The ΛCDM model, while successful at explaining Cosmic Microwave Background (CMB) anisotropies, faces increasing tension with late-universe observations, particularly regarding the Hubble constant (H0) and high-redshift expansion rates. In this work, we present a phenomenological correction to the luminosity distance relation derived from a "blind" machine learning analysis of over 4, 000 cosmological objects, including Type Ia Supernovae (Pantheon+), Baryon Acoustic Oscillations (BAO), Quasars, and Gamma-Ray Bursts. Using a Gated Residual Network (GatedResNet) optimized with a robust Cauchy loss function to mitigate outliers, we detect a systematic deviation in the distance modulus at z > 1 of magnitude Δμ ≈ -0. 18 log (1+z). Bayesian model comparison yields decisive evidence (ΔBIC > 300, Bayes Factor K > 10⁶0) favoring this correction over the standard ΛCDM model. We interpret this signal not as an evolution of Dark Energy, but as a "Container Lensing" effect: a topological magnification induced by a conformal inversion boundary condition at the cosmic horizon. We term this framework τCDM (Topological Cold Dark Matter). Furthermore, we report a numerical coincidence where the lensing amplitude A ≈ 0. 18 relates the cosmic acceleration scale to the galactic critical acceleration (a0), suggesting a path toward a unified Dark Sector theory (Pure-τ). Key Contributions: Blind Discovery: Identification of a systematic high-redshift deviation using scientific machine learning (GatedResNet) without assuming a background cosmology. Statistical Evidence: Overwhelming Bayesian support (ΔBIC ≈ +320) for the τCDM model over ΛCDM using a combined dataset of 2, 852 probes. The τCDM Framework: A geometric resolution to the Hubble Tension via Topological Horizon Lensing, explaining the bifurcation between distance probes (SNe, BAO, QSO) and clock probes (CC). Unified Dark Sector: A numerical link connecting the cosmic lensing amplitude (A ≈ 0. 18) with the MOND acceleration scale (a0), suggesting Dark Matter may be an inertial effect of the global topology.
Building similarity graph...
Analyzing shared references across papers
Loading...
José Ignacio Bautista Pérez (Wed,) studied this question.
www.synapsesocial.com/papers/69a75bf7c6e9836116a243be — DOI: https://doi.org/10.5281/zenodo.18402000
José Ignacio Bautista Pérez
Building similarity graph...
Analyzing shared references across papers
Loading...