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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 posterior using neural networks, and then attach importance…

We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual…

广义相对论与量子宇宙学 · 物理学 2024-04-09 Konstantin Leyde , Stephen R. Green , Alexandre Toubiana , Jonathan Gair

The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy, emphasizing the need for rapid and detailed parameter estimation and population-level analyses. Traditional…

广义相对论与量子宇宙学 · 物理学 2025-07-22 Bo Liang , He Wang

As gravitational-wave catalogs grow, they will become increasingly computationally expensive to analyze in their entirety, especially when inferring astrophysical source populations with high-dimensional, flexible models. Bayesian…

天体物理仪器与方法 · 物理学 2026-02-25 Noah E. Wolfe , Matthew Mould , John Veitch , Salvatore Vitale

Comparing population-synthesis models to the results of hierarchical Bayesian inference in gravitational-wave astronomy requires a careful understanding of the domain of validity of the models fitted to data. This comparison is usually done…

Gravitational-wave events are interpreted in terms of Bayesian posteriors for their source properties inferred under unphysical reference priors. Though these parameter estimates are important intermediate data products for downstream…

广义相对论与量子宇宙学 · 物理学 2026-04-20 Matthew Mould , Rodrigo Tenorio , Davide Gerosa

We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first…

广义相对论与量子宇宙学 · 物理学 2023-05-31 Maximilian Dax , Stephen R. Green , Jonathan Gair , Jakob H. Macke , Alessandra Buonanno , Bernhard Schölkopf

Gravitational waves (GWs) signals detected by the LIGO/Virgo/KAGRA collaboration might be sourced (partly) by the merges of primordial black holes (PBHs). The conventional hierarchical Bayesian inference methods can allow us to study…

广义相对论与量子宇宙学 · 物理学 2026-05-26 Hai-Long Huang , Zhan-He Wang , Qing-Yu Lan , Jun-Qian Jiang , Jibin He , Yu-Tong Wang , Jun Zhang , Yun-Song Piao

Since the first direct detection of gravitational waves by the LIGO--Virgo collaboration in 2015, the size of the gravitational-wave transient catalog has grown to nearly 100 events, with more than as many observed during the ongoing fourth…

天体物理仪器与方法 · 物理学 2024-09-24 Colm Talbot , Amanda Farah , Shanika Galaudage , Jacob Golomb , Hui Tong

We present a novel machine-learning approach to estimate selection effects in gravitational-wave observations. Using techniques similar to those commonly employed in image classification and pattern recognition, we train a series of…

高能天体物理现象 · 物理学 2020-11-18 Davide Gerosa , Geraint Pratten , Alberto Vecchio

Catalogs of stellar-mass compact binary systems detected by ground-based gravitational-wave instruments (such as Advanced LIGO and Advanced Virgo) will offer insights into the demographics of progenitor systems and the physics guiding…

高能天体物理现象 · 物理学 2018-10-24 Stephen R. Taylor , Davide Gerosa

Primordial black holes (PBHs), envisioned as a compelling dark matter candidate and a window onto early-Universe physics, may contribute to the part of the gravitational-wave (GW) signals detected by the LIGO-Virgo-KAGRA network.…

广义相对论与量子宇宙学 · 物理学 2025-05-22 Jun-Qian Jiang , Hai-Long Huang , Jibin He , Yu-Tong Wang , Yun-Song Piao

Bayesian inference is the workhorse of gravitational-wave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact…

天体物理仪器与方法 · 物理学 2019-09-04 Colm Talbot , Rory Smith , Eric Thrane , Gregory B. Poole

The LIGO and Virgo gravitational-wave observatories have detected many exciting events over the past five years. As the rate of detections grows with detector sensitivity, this poses a growing computational challenge for data analysis. With…

天体物理仪器与方法 · 物理学 2020-08-11 Stephen R. Green , Jonathan Gair

Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms. Because the evaluation of the likelihood function requires evaluating millions of…

The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses…

高能天体物理现象 · 物理学 2022-11-15 Matthew Mould , Davide Gerosa , Stephen R. Taylor

Ground-based gravitational-wave detectors like the Advanced LIGO, Advanced Virgo, and KAGRA experiments now regularly witness gravitational waves from compact binary mergers: the relativistic collisions of neutron stars and/or stellar-mass…

高能天体物理现象 · 物理学 2024-10-28 T. A. Callister

The LIGO, Virgo, and KAGRA (LVK) gravitational-wave observatories have opened new scientific research in astrophysics, fundamental physics, and cosmology. The collaborations that build and operate these observatories release the…

广义相对论与量子宇宙学 · 物理学 2026-01-23 Gregory Ashton

The detection of gravitational waves (GWs) from binary black holes (BBHs) has allowed the theory of general relativity to be tested in a previously unstudied regime: that of strong curvature and high GW luminosities. One distinctive and…

高能天体物理现象 · 物理学 2020-04-29 Oliver M. Boersma , David A. Nichols , Patricia Schmidt

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep…

广义相对论与量子宇宙学 · 物理学 2022-08-17 Alex Kolmus , Grégory Baltus , Justin Janquart , Twan van Laarhoven , Sarah Caudill , Tom Heskes
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