Neural importance sampling for rapid and reliable gravitational-wave inference
We combine amortized neural posterior estimation with importance sampling for fast and
accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian …
accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian …
Normalizing flows as an avenue to studying overlapping gravitational wave signals
J Langendorff, A Kolmus, J Janquart… - Physical Review Letters, 2023 - APS
Because of its speed after training, machine learning is often envisaged as a solution to a
manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been …
manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been …
Convolutional neural network for gravitational-wave early alert: Going down in frequency
We present here the latest development of a machine-learning pipeline for premerger alerts
from gravitational waves coming from binary neutron stars (BNSs). This work starts from the …
from gravitational waves coming from binary neutron stars (BNSs). This work starts from the …
Analyses of overlapping gravitational wave signals using hierarchical subtraction and joint parameter estimation
J Janquart, T Baka, A Samajdar… - Monthly Notices of …, 2023 - academic.oup.com
In the coming years, third-generation detectors such as Einstein Telescope and Cosmic
Explorer will enter the network of ground-based gravitational-wave detectors. Their current …
Explorer will enter the network of ground-based gravitational-wave detectors. Their current …
Parameter estimation methods for analyzing overlapping gravitational wave signals in the third-generation detector era
J Janquart, T Baka, A Samajdar, T Dietrich… - arXiv preprint arXiv …, 2022 - arxiv.org
In the coming years, third-generation detectors such as Einstein Telescope and Cosmic
Explorer will enter the network of ground-based gravitational-wave detectors. Their current …
Explorer will enter the network of ground-based gravitational-wave detectors. Their current …
Using deep learning to denoise and detect gravitational waves
CL Ma, SQ Li, Z Cao, M Jia - Physical Review D, 2024 - APS
We have upgraded the MSNRnet framework to MSNRnet-2 by refining the training strategy,
drawing inspiration from generative adversarial networks for data generation. The …
drawing inspiration from generative adversarial networks for data generation. The …
Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
S Sasaoka, N Koyama, D Dominguez, Y Sakai… - Physical Review D, 2024 - APS
Recent advancements in gravitational wave astronomy have seen the application of
convolutional neural networks (CNNs) in signal detection from compact binary …
convolutional neural networks (CNNs) in signal detection from compact binary …
Tuning neural posterior estimation for gravitational wave inference
A Kolmus, J Janquart, T Baka, T van Laarhoven… - arXiv preprint arXiv …, 2024 - arxiv.org
Modern simulation-based inference techniques use neural networks to solve inverse
problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a …
problems efficiently. One notable strategy is neural posterior estimation (NPE), wherein a …
Gravitational Wave Mixture Separation for Future Gravitational Wave Observatories Utilizing Deep Learning
C Ma, W Zhou, Z Cao - arXiv preprint arXiv:2407.13239, 2024 - arxiv.org
Future GW observatories, such as the Einstein Telescope (ET), are expected to detect
gravitational wave signals, some of which are likely to overlap with each other. This overlap …
gravitational wave signals, some of which are likely to overlap with each other. This overlap …
Robust parameter estimation within minutes on gravitational wave signals from binary neutron star inspirals
T Wouters, PTH Pang, T Dietrich… - arXiv preprint arXiv …, 2024 - arxiv.org
The gravitational waves emitted by binary neutron star inspirals contain information on
nuclear matter above saturation density. However, extracting this information and …
nuclear matter above saturation density. However, extracting this information and …