KIPAC Seminar: Model Predictive Scoring Shows Specific BAO Observations Drive w0wa Tension

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Abstract: The recent analyses of DESI DR2 BAO, Planck CMB and various Supernovae datasets have shown preferences for evolving dark energy at various levels of significance, under frequentist model comparison tests. However, the same analysis done with a pure Bayesian model comparison test can lead to the opposite conclusion due to the impact of w0wa priors. In contrast to these in-sample model comparison route, we approach the problem along a third axis -- which model better predicts unseen data. We use the Expected Log Predictive Density (ELPD) metric to score the predictiveness of ΛCDM and w0waCDM models. The comparison metric ΔELPD is out-of-sample, unlike Δχ2MAP and Bayes Factor, and insensitive to prior width, unlike the Bayes factor. Aggregated, we find modest preferences for w0waCDM, primarily driven by the BAO. Decomposing the ΔELPD score by redshift we find that a single BAO block, LRG2 (z=0.706), supplies the entire BAO preference. Interestingly, the biggest outlier point, LRG1, contributes little to the model predictive scoring because both ΛCDM and w0waCDM fail to predict the observation by equal amount. On the other hand, all SNIa datasets' ΔELPD scores are consistent with 0 in the LRG bins, indicating that at an out-of-distribution predictive level, the w0waCDM tension is driven by one BAO point.