Spotlight: efficient automated global optimization in rietveld analysis of diffraction data.

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight, building on established Rietveld packages such as MAUD, GSAS, or GSAS-II, which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters.

Personal Manuscript Acceptance Rates: Metrics for Self-assessment in Scholarship.

Introduction: There is no established baseline for how frequently clinical researchers personally encounter manuscript rejection, making it difficult for faculty to put their own evolving experience in context. The purpose of this study was to determine the feasibility of obtaining personal acceptance per submission (APS) and acceptance per manuscript (APM) rates for individual faculty members.

Methods: We performed a cross-section survey pilot study of clinical faculty members of two departments (family medicine and pediatrics), in one academic health center in the academic year 2017-2018.

Erratum: Tidal Deformabilities and Radii of Neutron Stars from the Observation of GW170817 [Phys. Rev. Lett. 121, 091102 (2018)].

This corrects the article DOI: 10.1103/PhysRevLett.121.

Tidal Deformabilities and Radii of Neutron Stars from the Observation of GW170817.

We use gravitational-wave observations of the binary neutron star merger GW170817 to explore the tidal deformabilities and radii of neutron stars. We perform a Bayesian parameter estimation with the source location and distance informed by electromagnetic observations. We also assume that the two stars have the same equation of state; we demonstrate that, for stars with masses comparable to the component masses of GW170817, this is effectively implemented by assuming that the stars' dimensionless tidal deformabilities are determined by the binary's mass ratio q by Λ_{1}/Λ_{2}=q^{6}.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background.

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources.