Thawing Quintessence: Priors, evidence, and likely trajectories
David Shlivko
arXiv:2512.20832v1 Announce Type: new
Abstract: We perform a Bayesian comparison between thawing quintessence and a cosmological constant, incorporating theoretically motivated priors on the phenomenological Pad’e-w parameters used to model thawing dynamics. We find that thawing quintessence is consistently preferred over a cosmological constant when combining BAO data from DESI DR2 and CMB data from Planck+ACT with any of the major supernova compilations, including the recently updated DES-Dovekie sample. This preference is not sensitive to our choice of prior, but it is contingent on the inclusion of supernovae in the analysis. We comment on the consistency between various information criteria and Bayesian evidence ratios, finding that the Deviance Information Criterion (DIC) tracks the Bayesian evidence more reliably than either the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). Finally, we use observational likelihoods to identify which thawing trajectories are compatible with the available data, independently of theoretical priors.arXiv:2512.20832v1 Announce Type: new
Abstract: We perform a Bayesian comparison between thawing quintessence and a cosmological constant, incorporating theoretically motivated priors on the phenomenological Pad’e-w parameters used to model thawing dynamics. We find that thawing quintessence is consistently preferred over a cosmological constant when combining BAO data from DESI DR2 and CMB data from Planck+ACT with any of the major supernova compilations, including the recently updated DES-Dovekie sample. This preference is not sensitive to our choice of prior, but it is contingent on the inclusion of supernovae in the analysis. We comment on the consistency between various information criteria and Bayesian evidence ratios, finding that the Deviance Information Criterion (DIC) tracks the Bayesian evidence more reliably than either the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). Finally, we use observational likelihoods to identify which thawing trajectories are compatible with the available data, independently of theoretical priors.