Adapting to noise distribution shifts in flow-based gravitational-wave inference

Y. Aso, Y. Michimura, K. Somiya, M. Ando, O. Miyakawa, T. Sekiguchi, D. Tatsumi, and H. Yamamoto (KAGRA), Interferometer design of the KAGRA gravitational wave detector, Phys. Rev. D 88

Original

2013

Cited alongside, same era.

J. Veitch and W. Del Pozzo, Analytic marginalisation of phase parameter, URL: https://dcc. ligo. org/LIGO-T1300326/public (2013)

2013

Cited alongside, same era.

D. P. Kingma and M. Welling, Auto-Encoding Variational Bayes, in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014) http://arxiv.org/abs/1312.6114v10

Original

2014

Cited alongside, same era.

D. J. Rezende, S. Mohamed, and D. Wierstra, Stochastic backpropagation and approximate inference in deep generative models, in International Conference on Machine Learning (2014) pp. 1278–1286, 1401.4082 [stat.ML]

Original

2014

Cited alongside, same era.

T. Littenberg and N. Cornish, Bayesline: Bayesian inference for spectral estimation of gravitational wave detector noise, Physical Review D 91

2014

Cited alongside, same era.

M. Hannam, P. Schmidt, A. Bohé, L. Haegel, S. Husa, F. Ohme, G. Pratten, and M. Pürrer, Simple Model of Complete Precessing Black-Hole-Binary Gravitational Waveforms, Phys. Rev. Lett. 113

Original

2014

Cited alongside, same era.

D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, (2014), arXiv:1412.6980 [cs.LG]

Original

2014

Cited alongside, same era.

A. Bohé et al. , Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors, Phys. Rev. D 95

Original

2017

Later among the works it cites.

LIGO Scientific Collaboration, LIGO Algorithm Library - LALSuite , free software (GPL) (2018)

2018

Later among the works it cites.

G. Ashton et al. , BILBY: A user-friendly Bayesian inference library for gravitational-wave astronomy, Astrophys. J. Suppl. 241

Original

2019

Later among the works it cites.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 , edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019) pp. 8024–8035

2019

Later among the works it cites.

E. Thrane and C. Talbot, An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models, Publ. Astron. Soc. Austral. 36

Original

2020

Later among the works it cites.

C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, nflows: normalizing flows in PyTorch (2020)

2020

Later among the works it cites.

M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, Real-Time Gravitational Wave Science with Neural Posterior Estimation, Phys. Rev. Lett. 127

Original

2021

Later among the works it cites.

M. Dax, S. R. Green, J. Gair, M. Deistler, B. Schölkopf, and J. H. Macke, Group equivariant neural posterior estimation, in International Conference on Learning Representations (ICLR 2022) (2022) arXiv:2111.13139 [cs.LG]

Original

2022

Closest in time.