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In recent years, a handful of “dark” binaries have been discovered with a nonluminous compact object. Astrometry and radial velocity measurements of the bright companion allow us to measure the post-supernova orbital elements of such a binary. In this paper, we develop a statistical formalism to use such measurements to infer the pre-supernova orbital elements, and the natal kick imparted by the supernova (SN). We apply this formalism to the recent discovery of an X-ray quiet binary with a black hole, VFTS 243, in the Large Magellanic Cloud. Assuming an isotropic, Maxwellian distribution on natal kicks and using broad agnostic mass priors, we find that kick velocity can be constrained toV_k< 72 km s⁻¹at 90% confidence. We find that a Blaauw kick cannot be ruled out, and that at least about 0.6M_⊙was lost during the supernova with 90% confidence. The pre-SN orbital separation is found to be robustly constrained to be around 0.3 au.

 

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

 

Abstract Upcoming LIGO–Virgo–KAGRA (LVK) observing runs are expected to detect a variety of inspiralling gravitational-wave (GW) events that come from black hole and neutron star binary mergers. Detection of noninspiral GW sources is also anticipated. We report the discovery of a new class of noninspiral GW sources—the end states of massive stars—that can produce the brightest simulated stochastic GW burst signal in the LVK bands known to date, and could be detectable in LVK run A+. Some dying massive stars launch bipolar relativistic jets, which inflate a turbulent energetic bubble—cocoon—inside of the star. We simulate such a system using state-of-the-art 3D general relativistic magnetohydrodynamic simulations and show that these cocoons emit quasi-isotropic GW emission in the LVK band, ∼10–100 Hz, over a characteristic jet activity timescale ∼10–100 s. Our first-principles simulations show that jets exhibit a wobbling behavior, in which case cocoon-powered GWs might be detected already in LVK run A+, but it is more likely that these GWs will be detected by the third-generation GW detectors with an estimated rate of ∼10 events yr −1 . The detection rate drops to ∼1% of that value if all jets were to feature a traditional axisymmetric structure instead of a wobble. Accompanied by electromagnetic emission from the energetic core-collapse supernova and the cocoon, we predict that collapsars are powerful multimessenger events. 

Abstract The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb , following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results. 

ABSTRACT Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) and Advanced Virgo have recently published the upper limit measurement of persistent directional stochastic gravitational-wave background (SGWB) based on data from their first and second observing runs. In this paper, we investigate whether a correlation exists between this maximal likelihood SGWB map and the electromagnetic (EM) tracers of matter structure in the Universe, such as galaxy number counts. The method we develop will improve the sensitivity of future searches for anisotropy in the SGWB and expand the use of SGWB anisotropy to probe the formation of structure in the Universe. In order to compute the cross-correlation, we used the spherical harmonic decomposition of SGWB in multiple frequency bands and converted them into pixel-based sky maps in healpix basis. For the EM part, we use the Sloan Digital Sky Survey alaxy catalogue and form healpix sky maps of galaxy number counts at the same angular resolution as the SGWB maps. We compute the pixel-based coherence between these SGWB and galaxy count maps. After evaluating our results in different SGWB frequency bands and in different galaxy redshift bins, we conclude that the coherence between the SGWB and galaxy number count maps is dominated by the null measurement noise in the SGWB maps, and therefore not statistically significant. We expect the results of this analysis to be significantly improved by using the more sensitive upcoming SGWB measurements based on the third observing run of Advanced LIGO and Advanced Virgo. 

Small, highly absorbing points are randomly present on the surfaces of the main interferometer optics in Advanced LIGO. The resulting nanometer scale thermo-elastic deformations and substrate lenses from these micron-scale absorbers significantly reduce the sensitivity of the interferometer directly though a reduction in the power-recycling gain and indirect interactions with the feedback control system. We review the expected surface deformation from point absorbers and provide a pedagogical description of the impact on power buildup in second generation gravitational wave detectors (dual-recycled Fabry–Perot Michelson interferometers). This analysis predicts that the power-dependent reduction in interferometer performance will significantly degrade maximum stored power by up to 50% and, hence, limit GW sensitivity, but it suggests system wide corrections that can be implemented in current and future GW detectors. This is particularly pressing given that future GW detectors call for an order of magnitude more stored power than currently used in Advanced LIGO in Observing Run 3. We briefly review strategies to mitigate the effects of point absorbers in current and future GW wave detectors to maximize the success of these enterprises.