Full Reusable Synthetic Observational Data Generation for Cosmological Special and General Relativity vs Lamda CDM:Python Code, Complete Data tables, and Deep Numerical Comparison-Which Predicts Better?

Q: What does this research mean for the field?

A complete, reusable synthetic observational data generation framework, including Python code and data tables, enables deep numerical comparison between Cosmological Special Relativity, Cosmological General Relativity, and Lambda CDM models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Sangwha Yi

doi:10.5281/zenodo.22231178

September 3, 2026Open Access

Full Reusable Synthetic Observational Data Generation for Cosmological Special and General Relativity vs Lamda CDM:Python Code, Complete Data tables, and Deep Numerical Comparison-Which Predicts Better?

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Key Points

To establish reusable synthetic observational data generation pipelines and conduct numerical comparisons evaluating predictive performance across Cosmological Special Relativity, Cosmological General Relativity, and Lambda CDM.
Generated full synthetic observational datasets across Cosmological Special Relativity (CSTR), Cosmological General Relativity (CGR), and standard Lambda CDM frameworks.
Implemented algorithms using dedicated, open-source Python code and published complete structured data tables for reuse.
Produced fully accessible Python software routines for automated synthetic observational data synthesis.
Delivered complete comparative data tables enabling direct numerical evaluation of predictive accuracy between cosmological relativity frameworks and standard Lambda CDM.

Implication

Computational study reveals synthetic observational data generation pipelines comparing cosmological special and general relativity to Lambda CDM, highlighting reproducible benchmark tools.

Abstract

We present full reusable synthetic observational data generation for Cosmological Special Relativity(CSTR) and Cosmological General Relativity(CGR), versus Lamda CDM, with complete Python code and full data tables for reuse.

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Cite This Study

Sangwha Yi (2026) studied this question.

synapsesocial.com/papers/6a9bd3a66b95aff0620eacf8 https://doi.org/10.5281/zenodo.22231178

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Full Reusable Synthetic Observational Data Generation for Cosmological Special and General Relativity vs Lamda CDM:Python Code, Complete Data tables, and Deep Numerical Comparison-Which Predicts Better?

Key Points

Implication

Abstract

Discussion

Cite This Study

Full Reusable Synthetic Observational Data Generation for Cosmological Special and General Relativity vs Lamda CDM:Python Code, Complete Data tables, and Deep Numerical Comparison-Which Predicts Better?

Key Points

Implication

Abstract

Discussion

Cite This Study

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