arXiv research

Simulation-based inference speeds up gravitational wave data analysis.

We seek to achieve the Holy Grail of Bayesian inference for gravitational-wave astronomy: using deep-learning techniques to instantly produce the posterior $p(θ|D)$ for the source parameters $θ$, given the detector data $D$. To do so, we train a deep neural network to take as input a signal + noise data set (drawn from…

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes. However, to date, these methods have not …

Eryn is a versatile MCMC package for Bayesian inference.

Study proves interaction of three impulsive gravitational waves, showing local solution and Lipschitz continuity.

Gravitational wave memory increases faster than Brownian motion in early universe and astrophysical sources.

Gravitational waves are predicted by the general theory of relativity. In [6] D. Christodoulou showed that gravitational waves have a nonlinear memory. We proved in [3] that the electromagnetic field contributes at highest order to the nonlinear memory effect of gravitational waves. In the present paper, we study this …

Study examines the training process of an unsupervised learning model for detecting gravitational-wave transient noise.

Deep learning helps remove secondary $B$-mode polarization to detect primordial gravitational waves.

New memory effect discovered in gravitational wave behavior.

Unsupervised learning classifies transient noise in gravitational wave detectors.

New magnetic memory effects found in gravitational waves and memory.

Quantum machine learning improves pulsar classification in radio astronomy.

Neural networks improve gravitational-wave parameter estimation.

TIER uses extended strain data to improve gravitational wave detection sensitivity.

Deep learning predicts nuclear equation of state from rotating core collapse GW signals.

Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images. We pursue an alternative approach, in which waveforms are first represented as weighted sums over reduced bases (reduced-order modeling)…

Study shows how electromagnetic and gravitational waves can form trapped surfaces.

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy require…

Deep learning speeds up gravitational wave analysis.

New geometrization of gravitational wave phase space.

Machine learning classifies gravitational wave signals to test General Relativity.

Developing a visual platform for faster astronomical source cataloging.

Deep learning improves GW signal detection efficiency and robustness.

A textbook on statistical machine learning for astronomy.

New method for efficient inference over complex parameter spaces.

LSVM with EBT reduces quasar detection errors by 10x.

Stability of timelike Ricci bounds in low-regularity spacetimes.

Kolmogorov-Arnold network improves GW catalog posterior construction.

Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.

We review recent work on the Einstein equations of general relativity when the curvature is defined in a weak sense. Weakly regular spacetimes are constructed, in which impulsive gravitational waves, as well as shock waves, propagate.

The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian noise transients known as glitches, since their high occurrence rate in L…

Paper presents a workflow for reliable unsupervised learning in science.

We define the notion of geodesic completeness for semi-Riemannian metrics of low regularity in the framework of the geometric theory of generalized functions. We then show completeness of a wide class of impulsive gravitational wave space-times.

Second paper in series solves Einstein vacuum equations for three impulsive waves.

Paper proves trapped surface formation for EMCSF system without symmetry assumptions.

We prove that for spacetimes solving the Einstein-Maxwell (EM) equations, the electromagnetic field contributes at highest order to the nonlinear memory effect of gravitational waves. In [5] D. Christodoulou showed that gravitational waves have a nonlinear memory. He discussed how this effect can be measured as a perma…

We consider restrictions placed by geodesic completeness on spacetimes possessing a null parallel vector field, the so-called Brinkmann spacetimes. This class of spacetimes includes important idealized gravitational wave models in General Relativity, namely the plane-fronted waves with parallel rays, or pp-waves, which…

Flatly Foliated Relativity (FFR) is a new theory which conceptually lies between Special Relativity (SR) and General Relativity (GR), in which spacetime is foliated by flat Euclidean spaces. While GR is based on the idea that "matter curves spacetime", FFR is based on the idea that "matter curves spacetime, but not spa…

New definition of angular momentum avoids supertranslation ambiguity.

Unified approach to constructing integrable systems using Stäckel lifts.

Estimates high-dimensional posterior densities by marginal distributions and neural networks.

Ehlers-Kundt conjecture is a physical assertion about the fundamental role of plane waves for the description of gravitational waves. Mathematically, it becomes equivalent to a problem on the Euclidean plane ${\mathbb R}^2$ with a very simple formulation in Classical Mechanics: given a non-necessarily autonomous potent…

Novel method reduces costly model evaluations in inference problems.

Machine learning models classify celestial objects like pulsars and black holes.

GNPE improves inference for astrophysical systems.

A classical problem in general relativity is the Cauchy problem for the linearised Einstein equation (the initial value problem for gravitational waves) on a globally hyperbolic vacuum spacetime. A well-known result is that it is uniquely solvable up to gauge solutions, given initial data on a spacelike Cauchy hypersur…

Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically identify, classify, segment and deblend various astronomical objects. In this paper, w…