First-year DESI BAO data are consistent with flat LambdaCDM and, when combined with CMB, show a 2.5-3.9 sigma preference for evolving dark energy (w0 > -1, wa < 0) that strengthens with certain supernova datasets.
Neal, Taking Bigger Metropolis Steps by Dragging Fast Variables , arXiv Mathematics e-prints (2005) math/0502099 [ math/0502099]
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abstract
I show how Markov chain sampling with the Metropolis-Hastings algorithm can be modified so as to take bigger steps when the distribution being sampled from has the characteristic that its density can be quickly recomputed for a new point if this point differs from a previous point only with respect to a subset of 'fast' variables. I show empirically that when using this method, the efficiency of sampling for the remaining 'slow' variables can approach what would be possible using Metropolis updates based on the marginal distribution for the slow variables.
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Using idealized synthetic data, knowing the true continuum in Lyα forest auto- and cross-correlations reduces uncertainties on the AP parameter and Ω_m by ~10%, with extension to 240 h^{-1}Mpc scales adding up to ~15% further improvement equivalent to a 40% larger survey area.
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Probing the limits of cosmological information from the Lyman-$\alpha$ forest 2-point correlation functions
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