We perform a statistical standard siren analysis of GW170817. Our analysis does not utilize knowledge of NGC 4993 as the unique host galaxy of the optical counterpart to GW170817. Instead, we consider each galaxy within the GW170817 localization region as a potential host; combining the redshifts from all of the galaxies with the distance estimate from GW170817 provides an estimate of the Hubble constant, H ₀. Considering all galaxies brighter than as equally likely to host a binary neutron star merger, we find km s⁻¹ Mpc⁻¹ (maximum a posteriori and 68.3% highest density posterior interval; assuming a flat H ₀ prior in the range km s⁻¹ Mpc⁻¹). We explore the dependence of our results on the thresholds by which galaxies are included in our sample, and we show that weighting the host galaxies by stellar mass or star formation rate provides entirely consistent results with potentially tighter constraints. By applying the method to simulated gravitational-wave events and a realistic galaxy catalog we show that, because of the small localization volume, this statistical standard siren analysis of GW170817 provides an unusually informative (top 10%) constraint. Under optimistic assumptions for galaxy completeness and redshift uncertainty, we find that dark binary neutron star measurements of H ₀ will converge as , where N is the number of sources. While these statistical estimates are inferior to the value from the counterpart standard siren measurement utilizing NGC 4993 as the unique host, km s⁻¹ Mpc⁻¹ (determined from the same publicly available data), our analysis is a proof-of-principle demonstration of the statistical approach first proposed by Bernard Schutz over 30 yr ago.

We describe directed searches for continuous gravitational waves (GWs) from 16 well-localized candidate neutron stars, assuming none of the stars has a binary companion. The searches were directed toward 15 supernova remnants and Fomalhaut b, a directly imaged extrasolar planet candidate that has been suggested to be a nearby old neutron star. Each search covered a broad band of frequencies and first and second time derivatives. After coherently integrating spans of data from the first Advanced LIGO observing run of 3.5–53.7 days per search, applying data-based vetoes, and discounting known instrumental artifacts, we found no astrophysical signals. We set upper limits on intrinsic GW strain as strict as 1 × 10⁻²⁵, fiducial neutron star ellipticity as strict as 2 × 10⁻⁹, and fiducial r-mode amplitude as strict as 3 × 10⁻⁸.

Astrophysical sources of gravitational waves, such as binary neutron star and black hole mergers or core-collapse supernovae, can drive relativistic outflows, giving rise to non-thermal high-energy emission. High-energy neutrinos are signatures of such outflows. The detection of gravitational waves and high-energy neutrinos from common sources could help establish the connection between the dynamics of the progenitor and the properties of the outflow. We searched for associated emission of gravitational waves and high-energy neutrinos from astrophysical transients with minimal assumptions using data from Advanced LIGO from its first observing run O1, and data from the Antares and IceCube neutrino observatories from the same time period. We focused on candidate events whose astrophysical origins could not be determined from a single messenger. We found no significant coincident candidate, which we used to constrain the rate density of astrophysical sources dependent on their gravitational-wave and neutrino emission processes.

We use a key concept of the continuous-time random walk formalism, i.e., continuous and fluctuating interevent times in which mutual dependence is taken into account, to model market fluctuation data when traders experience excessive (or superthreshold) losses or excessive (or superthreshold) profits. We analytically derive a class of “superstatistics” that accurately model empirical market activity data supplied by Bogachev, Ludescher, Tsallis, and Bunde that exhibit transition thresholds. We measure the interevent times between excessive losses and excessive profits and use the mean interevent discrete (or step) time as a control variable to derive a universal description of empirical data collapse. Our dominant superstatistic value is a power-law corrected by the lower incomplete gamma function, which asymptotically tends toward robustness but initially gives an exponential. We find that the scaling shape exponent that drives our superstatistics subordinates itself and a “superscaling” configuration emerges. Thanks to the Weibull copula function, our approach reproduces the empirically proven dependence between successive interevent times. We also use the approach to calculate a dynamic risk function and hence the dynamic VaR, which is significant in financial risk analysis. Our results indicate that there is a functional (but not literal) balance between excessive profits and excessive losses that can be described using the same body of superstatistics but different calibration values and driving parameters. We also extend our original approach to cover empirical seismic activity data (e.g., given by Corral), the interevent times of which range from minutes to years. Superpositioned superstatistics is another class of superstatistics that protects power-law behavior both for short- and long-time behaviors. These behaviors describe well the collapse of seismic activity data and capture so-called volatility clustering phenomena.

Droplet coalescence is an important process in nature and various technologies (e.g., inkjet printing). Here, we unveil the surfactant mass transport mechanism and report on several major differences in the coalescence of surfactant-laden droplets as compared to pure water droplets by means of molecular dynamics simulation of a coarse-grained model. Large-scale changes to bridge growth dynamics are identified, such as the lack of multiple thermally excited precursors, attenuated collective excitations after contact, slowing down in the inertial regime due to aggregate-induced rigidity and reduced water flow, and a slowing down in the coalescence rate (deceleration) when surfactant concentration increases, while at the same time, we also confirm the existence of an initial thermal, and a power-law, inertial, regime of the bridge growth dynamics in both the pure and the surfactant-laden droplets. Thus, we unveil the key mechanisms in one of the fundamental topological processes of liquid droplets containing surfactant, which is crucial in relevant technologies.