An advanced computational framework for testing fundamental cosmological hypotheses using multi-messenger open data (CMB, Supernovae, Gravitational Waves, and LHC Open Data).
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).
- 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),Akaike/Bayesian Information Criteria (AIC/BIC).
Scientific Goal: Testing for deviations from the cosmological constant (Λ) using a dynamic Equation of State parameter (Γ).
- Dataset: Pantheon+ (1590 Type Ia Supernovae) & SDSS DR12 BAO.
- Methodology: Bayesian inference via MCMC with analytical marginalization over M_B.
Scientific Goal: Constraining the "leakage" of gravitational waves into extra dimensions.
- 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 (N_eff) 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 P(k) 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.
| 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) |
Overall Conclusion: Across all stages of this multi-messenger analysis, the Standard Cosmological Model (ΛCDM) and the Standard Model of Particle Physics consistently emerged as the statistically preferred frameworks. Despite rigorous testing of advanced alternative theories—including dynamic dark energy, extra-dimensional gravity leakage, and conformal cyclic cosmology—no significant anomalies (e.g., ≥ 5σ excesses or strong Bayesian preference) were detected. The data firmly reinforces the robust predictive power of the Standard Model.
1. MCMC Parameter Estimation (Dark Energy Analysis) 
Figure 1: Posterior distributions for dynamic dark energy model parameters.
2. Holographic Universe vs. Standard Inflation 
Figure 2: Planck 2018 TT Power Spectrum fit against Afshordi QFT model and ΛCDM.
3. Concentric Ring Variance Analysis (Conformal Cyclic Cosmology) 
Figure 3: Hawking Point search on Planck SMICA map with Monte Carlo LEE correction.
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)}}
}