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Found 9 papers, 6 papers with code
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy.
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence.
Group equivariant neural posterior estimation
We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data.
Real-time gravitational-wave science with neural posterior estimation
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning.
Complete parameter inference for GW150914 using deep learning
By training with the detector noise power spectral density estimated at the time of GW150914, and conditioning on the event strain data, we use the neural network to generate accurate posterior samples consistent with analyses using conventional sampling techniques.
Gravitational-wave parameter estimation with autoregressive neural network flows
We introduce the use of autoregressive normalizing flows for rapid likelihood-free inference of binary black hole system parameters from gravitational-wave data with deep neural networks.
STROOPWAFEL: Simulating rare outcomes from astrophysical populations, with application to gravitational-wave sources
We implement the algorithm in the binary population synthesis code COMPAS, and compare the efficiency of our implementation to the standard method of Monte Carlo sampling from the birth probability distributions.
High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Data Analysis, Statistics and Probability
A Precessing Numerical Relativity Waveform Surrogate Model for Binary Black Holes: A Gaussian Process Regression Approach
Numerical relativity simulations are, however, computationally expensive, leading to the need for a surrogate model which can predict waveform signals in regions of the physical parameter space which have not been probed directly by simulation.
General Relativity and Quantum Cosmology Data Analysis, Statistics and Probability
Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
