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Record W3102489971 · doi:10.5167/uzh-54411

Supersonic relative velocity effect on the baryonic acoustic oscillation measurements

2011· article· en· W3102489971 on OpenAlexaff
Jaiyul Yoo, Neal Dalal, Uroš Seljak

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsPhysicsDark energyBaryon acoustic oscillationsRedshiftRelative velocityAstrophysicsOscillation (cell signaling)BaryonAmplitudeDark matterGalaxyBispectrumSpectral densityCosmologyClassical mechanicsOptics

Abstract

fetched live from OpenAlex

We investigate the effect of supersonic relative velocities between baryons and dark matter, recently shown to arise generically at high redshift, on baryonic acoustic oscillation (BAO) measurements at low redshift. The amplitude of the relative velocity effect at low redshift is model-dependent, but can be parameterized by using an unknown bias. We find that if unaccounted, the relative velocity effect can shift the BAO peak position and bias estimates of the dark energy equation-of-state due to its non-smooth, out-of-phase oscillation structure around the BAO scale. Fortunately, the relative velocity effect can be easily modeled in constraining cosmological parameters without substantially inflating the error budget. We also demonstrate that the presence of the relative velocity effect gives rise to a unique signature in the galaxy bispectrum, which can be utilized to isolate this effect. Future dark energy surveys can accurately measure the relative velocity effect and subtract it from the power spectrum analysis to constrain dark energy models with high precision.

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.021
GPT teacher head0.191
Teacher spread0.170 · 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 designSimulation or modeling
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

Citations54
Published2011
Admission routes1
Has abstractyes

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