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Record W4414656699 · doi:10.1103/ym2n-lzts

Probing Vector Chirality in the Early Universe

2025· article· en· W4414656699 on OpenAlexaff
Junsup Shim, Ue‐Li Pen, Hao-Ran Yu, Teppei Okumura

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoPerimeter InstituteCanadian Institute for Advanced Research
FundersAcademia SinicaMinistry of Science and Technology, TaiwanNational Natural Science Foundation of China
KeywordsHaloSpinsParity (physics)AsymmetryHelicityGalaxySpin (aerodynamics)Dark matter

Abstract

fetched live from OpenAlex

We explore the potential of using late-time galaxy spins to test the parity symmetry of primordial vector fossils. Using N-body simulations, we analyze halo spins as a reliable proxy for galaxy spins to investigate the detectability of this effect. We develop a novel approach to generate initial conditions (ICs) that have substantial parity asymmetry but do not alter the initial matter power spectrum. We construct the initial spin fields from the parity broken ICs and halo spin fields using late-time halos evolved from such ICs. Focusing on the helicity of these vector fields, we detect substantial asymmetry in the initial spin field. In addition, we find that over 50% of the initial spin field's asymmetry remains in the late-time halo spin field on a range of scales. Based on mock galaxy spin fields derived from the halo spin fields, we forecast that a maximum detection at 13σ is possible with the final DESI BGS for the model considered in this analysis. Our findings demonstrate that primordial vectorial parity violation survives nonlinear gravitational evolution, and thus, can be effectively probed with galaxy spins at late times.

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)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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