Controlled Tension Forecasting: Quantifying Cross-Probe Biases in $\omega_0\omega_a$CDM

TL;DR

This paper introduces a systematic framework to quantify how cross-probe tensions in cosmological data influence inferred dark energy parameters, aiding in robust multi-probe analysis for future surveys.

Contribution

It develops a controlled tension injection method to assess the impact of probe-level inconsistencies on dark energy parameter estimation in cosmology.

Findings

Quantifies how systematic tensions bias dark energy parameters.

Identifies probe combinations most susceptible to spurious signals.

Provides guidance for robust multi-probe cosmological inference.

Abstract

Recent analyses combining DESI DR2 BAO, Planck CMB, and Pantheon+ SNe have reported mild but intriguing deviations from the LambdaCDM model. A central challenge is to determine whether these deviations reflect genuine dynamical dark energy behavior or instead arise from cross-probe inconsistencies, prior choices, or mismatches in likelihood construction. Previous work demonstrated that imposing a biased supernova-motivated prior on Omega_{m0} can artificially displace the BAO-inferred w_0,w_a values from the LambdaCDM expectation. A complementary pedagogic study further showed that the differing degeneracy geometries of BAO, CMB, and SNe can generate apparent dark energy evolution even when the underlying cosmology is exactly LambdaCDM. In this manuscript, we develop a controlled tension injection framework that provides a systematic means of quantifying how probe-level tensions influence inferred dark energy parameters. Self-consistent BAO, CMB, and SNe mock datasets are augmented with parameterized shifts in (Omega_{m0}, H_0), supernova absolute calibration, and the BAO sound-horizon scale $r_d$. The resulting datasets are analyzed through a unified MCMC pipeline, enabling a direct assessment of how these controlled tensions propagate into biases in (w_0, w_a) and the pivot equation-of-state parameter w_p. This forecasting framework provides practical guidance for identifying probe combinations that are most susceptible to spurious signatures of dynamical dark energy, and helps ensure robust multi-probe inference in forthcoming precision surveys.