Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
Bayesian Methods in Cosmology
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abstract
These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level starts from elementary notions, without assuming any previous knowledge of statistical methods, and then progresses to more advanced, research-level topics. After an introduction to the importance of statistical inference for the physical sciences, elementary notions of probability theory and inference are introduced and explained. Bayesian methods are then presented, starting from the meaning of Bayes Theorem and its use as inferential engine, including a discussion on priors and posterior distributions. Numerical methods for generating samples from arbitrary posteriors (including Markov Chain Monte Carlo and Nested Sampling) are then covered. The last section deals with the topic of Bayesian model selection and how it is used to assess the performance of models, and contrasts it with the classical p-value approach. A series of exercises of various levels of difficulty are designed to further the understanding of the theoretical material, including fully worked out solutions for most of them.
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Flexible dark energy equation of state parametrizations mildly favor dynamical phantom-like behavior at z~1-2, but the preference is only ~2 sigma and sensitive to the model form and extrapolation.
CLOE pipeline produces forecasts showing Euclid can reach FoM >400 for dark energy w0 and wa by combining primary probes on synthetic data.
A matter bounce model driven by a scalar field with exponential potential fits Planck 2018 CMB data comparably to inflationary ΛCDM, with the potential slope λ directly setting the scalar spectral index ns.
Two three-parameter extensions of the mAH dark energy parametrization are compared to LambdaCDM, wCDM, CPL and others using CMB, DESI BAO, H(z), RSD and three SNIa samples, yielding Delta chi-squared improvements of 6-38 and 2-5 sigma tensions with LambdaCDM.
citing papers explorer
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Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference
Simulation-based inference on Big Sobol Sequence halos at z=0.5 shows CMD+MFs improves σ8 and Ωm precision by ~27% over MFs alone and outperforms PS by ~45% in mass-selected samples at matched scales.
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Probing the sensitivity of dark energy dynamics to equation of state parametrization flexibility
Flexible dark energy equation of state parametrizations mildly favor dynamical phantom-like behavior at z~1-2, but the preference is only ~2 sigma and sensitive to the model form and extrapolation.
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Euclid preparation. XCVI. Cosmology Likelihood for Observables in Euclid (CLOE). 3. Inference and Forecasts
CLOE pipeline produces forecasts showing Euclid can reach FoM >400 for dark energy w0 and wa by combining primary probes on synthetic data.
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Single field matter bounce with dark energy era: comparison with CMB Planck 2018 data and best fit parameters
A matter bounce model driven by a scalar field with exponential potential fits Planck 2018 CMB data comparably to inflationary ΛCDM, with the potential slope λ directly setting the scalar spectral index ns.
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Beyond CPL: Evidence for dynamical dark energy in three-parameter models
Two three-parameter extensions of the mAH dark energy parametrization are compared to LambdaCDM, wCDM, CPL and others using CMB, DESI BAO, H(z), RSD and three SNIa samples, yielding Delta chi-squared improvements of 6-38 and 2-5 sigma tensions with LambdaCDM.