Related papers: Dirichlet Process Gaussian-mixture model: An appli…
Probabilistic mixture models are recognized as effective tools for unsupervised outlier detection owing to their interpretability and global characteristics. Among these, Dirichlet process mixture models stand out as a strong alternative to…
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first…
Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 non-retracted candidates were identified and publicly announced in near real time. Gravitational waves from binary…
The problem of reconstructing the sky position of compact binary coalescences detected via gravitational waves is a central one for future observations with the ground-based network of gravitational-wave laser interferometers, such as…
Inferring the intrinsic population of compact binary mergers is complicated by detector selection biases and measurement uncertainties. Traditional parametric methods are limited by the need to presuppose functional forms, introducing…
GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts…
We present a new framework to detect various types of variable objects within massive astronomical time-series data. Assuming that the dominant population of objects is non-variable, we find outliers from this population by using a…
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating…
As the ground-based gravitational-wave telescopes LIGO, Virgo, and GEO 600 approach the era of first detections, we review the current knowledge of the coalescence rates and the mass and spin distributions of merging neutron-star and…
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized…
The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers…
We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper…
Accurate sky localization is essential for gravitational-wave (GW) astronomy, particularly for multimessenger follow-up and host galaxy identification. For strongly lensed GW events, achieving localization at the level of $\sim…
Most gravitational-wave signals from binary neutron star coalescences are too weak to be individually resolved with current detectors. We demonstrate how to extract a population of sub-threshold binary neutron star signals using Bayesian…
The Laser Interferometer Gravitational wave Observatory (LIGO) and Virgo, advanced ground-based gravitational-wave detectors, will begin collecting science data in 2015. With first detections expected to follow, it is important to quantify…
Multimessenger astronomy incorporating gravitational radiation is a new and exciting field that will potentially provide significant results and exciting challenges in the near future. With advanced interferometric gravitational wave…
We describe how the six planned detectors (2 LIGOs, VIRGO, GEO, AIGO, TAMA) can be used to perform coincidence experiments for the detection of broadband signals from either coalescing compact binaries or burst sources. We make comparisons…
Clustering measurements of Gravitational Wave (GW) mergers in Luminosity Distance Space can be used in the future as a powerful tool for Cosmology. We consider tomographic measurements of the Angular Power Spectrum of mergers both in an…
Pre-merger localization of binary neutron stars (BNSs) is one of the most important scientific goals for the third generation (3G) gravitational wave (GW) detectors. It will enable the electromagnetic observation of the whole process of BNS…
One unanswered question about the binary neutron star coalescence GW170817 is the nature of its post-merger remnant. A previous search for post-merger gravitational waves targeted high-frequency signals from a possible neutron star remnant…