Physics & Astronomy Nuclear Science Seminar

Title: From chiral effective field theory to perturbative QCD: A Bayesian model mixing approach to neutron star matter

Abstract: Constraining the equation of state (EOS) of strongly interacting, dense matter is the focus of significant experimental, observational, and theoretical effort. While chiral effective field theory (EFT) can describe the EOS between the typical densities of nuclei and those in the outer cores of neutron stars, perturbative QCD (pQCD) can be applied to properties of deconfined quark matter, both with quantified theoretical uncertainties.

However, describing the full range of densities in between with a single EOS that has well-quantified uncertainties is a challenging problem. Bayesian model mixing (BMM) can help bridge the gap between the two theories.

In this talk, I will present a BMM framework that can combine EOS constraints from different density regions in a principled way to construct a globally predictive, composite EOS model based on Gaussian processes (GPs). I will discuss applications of this BMM framework to the EOS and structure of neutron stars, as well as the statistical uncertainty quantification of the underlying microscopic EOS calculations.

Date:
-
Location:
CP 179
Event Series:

Statistics Seminar

Title: Doubly robust estimation and inference for a log-concave counterfactual density

Abstract: We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we study inference for the counterfactual density functions and contrasts thereof, which can provide more nuanced information than counterfactual means and the average treatment effect. We impose the shape-constraint of log-concavity, a type of unimodality constraint, on the counterfactual densities, and then develop doubly robust estimators of the log-concave counterfactual density based on augmented inverse-probability weighted pseudo-outcomes. We provide conditions under which the estimator is consistent in various global metrics. We also develop asymptotically valid pointwise confidence intervals for the counterfactual density functions and differences and ratios thereof, which serve as a building block for more comprehensive analyses of distributional differences.

Date:
-
Location:
MDS 220
Subscribe to