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Found 6 papers, 4 papers with code
Adiabatic waveforms for extreme mass-ratio inspirals via multivoice decomposition in time and frequency
We compute adiabatic waveforms for extreme mass-ratio inspirals (EMRIs) by "stitching" together a long inspiral waveform from a sequence of waveform snapshots, each of which corresponds to a particular geodesic orbit.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena
Rapid generation of fully relativistic extreme-mass-ratio-inspiral waveform templates for LISA data analysis
The future space mission LISA will observe a wealth of gravitational-wave sources at millihertz frequencies.
General Relativity and Quantum Cosmology
Learning Bayesian posteriors with neural networks for gravitational-wave inference
To do so, we train a deep neural network to take as input a signal + noise data set (drawn from the astrophysical source-parameter prior and the sampling distribution of detector noise), and to output a parametrized approximation of the corresponding posterior.
Sampling from manifold-restricted distributions using tangent bundle projections
A common problem in Bayesian inference is the sampling of target probability distributions at sufficient resolution and accuracy to estimate the probability density, and to compute credible regions.
Computation Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology Methodology
Reduced-order modeling with artificial neurons for gravitational-wave inference
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.
Fast methods for training Gaussian processes on large data sets
Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data.
