Example of negative energy density in a classical electron model.

Classical models of the electron have been predicted to have negative rest energy density in certain regions. Using the model of the electron by Blinder we show that there are regions containing negative energy density, although the integral of the energy density over all space gives the electron rest mass. If the spin of the electron is ignored, then all regions of space have positive energy density with the Blinder model. The existence of Poincaré stress for the Blinder model is also demonstrated. The classical model for the electron discussed here admittedly does not involve quantum electrodynamics, where the infinite self energy is made finite with renormalization methods.

Measuring the Hubble constant with a sample of kilonovae.

Kilonovae produced by the coalescence of compact binaries with at least one neutron star are promising standard sirens for an independent measurement of the Hubble constant (H0). Through their detection via follow-up of gravitational-wave (GW), short gamma-ray bursts (sGRBs) or optical surveys, a large sample of kilonovae (even without GW data) can be used for H0 contraints. Here, we show measurement of H0 using light curves associated with four sGRBs, assuming these are attributable to kilonovae, combined with GW170817. Including a systematic uncertainty on the models that is as large as the statistical ones, we find [Formula: see text] and [Formula: see text] for two different kilonova models that are consistent with the local and inverse-distance ladder measurements. For a given model, this measurement is about a factor of 2-3 more precise than the standard-siren measurement for GW170817 using only GWs.

Stochastic gravitational wave backgrounds.

A stochastic background of gravitational waves could be created by the superposition of a large number of independent sources. The physical processes occurring at the earliest moments of the universe certainly created a stochastic background that exists, at some level, today. This is analogous to the cosmic microwave background, which is an electromagnetic record of the early universe. The recent observations of gravitational waves by the Advanced LIGO and Advanced Virgo detectors imply that there is also a stochastic background that has been created by binary black hole and binary neutron star mergers over the history of the universe. Whether the stochastic background is observed directly, or upper limits placed on it in specific frequency bands, important astrophysical and cosmological statements about it can be made. This review will summarize the current state of research of the stochastic background, from the sources of these gravitational waves to the current methods used to observe them.

Gravitational waves: search results, data analysis and parameter estimation: Amaldi 10 Parallel session C2.

The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity.

Fast Bayesian reconstruction of chaotic dynamical systems via extended Kalman filtering.

We present an improved Markov chain Monte Carlo (MCMC) algorithm for posterior computation in chaotic dynamical systems. Recent Bayesian approaches to estimate the parameters of chaotic maps have used the Gibbs sampler which exhibits slow convergence due to high posterior correlations. Using the extended Kalman filter to compute the likelihood function by integrating out all unknown system states, we obtain a very efficient MCMC technique. We compare the new algorithm to the Gibbs sampler using the logistic, the tent, and the Moran-Ricker maps as applications, measuring the performance in terms of CPU and integrated autocorrelation time.