An advanced computational framework for testing fundamental cosmological hypotheses using multi-messenger open data (CMB, Supernovae, Gravitational Waves, and LHC Open Data), alongside high-fidelity synthetic forecasts for next-generation observatories.
This project implements a robust statistical and computational pipeline to evaluate the limits of the Standard Cosmological Model (ΛCDM) against alternative theories such as Dynamic Dark Energy, Extra-Dimensional Gravity, and Conformal Cyclic Cosmology (CCC). Furthermore, it includes a cutting-edge Injection-Recovery framework to forecast the detection capabilities of future interferometers for beyond-GR anomalies.
- Physical Engines: CAMB (Einstein-Boltzmann solver), PyCBC (Gravitational wave informatics).
- Data Processing: Healpy (HEALPix spherical mapping), Uproot & Awkward (ROOT file processing), Pandas, NumPy.
- Statistical Inference: Emcee (MCMC), SciPy (Optimization), Grid-based Bayesian Inference, Savage-Dickey Density Ratios, Akaike/Bayesian Information Criteria (AIC/BIC).
- Scientific Goal: Testing for deviations from the cosmological constant (Λ) using a dynamic Equation of State parameter.
- Dataset: Pantheon+ (1590 Type Ia Supernovae) & SDSS DR12 BAO.
- Methodology: Bayesian inference via MCMC with analytical marginalization over absolute magnitude.
- Scientific Goal: Constraining the "leakage" of gravitational waves into extra dimensions using current observational limits.
- Dataset: LIGO GW150914 Strain Data + SN + BAO.
- Methodology: Waveform damping analysis and signal-to-noise ratio (SNR) consistency checks via joint MCMC.
- Scientific Goal: Searching for Missing Transverse Energy (MET) as a proxy for extra-dimensional particle decay.
- Dataset: CERN ATLAS 13 TeV Monojet Open Data.
- Methodology: High-dimensional phase-space analysis and background estimation using ROOT/Uproot.
- Scientific Goal: Modeling early universe leakage by adjusting the effective number of relativistic species and Dynamic Dark Energy.
- Methodology: Acoustic peak simulation utilizing the CAMB Boltzmann solver.
- Scientific Goal: Evaluating the 2D Holographic QFT Power Spectrum against 3D Inflation.
- Dataset: Planck 2018 CMB (TT Spectrum).
- Methodology: Custom power spectrum injection into CAMB and reduced χ²_ν optimization via Nelder-Mead.
- Scientific Goal: Searching for concentric rings of low variance in the CMB as remnants of a previous Aeon.
- Dataset: Planck 2018 SMICA Full-Sky Map.
- Methodology: Spherical image processing (HEALPix), radial autocorrelation, and rigorous Monte Carlo Look-Elsewhere Effect (LEE) / Bonferroni corrections.
- Scientific Goal: Forecasting the pipeline's sensitivity to detect macroscopic extra-dimensional gravitational leakage (2% phenomenological deviation) using next-generation observatories.
- Dataset: 50,000-event High-Fidelity Synthetic Catalog (LISA/Cosmic Explorer era) utilizing Madau-Dickinson Star Formation Evolution.
- Methodology: Ultra-high resolution 1D grid-based Bayesian inference, Savage-Dickey Bayes Factor calculation, and Lognormal instrumental noise modeling.
| Stage | Hypothesis Tested | Finding | Statistical Significance |
|---|---|---|---|
| Stage 1 | Dynamic Dark Energy | ΛCDM Preferred | ΔBIC > 10 |
| Stage 2 | Extra-Dimensional Leakage | No Leakage Detected | η ≈ 0 |
| Stage 3 | LHC MET Anomaly | Consistent with SM | No 5σ excess |
| Stage 5 | Holographic QFT | Competitive Fit | χ²_ν ≈ 1.13 |
| Stage 6 | CCC Hawking Points | No Signal Detected | 1.7σ (Noise consistent) |
| Stage 7 | Brane-Bulk Leakage Forecast | Signal Successfully Recovered | > 22σ (BF > 10^110) |
Overall Conclusion: Across Stages 1 through 6, using current multi-messenger observational data, the Standard Cosmological Model (ΛCDM) and the Standard Model of Particle Physics consistently emerged as the statistically preferred frameworks. Despite rigorous testing, no significant anomalies (≥ 5σ excesses) were detected in current datasets. However, Stage 7 demonstrates the profound capability of this computational architecture: the injection-recovery framework proves that the pipeline is mathematically primed to isolate and recover even microscopic deviations (e.g., 2% Brane-Bulk leakage) with overwhelming Bayesian evidence (Bayes Factor > 10^110) when next-generation multi-messenger data becomes available.
- MCMC Parameter Estimation (Dark Energy Analysis)
Figure 1: Posterior distributions for dynamic dark energy model parameters.
- Holographic Universe vs. Standard Inflation
Figure 2: Planck 2018 TT Power Spectrum fit against Afshordi QFT model and ΛCDM.
- Concentric Ring Variance Analysis (Conformal Cyclic Cosmology)
Figure 3: Hawking Point search on Planck SMICA map with Monte Carlo LEE correction.
- Ultra-High Resolution Leakage Posterior (Injection-Recovery Framework)
Figure 4: Grid-based Bayesian posterior successfully recovering the injected 2% gravitational leakage against a simulated Standard Model baseline.
git clone [https://github.com/fatihwf/AstroData-Testing-Pipeline.git](https://github.com/fatihwf/AstroData-Testing-Pipeline.git)
cd AstroData-Testing-Pipeline
pip install -r requirements.txtDistributed under the MIT License. See LICENSE for more information.
If you use this framework or data pipeline in your research or projects, please cite it as follows:
@misc{Goc2026Cosmo,
author = {Fatih Gazi Göç},
title = {AstroData-Testing-Pipeline: A Multi-Stage Cosmological Data Analysis Pipeline},
year = {2026},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{[https://github.com/fatihwf/AstroData-Testing-Pipeline](https://github.com/fatihwf/AstroData-Testing-Pipeline)}}
}