anesthetic: nested sampling visualisation
read the original abstract
anesthetic is a Python package for processing nested sampling runs, and will be useful for any scientist or statistician who uses nested sampling software. anesthetic unifies many existing tools and techniques in an extensible framework that is intuitive for users familiar with the standard Python packages, namely NumPy, SciPy, Matplotlib and pandas.
This paper has not been read by Pith yet.
Forward citations
Cited by 5 Pith papers
-
DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations
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.
-
Numerical polology: towards next-generation model-building for cosmology
Numerical polology framework samples coupling space to discover ghost-free tensor field theories up to rank three for cosmology, then applies resulting priors to black hole superradiance, dynamical dark energy, and GW data.
-
Global structure of the time delay likelihood
Time delay likelihoods modeled with Gaussian processes develop a boundary-driven W-shape with a global maximum at the true delay and rises at observation window edges, misleading nested sampling and biasing H0 high.
-
Reconstructing dark energy with fewer assumptions
Bin-wise uncorrelated reconstruction from DESI/SDSS BAO and Pantheon+/Union3.1/DES-Dovekie supernovae yields dark energy density peaking then declining and equation of state oscillating with phantom crossing near z~0....
-
Extended Dark Energy analysis using DESI DR2 BAO measurements
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.