Normalizing Flows as an Avenue to Studying Overlapping Gravitational Wave Signals.

Because of its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data analysis. In this Letter, we focus on a challenging problem faced by third-generation detectors: parameter inference for overlapping signals.

Constraining neutron-star matter with microscopic and macroscopic collisions.

Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not probed only in astrophysical observations, but also in terrestrial heavy-ion collision experiments.

A study of whether video scoring is a reliable option for blinded scoring of the Gross Motor Function Measure-88.

Objective: To investigate the agreement between live and video scores of the Gross Motor Function Measure-88.

Design: Reliability study.

Subjects: Forty children with bilateral spastic cerebral palsy.

A clinical decision framework for the identification of main problems and treatment goals for ambulant children with bilateral spastic cerebral palsy.

The primary aim of the study was to investigate how a clinical decision process based on the International Classification of Function, Disability and Health (ICF) and the Hypothesis-Oriented Algorithm for Clinicians (HOAC-II) can contribute to a reliable identification of main problems in ambulant children with cerebral palsy (CP). As a secondary aim, to evaluate how the additional information from three-dimensional gait analysis (3DGA) can influence the reliability. Twenty-two physical therapists individually defined the main problems and specific goals of eight children with bilateral spastic CP.

Demonstrating the feasibility of probing the neutron-star equation of state with second-generation gravitational-wave detectors.

Fisher matrix and related studies have suggested that, with second-generation gravitational-wave detectors, it may be possible to infer the equation of state of neutron stars using tidal effects in a binary inspiral. Here, we present the first fully Bayesian investigation of this problem. We simulate a realistic data analysis setting by performing a series of numerical experiments of binary neutron-star signals hidden in detector noise, assuming the projected final design sensitivity of the Advanced LIGO-Virgo network.