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Record W3215771671 · doi:10.1103/physrevd.105.083504

Correlating galaxy shapes and initial conditions: An observational study

2022· article· en· W3215771671 on OpenAlexafffund
Pavel Motloch, Ue‐Li Pen, Hao-Ran Yu

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Advanced ResearchCanadian Institute for Theoretical AstrophysicsPerimeter InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaSimons FoundationCanada Foundation for InnovationOntario Research FoundationCanadian Institute for Advanced ResearchAlexander von Humboldt-Stiftung
KeywordsObservablePhysicsGalaxyAstrophysicsSkySigmaCorrelation function (quantum field theory)Spiral galaxyRedshiftAstronomyQuantum mechanics

Abstract

fetched live from OpenAlex

Using data from the Sloan Digital Sky Survey we study correlations between directions of galaxy angular momenta determined from images of spiral galaxies and various observables derived from the reconstructed initial conditions. We find an apparent systematic effect consistent with galaxy-orientation-dependent selection function. After restricting our attention to the brightest half of the galaxies where this systematic effect is presumed to be absent, we find hints of excess/deficit correlation for two observables. Interestingly, tidal torque theory predicts excess/deficit correlation in exactly these two observables. After correcting for the redshift space distortions, the significance of these correlations drops below $3\ensuremath{\sigma}$ threshold. We do not find any other systematic issues, but a thorough systematic analysis goes beyond the scope of this work.

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)0.005
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.050
GPT teacher head0.421
Teacher spread0.371 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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