PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 13, 20260 citationsOpen Access

EFTP1RCGGL: Joint Fitting and Closure Testing of Rotation Curves and Galaxy–Galaxy Weak Lensing (GGL)

GTGuanglin Tu

Key Points

  • The aim is to quantitatively compare two theoretical frameworks, EFT average-gravity modification models and DM_RAZOR cold dark matter baseline, using the same data and statistical methods.
  • Collected SPARC rotation curves of 104 galaxies, preprocessed into 2,295 velocity points.
  • Utilized KiDS-1000 galaxy-galaxy weak lensing data with full covariance.
  • Conducted RC-only inference, RC→GGL closure testing, GGL-only inference, and joint RC+GGL inference.
  • Applied audits for consistency in quoted results under strict parameter constraints.
  • EFT models show significant improvement over DM_RAZOR in joint fitting, with ΔlogL_total = 1155–1337.
  • EFT achieved a closure strength ΔlogL_closure = 172–281, higher than DM_RAZOR’s 127.
  • Closure signal remained stable across systematic tests, confirming non-coincidental predictive power.

Abstract

This record provides an audit-oriented, reproducible release package for a quantitative comparison between EFT “average-gravity modification” models and a cold dark matter NFW-halo baseline (DMRAZOR), under the same data and the same statistical protocol. It includes (i) the bilingual release report, (ii) a minutes-level verification package (runnableₛnapshot) for checksum/quote alignment, and (iii) a Tables not to be confused with the common abbreviation for Effective Field Theory), and a cold dark matter (DM) baseline model using an NFW halo (DMRAZOR). The data comprise: (i) SPARC rotation curves (RC), uniformly preprocessed and binned into 2, 295 velocity points (104 galaxies; 20 RC bins), and (ii) KiDS-1000 galaxy–galaxy weak lensing (GGL) excess surface density ΔΣ (R) from Brouwer et al. (2021) (4 stellar-mass bins × 15 R-points per bin, 60 points total, with the full covariance). We sequentially perform RC-only inference, an RC→GGL closure test, GGL-only inference, and joint RC+GGL inference, and apply consistency audits to guarantee that every quoted number is traceable. Under a strict parameter ledger and shared mapping constraints (DM: 20 log M200bin; EFT: 20 log V0bin + one global log ℓ), the EFT family outperforms DMRAZOR in the joint fit: ΔlogLₜotal = 1155–1337 (relative to DMRAZOR). More importantly, the closure test shows that the RC posterior has non-trivial predictive power for GGL: EFT achieves a closure strength ΔlogLclosure = 172–281, higher than DMRAZOR’s 127; after randomly shuffling the RC-bin→GGL-bin grouping, the closure signal collapses to 6–23, confirming that it is neither a statistical coincidence nor an implementation artifact. Across systematic sweeps of σᵢnt, Rₘin, and covariance shrinkage, EFT’s relative advantage remains positive and stable in magnitude. Record structure (aligned with the “3-pack” set in Section 8. 2): Release report (main text): two PDFs (English & Chinese). runnableₛnapshot: minutes-level verification (no full refit required), for checksums/manifests and quoted-number alignment. Tables & Figures Supplement: all tables/figures cited in the report. Full reproduction runpack (Record B, Software, CC BY 4. 0): Concept DOI (stable entry): https: //doi. org/10. 5281/zenodo. 18526286 (End-to-end reproduction from scratch: public-data download → preprocessing → full fit/sampling → regeneration of tables/figures → auditable snapshots. )

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Guanglin Tu (2026) studied this question.

synapsesocial.com/papers/698ebf6985a1ff6a93016e69https://doi.org/10.5281/zenodo.18606011
Ask AI
Helpful
Bookmark
Share
View Full Paper