PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 22, 20260 citationsOpen Access

Solvency Field Theory (SFT): The V7 Framework From Information Thermodynamics to Dark Energy, Galaxy Dynamics, and Exact General Relativity

View Full Paper
SFShaun Fosmark

Key Points

  • Investigate the connection between information thermodynamics and cosmic expansion through Solvency Field Theory.
  • Developed a V7 Framework founded on Landauer's Principle and Holographic Bound.
  • Analyzed SPARC galaxy data to test weak-field dynamics and rotation curves.
  • Utilized IllustrisTNG300 data to model dark energy evolution.
  • SFT predicts a fixed acceleration law reducing rotation-curve error by 63% in SPARC galaxies.
  • The dark energy equation of state aligns with DESI DR2 observations without invoking a Cosmological Constant.
  • Mathematical derivations confirm a connection with the Schwarzschild exterior metric.

Abstract

Solvency Field Theory (SFT): The V7 Framework From Information Thermodynamics to Dark Energy, Galaxy Dynamics, and Exact General Relativity Overview Solvency Field Theory (SFT) proposes that cosmic expansion is not driven by a mysterious vacuum fluid (), but is instead the thermodynamic cost of irreversible information processing. Grounded in Landauer’s Principle and the Holographic Bound, SFT dictates that space has a finite information capacity. As active baryonic structures (black holes, stars) generate entropy, the cosmic horizon must scale to maintain thermodynamic solvency. Under SFT, expansion is a definitional thermodynamic identity: H = Ṡₓ₎ₓ₀₋ / I (where H is the fractional rate of entropy accumulation relative to the universe's stored capacity). The V7 Framework Results This repository contains the mathematical derivations, cosmological data analysis, and reproducible code demonstrating that a single information-theoretic principle naturally resolves anomalies across three distinct orders of magnitude, without invoking new particles or modifying the core equations of General Relativity. 1. Galactic Dynamics (The SPARC V7 Weak-Field Test) SFT yields a weak-field acceleration threshold of aₒ₅ₓ = cH₀/2. Applied to the raw baryonic mass of 137 SPARC galaxies, this fixed acceleration law reduces the mean relative rotation-curve error by 63% (from 0. 458 to 0. 167) with zero per-galaxy fitting parameters, providing a first-principles physical origin for empirical modifications like MOND. 2. Cosmological Expansion (The DESI DR2 Match) By modeling the universe as a bimodal entropy ledger (active baryonic processing + passive structural capacity) using 150GB of IllustrisTNG300 telemetry, SFT natively reproduces the evolving Dark Energy equation of state observed in the recent DESI DR2 data. The theoretical prediction (w₀ = -0. 837, wₐ = -0. 615) lands cleanly inside the DESI phantom-crossing shadow, distance 0. 005 in the CPL plane, without a Cosmological Constant. 3. Local Gravity & Strong Field (Exact GR Derivations) At the scale of compact objects, the SFT "Compactness Burden" (= GM/2c²r) acts as a localized spatial warp to store intense baryonic information debt. We prove mathematically that this limit strictly forces the exact Schwarzschild exterior metric and closes identically to the Tolman-Oppenheimer-Volkoff (TOV) hydrostatic equations. Reading Guide for V7 If you are new to this framework, please begin with the SFTV7ReadersGuide. pdf. To verify the galaxy rotation curves: See sftᵥ7weakfieldₚaperdraft. pdf. To verify the local gravity math: See sftdoc₆ₛchwarzschildderivation. pdf. To verify the CMB compatibility: SFT preserves the Planck 2018 acoustic peaks with a 0. 078% RMS residual, predicting a distinct large-scale ISW crossover signature for future observatories (e. g. , CMB-S4). All data necessary to reproduce the SPARC and IllustrisTNG bimodal regressions are included in the supplementary. zip bundles.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Shaun Fosmark (2026) studied this question.

synapsesocial.com/papers/69bf3924c7b3c90b18b4375fhttps://doi.org/10.5281/zenodo.19136724
Ask AI
Helpful
Bookmark
Share
View Full Paper