Dily Duan Yi Ong

We perform a Bayesian reanalysis of DESI DR2 data using nested sampling to compute evidence values across multiple cosmological models. When analysing DESI DR2 with Planck CMB data alone, the statistical preference for extended dark energy models is eliminated entirely with modest support for the standard Lambda-CDM model instead. A calibration issue in supernova data (DES-SN5YR) initially suggested a 3.07±0.10 sigma preference for dynamical dark energy, but this signal disappeared after correction. The analysis reveals a 2.95±0.04 sigma conflict between the uncorrected supernova calibration and DESI DR2 within the standard model. Bayesian model selection penalises complexity differently than frequentist approaches, producing weaker or opposite preferences compared to chi-squared-based analyses.

Observations have suggested that galactic outflows contain substantial amounts of dense and clumpy molecular gas, creating favourable conditions for igniting star formation. Indeed, theoretical models and hydrodynamical simulations have suggested that stars could form within galactic outflows, representing a new mode of star-formation that differs significantly from the typical star formation in star forming discs. In this paper, we examine 12 local galaxies with powerful Active Galactic Nuclei and high star-formation rate using spectroscopic data from the X-shooter spectrograph at the Very Large Telescope. We investigate the excitation mechanism and physical properties of these outflows via spatially resolved diagnostic diagrams (along with tests to rule out contribution by shocks and external photoionisation). Out of the seven galaxies with clearly detected outflows, we find robust evidence for star formation within the outflow of one galaxy (IRAS 20551-4250), with two additional...

The DESI collaboration reports a significant preference for a dynamic dark energy model (w0waCDM) over the cosmological constant (ΛCDM) when their data are combined with other frontier cosmological probes. We present a direct Bayesian model comparison using nested sampling to compute the Bayesian evidence, revealing a contrasting conclusion: for the key combination of the DESI DR2 BAO and the Planck CMB data, we find the Bayesian evidence modestly favours ΛCDM (log-Bayes factor ln B = -0.57±0.26), in contrast to the collaboration’s 3.1σ frequentist significance in favoring w0waCDM.

Bayesian inference is central to modern cosmology, yet comprehensive model comparison and tension quantification remain computationally prohibitive for many researchers. To address this, we release unimpeded, a publicly available Python library and data repository providing pre-computed nested sampling and MCMC chains. We apply this resource to conduct a systematic analysis across a grid of eight cosmological models, including ΛCDM and seven extensions, and 39 datasets, including individual probes and their pairwise combinations.

Bayesian inference is central to modern cosmology. While parameter estimation is achievable with unnormalised posteriors traditionally obtained via MCMC methods, comprehensive model comparison and tension quantification require Bayesian evidences and normalised posteriors, which remain computationally prohibitive for many researchers. To address this, we present unimpeded, a publicly available Python library and data repository providing DiRAC-funded pre-computed nested sampling and MCMC chains with their normalised posterior samples.