Speaker
Description
Persistent cosmological tensions, including the Hubble and curvature tensions, together with the theoretical challenge of unifying General Relativity (GR) with the other fundamental forces in particle theory, motivate the exploration of extensions to the $\Lambda$CDM model.
We investigate the Torsion Condensation (TorC) model, a framework derived from Poincare Gauge Theory that introduces two parameters in addition to $\Lambda$CDM. The resulting modifications can be redefined as evolving dark energy components, i.e. as a standard extension to $\Lambda$CDM model. Implementing TorC in the CAMB Boltzmann code, we perform Bayesian inference using nested sampling with PolyChord and Cobaya to compare TorC against $\Lambda$CDM. Notably, the TorC field equations are insensitive to spatial curvature. We therefore examine the implications of this "k-screening" mechanism and compare it to k$\Lambda$CDM.
We present constraints on TorC from cosmological data and assess its performance relative to $\Lambda$CDM in terms of Bayesian evidence and tension metrics, evaluating its potential to alleviate current observational discrepancies. Our results highlight the importance of exploring well-motivated extensions to GR, and of applying robust statistical frameworks when assessing their viability against precision cosmological data.