Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis | Paper Citation Record

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This paper cites Schmitt et al.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Schmitt et al

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This paper cites holy grail.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis holy grail

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis black boxes

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This paper cites 2021YFC2203004), the National Natural Science Foundation of China (NSFC) (Grant Nos.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 2021YFC2203004), the National Natural Science Foundation of China (NSFC) (Grant Nos

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This paper cites Obser- vation of gravitational waves from a binary black hole merger.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Obser- vation of gravitational waves from a binary black hole merger

Reference 7

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Observation 76d1a92b-96a5-4972-8e3c-7fe77fa8066a · outbound

This paper cites Gwtc-2: Compact binary coalescences observed by ligo and virgo during the first half of the third observing run.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Gwtc-2: Compact binary coalescences observed by ligo and virgo during the first half of the third observing run

Reference 8

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This paper cites Abbott et al.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Abbott et al

Reference 9

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This paper cites Abbott et al.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Abbott et al

Reference 10

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Reference 11

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This paper cites Gravita- tional waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral waveform? Phys- ical Review D , 49(6):2658, 1994.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Gravita- tional waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral waveform? Phys- ical Review D , 49(6):2658, 1994

Reference 12

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Observation 1adfdb5a-1e3b-49fa-a0bf-c368e56869e7 · outbound

This paper cites Parame- ter estimation with gravitational waves.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Parame- ter estimation with gravitational waves

Reference 13

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Observation b3b1b459-eba8-40a7-9c26-579a6b500f6e · outbound

This paper cites An introduction to bayesian inference in gravitational-wave as- tronomy: parameter estimation, model selec- tion, and hierarchical models.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis An introduction to bayesian inference in gravitational-wave as- tronomy: parameter estimation, model selec- tion, and hierarchical models

Reference 14

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This paper cites AI for Science: An Emerging Agenda.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis AI for Science: An Emerging Agenda

Reference 15

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This paper cites Enhancing gravitational- wave science with machine learning.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Enhancing gravitational- wave science with machine learning

Reference 16

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This paper cites Review of artificial intelligence applications in astronom- ical data processing.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Review of artificial intelligence applications in astronom- ical data processing

Reference 17

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This paper cites Ap- plications of machine learning in gravitational wave research with current interferometric de- tectors, 2024.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Ap- plications of machine learning in gravitational wave research with current interferometric de- tectors, 2024

Reference 18

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Reference 19

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Observation 9ecede2e-f757-4be4-aa1f-133a55eae0f3 · outbound

This paper cites Bayesian parameter estima- tion using conditional variational autoencoders for gravitational-wave astronomy.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Bayesian parameter estima- tion using conditional variational autoencoders for gravitational-wave astronomy

Reference 20

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This paper cites Green, Christine Simpson, and Jonathan Gair.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Christine Simpson, and Jonathan Gair

Reference 21

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Observation dea1bdf7-e8d9-4a69-b908-87a9be6ef732 · outbound

This paper cites Complete parameter inference for gw150914 using deep learning.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Complete parameter inference for gw150914 using deep learning

Reference 22

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This paper cites Ashley Villar, and Joshua S.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Ashley Villar, and Joshua S

Reference 24

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This paper cites Gon¸ calves, David S.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Gon¸ calves, David S

Reference 25

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This paper cites Veitch, V.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Veitch, V

Reference 26

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This paper cites Lasky, Colm Talbot, Kendall Ackley, Sylvia Bis- coveanu, Qi Chu, Atul Divakarla, Paul J.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Lasky, Colm Talbot, Kendall Ackley, Sylvia Bis- coveanu, Qi Chu, Atul Divakarla, Paul J

Reference 27

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This paper cites Bayesian inference for compact binary coalescences with bilby: validation and application to the first ligo–virgo gravitational- wave transient catalogue.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Bayesian inference for compact binary coalescences with bilby: validation and application to the first ligo–virgo gravitational- wave transient catalogue

Reference 28

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This paper cites dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences

Reference 29

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This paper cites Katz, Sylvain Marsat, Alvin J.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Katz, Sylvain Marsat, Alvin J

Reference 30

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This paper cites Blum, Oscar E.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Blum, Oscar E

Reference 32

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This paper cites Varia- tional inference with normalizing flows.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Varia- tional inference with normalizing flows

Reference 33

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This paper cites Likelihood-free inference with emulator net- works.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Likelihood-free inference with emulator net- works

Reference 35

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This paper cites Flexible statis- tical inference for mechanistic models of neu- ral dynamics.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Flexible statis- tical inference for mechanistic models of neu- ral dynamics

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This paper cites Automatic posterior transfor- mation for likelihood-free inference.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Automatic posterior transfor- mation for likelihood-free inference

Reference 37

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This paper cites Approximating Likelihood Ratios with Calibrated Discriminative Classifiers.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Reference 38

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Likelihood-free inference by ratio estimation

Reference 39

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This paper cites Likelihood-free mcmc with amortized Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 25 approximate ratio estimators.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Likelihood-free mcmc with amortized Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 25 approximate ratio estimators

Reference 40

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This paper cites On contrastive learning for likelihood-free inference.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis On contrastive learning for likelihood-free inference

Reference 41

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Contrastive neural ratio estimation

Reference 42

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Bayesian synthetic likelihood

Reference 43

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This paper cites Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows

Reference 44

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This paper cites Bayesian in- ference using synthetic likelihood: asymp- totics and adjustments.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Bayesian in- ference using synthetic likelihood: asymp- totics and adjustments

Reference 45

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This paper cites Macke, and Bernhard Sch¨ olkopf.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Macke, and Bernhard Sch¨ olkopf

Reference 46

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Generative adversarial nets

Reference 48

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This paper cites NICE: Non-linear Independent Components Estimation.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis NICE: Non-linear Independent Components Estimation

Reference 49

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This paper cites Density estimation using Real NVP.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Density estimation using Real NVP

Reference 50

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This paper cites Masked autoregressive flow for density estimation.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Masked autoregressive flow for density estimation

Reference 51

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Neural autore- gressive flows

Reference 52

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This paper cites Neural ordi- nary differential equations.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Neural ordi- nary differential equations

Reference 53

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Sum-of-squares polynomial flow

Reference 54

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Neural spline flows

Reference 55

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This paper cites Uncon- strained monotonic neural networks.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Uncon- strained monotonic neural networks

Reference 56

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This paper cites Normalizing flows on tori and spheres.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Normalizing flows on tori and spheres

Reference 57

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This paper cites Deep transformation models: Tackling com- plex regression problems with neural network based transformation models.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Deep transformation models: Tackling com- plex regression problems with neural network based transformation models

Reference 58

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This paper cites Gaussianization flows.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Gaussianization flows

Reference 59

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This paper cites Normalizing flows for probabilistic modeling and infer- Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 26 ence.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Normalizing flows for probabilistic modeling and infer- Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 26 ence

Reference 60

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This paper cites Auto- encoding variational bayes, 2022.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Auto- encoding variational bayes, 2022

Reference 61

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This paper cites Variational autoencoders for efficient simulation-based inference.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Variational autoencoders for efficient simulation-based inference

Reference 62

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Unresolved cited work

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Reference 64

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This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 65

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This paper cites Building normalizing flows with stochastic interpolants.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Building normalizing flows with stochastic interpolants

Reference 66

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This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 67

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Albergo, Nicholas M

Reference 68

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Score-based generative mod- eling through stochastic differential equations

Reference 69

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This paper cites Improved techniques for training score-based generative models.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Improved techniques for training score-based generative models

Reference 70

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Denoising diffusion probabilistic models

Reference 71

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This paper cites Maximum likelihood training of score-based diffusion models.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Maximum likelihood training of score-based diffusion models

Reference 72

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This paper cites Deep un- supervised learning using nonequilibrium ther- modynamics.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Deep un- supervised learning using nonequilibrium ther- modynamics

Reference 73

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Improved techniques for training consistency models

Reference 75

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This paper cites Fast ε-free inference of simulation models with bayesian conditional density estimation.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Fast ε-free inference of simulation models with bayesian conditional density estimation

Reference 76

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Reference 77

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Jonathan Gair, Jakob H

Reference 78

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Jonathan Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis 27 Gair, Michael P¨ urrer, Jonas Wildberger, Jakob H

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:20.371413Z

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Jonathan Gair, Michael P¨ urrer, Jakob H

Reference 80

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This paper cites Green, Alexan- dre Toubiana, and Jonathan Gair.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Alexan- dre Toubiana, and Jonathan Gair

Reference 81

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Observation 1ba8de8c-2625-4a9a-b5b3-d8b3a836fd27 · outbound

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Coleman Miller, Maximilian Dax, Stephen R

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:20.623033Z

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Green, Jonathan Gair, Nihar Gupte, Michael P¨ urrer, Vivien Raymond, Jonas Wildberger, Jakob H

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:20.670910Z

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This paper cites Statistically-informed deep learning for gravitational wave parameter estimation.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Statistically-informed deep learning for gravitational wave parameter estimation

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:20.725784Z

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Premerger sky localization of gravitational waves from bi- nary neutron star mergers using deep learn- ing

Reference 85

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Rapid localization of gravitational wave sources from compact binary coalescences using deep learning

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T17:20:20.909731Z

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This paper cites Deep learning to detect gravitational waves from binary close encounters: Fast parameter estimation using normalizing flows.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Deep learning to detect gravitational waves from binary close encounters: Fast parameter estimation using normalizing flows

Reference 87

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Reference 88

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Observation a96ef5f9-eb9e-4cec-bf61-7483f9561c32 · outbound

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Reference 89

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Observation bb62a9b2-96fa-4484-9b5c-6b0fa523b65b · outbound

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Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:38.317782Z

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Observation 9b305f17-627e-4bd5-bd85-7efe1af3bf5c · outbound

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Unresolved cited work

Reference 91

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Observation 14fec603-0248-4219-b37d-ee2093ed0237 · outbound

This paper cites an unresolved cited work.

Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:20:38.098600Z

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Observation f045f4a7-c917-464e-927a-c7bd1d4b2f12 · outbound

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Williams, John Veitch, and Chris Messenger

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:38.032942Z

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Observation 439aa0b9-a19c-441b-813c-c4323c1a4cc6 · outbound

This paper cites Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation, and sky localization.

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Reference 94

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Observation f6a67bf6-0793-42ed-b360-dcdcb971733d · outbound

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Unresolved cited work

Reference 95

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Observation 959d84fc-69bc-4d6d-a1eb-b0d6aaebb086 · outbound

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Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:20:37.779972Z

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Observation 9c3710ce-17fb-40af-bcb8-1447d0657ad7 · outbound

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Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.676274Z

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Observation 0b6450bd-f128-4e1e-af11-b2990d0d8c95 · outbound

This paper cites Estimating the warm dark mat- ter mass from strong lensing images with truncated marginal neural ratio estimation.

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Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.573605Z

Source-reported events for the cited work

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Observation 90685499-acb9-40c4-b4b8-510b47eaf998 · outbound

This paper cites Sicret: Supernova ia cos- mology with truncated marginal neural ratio estimation.

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Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.515081Z

Source-reported events for the cited work

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Observation 2b83e74f-7e6a-4c4a-9f4c-0ef44579dc8f · outbound

This paper cites The effect of the perturber population on subhalo measure- ments in strong gravitational lenses.

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.416895Z

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Observation 393740f6-0586-4470-9506-c7ef5efbafe7 · outbound

This paper cites Detection is truncation: studying source populations with truncated marginal neural ratio estimation, 2022.

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Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.300194Z

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Observation 5fac27f4-88d4-45f9-ba22-f706f953fb1d · outbound

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Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:37.162844Z

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Observation eb4bfbb7-b9ff-403e-8925-7b9ac2f6c3a7 · outbound

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Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:20:36.999314Z

Source-reported events for the cited work

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