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Record W2171430126 · doi:10.1093/mnrasl/slu108

Deep spectroscopic luminosity function of Abell 85: no evidence for a steep upturn of the faint-end slope

2014· article· en· W2171430126 on OpenAlexaff
I. Agulli, J. A. L. Aguerri, Rubén Sánchez-Janssen, R. Barrena, Antonaldo Diaferio, A. L. Serra, J. Méndez‐Abreu

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsLuminosity functionVirial theoremCluster (spacecraft)GalaxyVirial massRADIUSDwarf galaxyPopulationLuminosityGalaxy clusterAstronomy

Abstract

fetched live from OpenAlex

Abstract We present a new deep determination of the spectroscopic luminosity function (LF) within the virial radius of the nearby and massive Abell 85 (A85) cluster down to the dwarf regime (M* + 6) using Very Large Telescope/Visible Multi-Object Spectrograph (VLT/VIMOS) spectra for ∼2000 galaxies with mr ≤ 21 mag and 〈μe,r〉 ≤ 24 mag arcsec−2. The resulting LF from 438 cluster members is best modelled by a double Schechter function due to the presence of a statistically significant upturn at the faint end. The amplitude of this upturn ($\alpha _{{\rm f}} = -1.58^{+0.19}_{-0.15}$), however, is much smaller than that of the Sloan Digital Sky Survey (SDSS) composite photometric cluster LF by Popesso et al., αf ∼ −2. The faint-end slope of the LF in A85 is consistent, within the uncertainties, with that of the field. The red galaxy population dominates the LF at low luminosities, and is the main factor responsible for the upturn. The fact that the slopes of the spectroscopic LFs in the field and in a cluster as massive as A85 are similar suggests that the cluster environment does not play a major role in determining the abundance of low-mass galaxies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.200
Teacher spread0.192 · 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 teacher head, 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

Citations41
Published2014
Admission routes1
Has abstractyes

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