Paper Citation Record · LEDGER

Gravitational wave populations and cosmology with neural posterior estimation

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2311.12093.

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pith.paper-citation-record.v1
2311.12093 v2

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Pith citing papers itemized under the disclosed page cap.

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Pith citing papers

Observation b52c4e87-81ee-492f-b57f-60add088cf23 · inbound

Applications of machine learning in gravitational wave research with current interferometric detectors cites this paper.

Applications of machine learning in gravitational wave research with current interferometric detectors Gravitational wave populations and cosmology with neural posterior estimation

Reference 255

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Constraining Population III stellar demographics with next-generation gravitational-wave observatories cites this paper.

Constraining Population III stellar demographics with next-generation gravitational-wave observatories Gravitational wave populations and cosmology with neural posterior estimation

Reference 89

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Observation 5321f870-75b1-4dcb-adcc-e47cd355a2a4 · inbound

A fast deep-learning approach to probing primordial black hole populations in gravitational wave events cites this paper.

A fast deep-learning approach to probing primordial black hole populations in gravitational wave events Gravitational wave populations and cosmology with neural posterior estimation

Reference 34

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Observation 4ce1411e-bd11-4348-a732-375ca12d9d3c · inbound

Parameter inference of millilensed gravitational waves using neural spline flows cites this paper.

Parameter inference of millilensed gravitational waves using neural spline flows Gravitational wave populations and cosmology with neural posterior estimation

Reference 66

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Observation 6fe0c24e-9e93-4de2-8da8-ea71469382a2 · inbound

Nowhere left to hide: revealing realistic gravitational-wave populations in high dimensions and high resolution with PixelPop cites this paper.

Nowhere left to hide: revealing realistic gravitational-wave populations in high dimensions and high resolution with PixelPop Gravitational wave populations and cosmology with neural posterior estimation

Reference 75

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Observation fc5d357f-8ca9-4b78-a46c-8d9d133c9a90 · inbound

Comparing astrophysical models to gravitational-wave data in the observable space cites this paper.

Comparing astrophysical models to gravitational-wave data in the observable space Gravitational wave populations and cosmology with neural posterior estimation

Reference 18

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Observation a9fea4fc-fbfa-4ad9-bd9e-c124c3c44b9e · inbound

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference cites this paper.

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference Gravitational wave populations and cosmology with neural posterior estimation

Reference 102

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Observation 5daa8210-9182-44c2-a864-ec7530fe5e73 · inbound

An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources cites this paper.

An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources Gravitational wave populations and cosmology with neural posterior estimation

Reference 64

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Observation c94c1cb6-8aff-4696-9c13-30b8b96f7b2d · inbound

Measurement prospects for the pair-instability mass cutoff with gravitational waves cites this paper.

Measurement prospects for the pair-instability mass cutoff with gravitational waves Gravitational wave populations and cosmology with neural posterior estimation

Reference 98

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Observation cb3a4dcf-87ee-44e3-983d-9dbfeab9e0b0 · inbound

Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows cites this paper.

Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows Gravitational wave populations and cosmology with neural posterior estimation

Reference 100

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Observation 200987f4-c13d-49f6-a771-417f15345a2f · inbound

End-to-End Population Inference from Gravitational-Wave Strain using Transformers cites this paper.

End-to-End Population Inference from Gravitational-Wave Strain using Transformers Gravitational wave populations and cosmology with neural posterior estimation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:02:06.075255Z

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Observation 1fc0a3ee-aefd-4f5d-ac77-525228402bdd · inbound

Fortifying gravitational-wave population inference with normalizing flows cites this paper.

Fortifying gravitational-wave population inference with normalizing flows Gravitational wave populations and cosmology with neural posterior estimation

Reference 79

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Observation 7bef4268-3ba5-4be1-b010-9f781030b7f5 · inbound

Identifying lensed gravitational waves with physics-informed posterior learning cites this paper.

Identifying lensed gravitational waves with physics-informed posterior learning Gravitational wave populations and cosmology with neural posterior estimation

Reference 157

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Observation 972d9cd4-47d0-4b51-8fd9-ea2cbc8f640f · inbound

Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers cites this paper.

Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers Gravitational wave populations and cosmology with neural posterior estimation

Reference 133

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Observation 1f686fb8-ee8d-40f7-b561-28fb045cc733 · inbound

Cosmology in the Einstein Telescope era: comparing traditional and simulation-based methods for population inference cites this paper.

Cosmology in the Einstein Telescope era: comparing traditional and simulation-based methods for population inference Gravitational wave populations and cosmology with neural posterior estimation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T14:49:04.412659Z

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