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Record W4403032412 · doi:10.1103/physrevd.110.083004

Dark matter induced baryonic feedback in galaxies

2024· article· en· W4403032412 on OpenAlexafffund
Javier F. Acevedo, Haipeng An, Yilda Boukhtouchen, Joseph Bramante, Mark L. A. Richardson, Lucy Sansom

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsPerimeter InstituteArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaTsinghua UniversityNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaU.S. Department of Energy
KeywordsPhysicsAstrophysicsDwarf galaxy problemDark matterBaryonic dark matterDwarf galaxyAstronomyDark matter haloCuspy halo problemGalaxyHalo

Abstract

fetched live from OpenAlex

We demonstrate that nongravitational interactions between dark matter and baryonic matter can affect structural properties of galaxies. Detailed galaxy simulations and analytic estimates demonstrate that dark matter which collects inside white dwarf stars and ignites type Ia supernovae can substantially alter star formation, stellar feedback, and the halo density profile through a dark matter induced baryonic feedback process, distinct from usual supernova feedback in galaxies. Published by the American Physical Society 2024

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.355
Teacher spread0.343 · 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 designTheoretical or conceptual
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

Citations5
Published2024
Admission routes2
Has abstractyes

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