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Record W3159440396 · doi:10.24908/iqurcp.7749

Is There a Fifth-Force? Using Galaxy Mergers to Constrain Dark-Matter Self-Interactions

2017· article· en· W3159440396 on OpenAlexvenueno aff
Maggie McLean

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsDark matterGalaxyAstrophysicsCold dark matter

Abstract

fetched live from OpenAlex

Over ninety percent of the matter in the universe is believed to be “dark matter,” a mysterious form of matter the nature of which is still unknown. Since it cannot be detected directly, dark matter can only be inferred from its effect on visible matter. This leaves a significant gap in our knowledge. Without the ability to measure the influence of dark matter on other dark matter, we could miss a possible fifth fundamental force which mediates dark matter self-interactions. We propose a means of constraining the existence of a “fifth-force” by observing galaxies that are in the process of merging. Using numerical simulations, we examine the effect of including a hypothetical fifth-force on the tidal disruption of visible matter during galaxy mergers. We find distinct differences in the formation and appearance of tidal features produced during these interactions, providing an observable constraint on the strength of any “fifth-force.” The sheer volume of interacting galaxies that can be observed makes tidal forces a valuable tool in studying a fundamental problem that would otherwise pose a great challenge for physicists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.406
Teacher spread0.312 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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