocks, supernova feedback, and morphological stabilisation) are found to be inconsistent with our observational results and those from the literature.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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

Citations73
Published2022
Admission routes2
Has abstractyes

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