Speaker
Description
Recent cosmological surveys have opened a new window onto the nature of dark energy. In our work we reconstruct the dark energy equation of state using a “flexknot” parameterisation that represents $w(a)$ as a linear spline with free–moving nodes. By combining the latest DESI Baryonic Acoustic Oscillation measurements with Pantheon+ supernovae data—and cross–checking our results with an independent Cobaya–based pipeline—we obtain posterior distributions for $w(a)$ that reveal an unexpected W–shaped structure. Although the Bayesian evidence does not ultimately favour dynamical dark energy over the standard $\Lambda$CDM model, our analysis shows that even non–CMB datasets can indicate deviations from a constant $w = –1$.
We have also generalised dataset tension statistics to marginalise over multiple models, ensuring our unexpected results are not driven by inter-dataset disagreement.
We are also interested in the pedagogical advantages and computational benefits of developing an analysis pipeline in-house, which, in addition to increasing efficiency, allows us to analytically marginalise nuisance parameters and provides confidence that these features are genuinely driven by the data.
| AI keywords | Bayesian inference; dataset tension; model selection |
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