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Paper Citation Record · LEDGER

Black Hole Spectroscopy with Conditional Variational Autoencoder

As of 16 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2506.17618.

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pith.paper-citation-record.v1
2506.17618 v1

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Outbound references

Observation 5ae6eae0-f721-4bea-a1d6-4655d6024276 · outbound

This paper cites Parameter Estimation.

Black Hole Spectroscopy with Conditional Variational Autoencoder Parameter Estimation

Reference 1

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Observation 4e05a316-da42-405b-9fd4-dd5d6ec990d4 · outbound

This paper cites Observation of Gravitational Waves from a Binary Black Hole Merger.

Black Hole Spectroscopy with Conditional Variational Autoencoder Observation of Gravitational Waves from a Binary Black Hole Merger

Reference 2

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Observation 8c80907c-7833-4724-b32b-db6e458d6cc1 · outbound

This paper cites Properties of the Binary Black Hole Merger GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Properties of the Binary Black Hole Merger GW150914

Reference 3

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Observation b8dd88e0-d0c3-4756-9698-b89835f470d7 · outbound

This paper cites GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run.

Black Hole Spectroscopy with Conditional Variational Autoencoder GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

Reference 4

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Observation cca244a9-2379-4bc0-974f-d9884bc0c24b · outbound

This paper cites GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run.

Black Hole Spectroscopy with Conditional Variational Autoencoder GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run

Reference 5

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Observation 0e7dd81b-f7e5-443c-bd5e-9d8fa4862d5e · outbound

This paper cites Tests of general relativity with GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Tests of general relativity with GW150914

Reference 6

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Observation dd459a44-059b-42cf-aed5-897f8835b8c3 · outbound

This paper cites Tests of General Relativity with GWTC-3.

Black Hole Spectroscopy with Conditional Variational Autoencoder Tests of General Relativity with GWTC-3

Reference 7

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Observation 335972ca-f73f-448b-8dcd-49b174ffb584 · outbound

This paper cites GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral.

Black Hole Spectroscopy with Conditional Variational Autoencoder GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral

Reference 8

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Observation 64d94f60-10ba-48d7-bc23-642215e46f66 · outbound

This paper cites Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog.

Black Hole Spectroscopy with Conditional Variational Autoencoder Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Reference 9

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Observation f8c04d48-c75c-410d-97bf-4983caa0bc81 · outbound

This paper cites The population of merging compact binaries inferred using gravitational waves through GWTC-3.

Black Hole Spectroscopy with Conditional Variational Autoencoder The population of merging compact binaries inferred using gravitational waves through GWTC-3

Reference 10

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Observation 0c22876f-df6b-481e-bb28-9ded6fcce60a · outbound

This paper cites Stability of a Schwarzschild singularity,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Stability of a Schwarzschild singularity,

Reference 11

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Observation 91269459-635a-4cf3-9192-8a345b6d3b77 · outbound

This paper cites Scattering of Gravitational Radiation by a Schwarzschild Black-hole,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Scattering of Gravitational Radiation by a Schwarzschild Black-hole,

Reference 12

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Observation f7b80ae5-eec9-4325-b48d-3b3d8948efdd · outbound

This paper cites Perturbation analysis for gravitational and electromagnetic radiation in a reissner-nordstroem geometry,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Perturbation analysis for gravitational and electromagnetic radiation in a reissner-nordstroem geometry,

Reference 13

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Black Hole Spectroscopy with Conditional Variational Autoencoder Unresolved cited work

Reference 14

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Observation 5c8b37ca-9c0a-4bd7-b91a-6ced6b0abf1a · outbound

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Black Hole Spectroscopy with Conditional Variational Autoencoder Unresolved cited work

Reference 15

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Observation ff7cce4e-dc65-4276-bceb-8997cf9869d6 · outbound

This paper cites On gravitational-wave spectroscopy of massive black holes with the space interferometer LISA.

Black Hole Spectroscopy with Conditional Variational Autoencoder On gravitational-wave spectroscopy of massive black holes with the space interferometer LISA

Reference 16

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Observation 2300f091-07ce-4744-b2a5-99257674c331 · outbound

This paper cites Event horizons in static electrovac space-times,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Event horizons in static electrovac space-times,

Reference 17

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Observation f2f70567-10b5-4f6b-a6c8-7640e35fd08c · outbound

This paper cites Axisymmetric Black Hole Has Only Two Degrees of Freedom,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Axisymmetric Black Hole Has Only Two Degrees of Freedom,

Reference 18

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Observation fbead760-1a6a-4db0-8c70-315c7914916b · outbound

This paper cites Uniqueness of the Kerr black hole,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Uniqueness of the Kerr black hole,

Reference 19

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Observation f1c44417-24e1-4149-a84a-68390dce2c3d · outbound

This paper cites Testing the No-Hair Theorem with Observations in the Electromagnetic Spectrum: II. Black-Hole Images.

Black Hole Spectroscopy with Conditional Variational Autoencoder Testing the No-Hair Theorem with Observations in the Electromagnetic Spectrum: II. Black-Hole Images

Reference 20

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Observation 6c4d793c-ecbb-4968-9256-d4e22b5895b3 · outbound

This paper cites Testing the no-hair theorem with black hole ringdowns using TIGER.

Black Hole Spectroscopy with Conditional Variational Autoencoder Testing the no-hair theorem with black hole ringdowns using TIGER

Reference 21

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Observation 45bede23-0469-47cc-a008-d3bc016ba020 · outbound

This paper cites Testing the no-hair theorem with GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Testing the no-hair theorem with GW150914

Reference 22

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This paper cites Black-hole spectroscopy, the no-hair theorem and GW150914: Kerr vs. Occam.

Black Hole Spectroscopy with Conditional Variational Autoencoder Black-hole spectroscopy, the no-hair theorem and GW150914: Kerr vs. Occam

Reference 23

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Observation 4e96fd75-bb01-42b6-949b-9bd6d0516dd4 · outbound

This paper cites Observational Black Hole Spectroscopy: A time-domain multimode analysis of GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Observational Black Hole Spectroscopy: A time-domain multimode analysis of GW150914

Reference 24

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This paper cites Analyzing black-hole ringdowns.

Black Hole Spectroscopy with Conditional Variational Autoencoder Analyzing black-hole ringdowns

Reference 25

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Observation d9f317da-cad5-4111-9a6f-185ae3f9bf0f · outbound

This paper cites Analysis of Ringdown Overtones in GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Analysis of Ringdown Overtones in GW150914

Reference 26

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This paper cites The Ringdown of GW190521: Hints of Multiple Quasinormal Modes with a Precessional Interpretation.

Black Hole Spectroscopy with Conditional Variational Autoencoder The Ringdown of GW190521: Hints of Multiple Quasinormal Modes with a Precessional Interpretation

Reference 27

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Black Hole Spectroscopy with Conditional Variational Autoencoder Probing Spacetime Symmetries Using Gravitational Wave Ringdown

Reference 28

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This paper cites Testing the black-hole area law with GW150914.

Black Hole Spectroscopy with Conditional Variational Autoencoder Testing the black-hole area law with GW150914

Reference 29

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This paper cites Constraints on Kerr-Newman black holes from merger-ringdown gravitational-wave observations.

Black Hole Spectroscopy with Conditional Variational Autoencoder Constraints on Kerr-Newman black holes from merger-ringdown gravitational-wave observations

Reference 30

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This paper cites Constraining extra dimensions using observations of black hole quasi-normal modes.

Black Hole Spectroscopy with Conditional Variational Autoencoder Constraining extra dimensions using observations of black hole quasi-normal modes

Reference 31

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This paper cites Identifying modified theories of gravity using binary black-hole ringdowns.

Black Hole Spectroscopy with Conditional Variational Autoencoder Identifying modified theories of gravity using binary black-hole ringdowns

Reference 32

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This paper cites Black-hole ringdown as a probe of higher-curvature gravity theories.

Black Hole Spectroscopy with Conditional Variational Autoencoder Black-hole ringdown as a probe of higher-curvature gravity theories

Reference 33

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Observation f2ef16c5-5ab6-4304-ac77-7050f29b8ec3 · outbound

This paper cites Measuring deviations from the Kerr geometry with black hole ringdown.

Black Hole Spectroscopy with Conditional Variational Autoencoder Measuring deviations from the Kerr geometry with black hole ringdown

Reference 34

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This paper cites Bounds on tidal charges from gravitational-wave ringdown observations.

Black Hole Spectroscopy with Conditional Variational Autoencoder Bounds on tidal charges from gravitational-wave ringdown observations

Reference 35

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This paper cites Higher-derivative corrections to the Kerr quasinormal mode spectrum.

Black Hole Spectroscopy with Conditional Variational Autoencoder Higher-derivative corrections to the Kerr quasinormal mode spectrum

Reference 36

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Black Hole Spectroscopy with Conditional Variational Autoencoder Black hole spectroscopy: from theory to experiment

Reference 37

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Observation 52d4f7ff-a0d0-4883-82c9-ef8a921c8c93 · outbound

This paper cites Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy.

Black Hole Spectroscopy with Conditional Variational Autoencoder Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy

Reference 38

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Observation 552e87bc-3e85-4e69-a7cb-37a2cca7c6f2 · outbound

This paper cites An introduction to bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models,.

Black Hole Spectroscopy with Conditional Variational Autoencoder An introduction to bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models,

Reference 39

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Observation bef688ca-c348-47da-868a-12f6afcd34a5 · outbound

This paper cites Inferring Binary Properties from Gravitational Wave Signals.

Black Hole Spectroscopy with Conditional Variational Autoencoder Inferring Binary Properties from Gravitational Wave Signals

Reference 40

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Observation 33a2a5d7-dd0c-4bb6-bee4-b8faf4f7828a · outbound

This paper cites pyRing: a time-domain ringdown analysis python package.

Black Hole Spectroscopy with Conditional Variational Autoencoder pyRing: a time-domain ringdown analysis python package

Reference 41

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Observation f51f00db-01ad-4910-bf95-1024ae82b0d7 · outbound

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

Black Hole Spectroscopy with Conditional Variational Autoencoder dynesty: a dynamic nested sampling package for estimating bayesian posteriors and evidences,

Reference 42

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Observation b16abe78-5992-4bbe-8ce6-3ceac7473137 · outbound

This paper cites johnveitch/cpnest: v0.11.4,.

Black Hole Spectroscopy with Conditional Variational Autoencoder johnveitch/cpnest: v0.11.4,

Reference 43

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Observation c120d88b-de4e-433f-925f-55b159e2abba · outbound

This paper cites Matching matched filtering with deep networks in gravitational-wave astronomy.

Black Hole Spectroscopy with Conditional Variational Autoencoder Matching matched filtering with deep networks in gravitational-wave astronomy

Reference 44

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Observation 7e38e6c5-31dd-460d-a957-aed6cd89b223 · outbound

This paper cites emcee: The MCMC Hammer.

Black Hole Spectroscopy with Conditional Variational Autoencoder emcee: The MCMC Hammer

Reference 45

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Observation 12966076-0fa9-4983-9292-06bec06a99b7 · outbound

This paper cites Probabilistic programming in python using pymc3,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Probabilistic programming in python using pymc3,

Reference 46

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Observation 1f750343-5b59-4384-b9a4-441ba1bf5e1e · outbound

This paper cites Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Reference 47

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source=pdf_text observed=2026-08-15T19:11:25.222031Z digest=sha256:ad415822984b47eee4c264f15406301e48aec3bfadd32cffc100381878558f34

Observation a91863aa-6a90-4236-be3b-bcf2f7e18956 · outbound

This paper cites Detecting and Denoising Gravitational Wave Signals from Binary Black Holes using Deep Learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Detecting and Denoising Gravitational Wave Signals from Binary Black Holes using Deep Learning

Reference 48

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source=pdf_text observed=2026-08-15T19:11:25.212402Z digest=sha256:2e63339bb21c3f0d0631a03c306bae6761db3f47c9fd3be92cab689215a436a2

Observation ea8dfd78-9de9-4b19-b269-1dc2df61b3a8 · outbound

This paper cites Convolutional Neural Networks for signal detection in real LIGO data.

Black Hole Spectroscopy with Conditional Variational Autoencoder Convolutional Neural Networks for signal detection in real LIGO data

Reference 49

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source=pdf_text observed=2026-08-15T19:11:25.216651Z digest=sha256:d36463349685cbb4f3811d7c0313dc5e650f9cc74e1e71a07022f9a793087f96

Observation b5a79e26-ba0a-487e-b99a-cd02968da0a4 · outbound

This paper cites Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks.

Black Hole Spectroscopy with Conditional Variational Autoencoder Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks

Reference 50

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source=pdf_text observed=2026-08-15T19:11:25.239032Z digest=sha256:fd001cf0d08f2f3ffef35de98f6db7475575774fd70360f93d6e96a00345d1b5

Observation 42006fd3-c698-4a2b-a6db-fbbaa7a4cd71 · outbound

This paper cites Noise Reduction in Gravitational-wave Data via Deep Learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Noise Reduction in Gravitational-wave Data via Deep Learning

Reference 51

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source=pdf_text observed=2026-08-15T19:11:25.227040Z digest=sha256:6cb3bd15e1026788b8e7c0214ebe024aa11315f1b5f30d4b93899fc6ff8cee97

Observation 274fdb4d-95d0-4a74-9286-9359c37ebd47 · outbound

This paper cites Machine-learning non-stationary noise out of gravitational wave detectors.

Black Hole Spectroscopy with Conditional Variational Autoencoder Machine-learning non-stationary noise out of gravitational wave detectors

Reference 52

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source=pdf_text observed=2026-08-15T19:11:25.233646Z digest=sha256:23ea66b8c1ce30c2b5b2761b0912fe873ef0c80bc5a86c60c5a55d661822a8d0

Observation f2287b4d-6c5f-4bc9-85a0-23bf7190a3e0 · outbound

This paper cites NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals.

Black Hole Spectroscopy with Conditional Variational Autoencoder NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals

Reference 53

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Observation 3ad0fc6f-2d89-4ec9-a162-92809eba8993 · outbound

This paper cites Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning

Reference 54

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source=pdf_text observed=2026-08-15T19:11:25.244331Z digest=sha256:2e5d56a814174758d2a43e606d43861cca60a5349855757561c6b9132e2f6f34

Observation cfe894bf-1600-457a-a5d3-e929f0353081 · outbound

This paper cites Application of dictionary learning to denoise LIGO's blip noise transients.

Black Hole Spectroscopy with Conditional Variational Autoencoder Application of dictionary learning to denoise LIGO's blip noise transients

Reference 55

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source=pdf_text observed=2026-08-15T19:11:25.248790Z digest=sha256:7fe2139ea63ade20cb341cfe110ca3fbf57c654e8feba00215af8d4aa1da6927

Observation db257ee3-ccb8-4680-a0ce-7e781e365a2f · outbound

This paper cites NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms,.

Black Hole Spectroscopy with Conditional Variational Autoencoder NRSurNN3dq4: A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms,

Reference 56

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source=pdf_text observed=2026-08-15T19:11:25.268025Z digest=sha256:a2420d1bbc4047500d942a03c1a5b1a9bd2579eb2a8c087171513546d7166340

Observation 7debb33e-d82a-4134-baf7-8d481e7f724f · outbound

This paper cites Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder.

Black Hole Spectroscopy with Conditional Variational Autoencoder Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder

Reference 57

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Observation b76d92d9-afc7-4458-8352-6b2d37eb0195 · outbound

This paper cites Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation.

Black Hole Spectroscopy with Conditional Variational Autoencoder Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation

Reference 58

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Observation 17fce9ff-27e1-4561-a080-168a59e79b20 · outbound

This paper cites Use of conditional variational auto encoder to analyze ringdown gravitational waves.

Black Hole Spectroscopy with Conditional Variational Autoencoder Use of conditional variational auto encoder to analyze ringdown gravitational waves

Reference 59

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Observation c15ff125-f54b-49ad-bd4f-67a75a20a5e2 · outbound

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

Black Hole Spectroscopy with Conditional Variational Autoencoder Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Reference 60

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Observation 5a73fa7e-a18b-41a1-96ca-284056eb1634 · outbound

This paper cites Gravitational-wave parameter estimation with autoregressive neural network flows.

Black Hole Spectroscopy with Conditional Variational Autoencoder Gravitational-wave parameter estimation with autoregressive neural network flows

Reference 61

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Observation 523563da-f8e0-46e5-9871-d70894217844 · outbound

This paper cites Merger-Ringdown Consistency: A New Test of Strong Gravity using Deep Learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Merger-Ringdown Consistency: A New Test of Strong Gravity using Deep Learning

Reference 62

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Observation 6ae29b3e-622b-4ec6-8145-2f00806ed197 · outbound

This paper cites Complete parameter inference for GW150914 using deep learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Complete parameter inference for GW150914 using deep learning

Reference 63

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Observation 46abc031-c9bd-43e8-8c20-9e100e55a94e · outbound

This paper cites Real-time gravitational-wave science with neural posterior estimation.

Black Hole Spectroscopy with Conditional Variational Autoencoder Real-time gravitational-wave science with neural posterior estimation

Reference 64

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Observation 32a2036d-3e3a-483e-a19c-ab8a12549b80 · outbound

This paper cites Simulation-based inference of black hole ringdowns in the time domain.

Black Hole Spectroscopy with Conditional Variational Autoencoder Simulation-based inference of black hole ringdowns in the time domain

Reference 65

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Observation 8f82e842-eed1-47c1-b045-84f903cb36c8 · outbound

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

Black Hole Spectroscopy with Conditional Variational Autoencoder Statistically-informed deep learning for gravitational wave parameter estimation

Reference 66

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Observation dece5d0c-58c6-4a25-96ef-970e03bdf07f · outbound

This paper cites Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning.

Black Hole Spectroscopy with Conditional Variational Autoencoder Rapid Mass Parameter Estimation of Binary Black Hole Coalescences Using Deep Learning

Reference 67

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Observation 7ce2c735-3af0-4b90-8f61-8c5a43833cf8 · outbound

This paper cites Learning structured output representation using deep conditional generative models,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Learning structured output representation using deep conditional generative models,

Reference 68

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Observation ca3fb1d5-a9c0-4cce-b15c-5dd05d74da41 · outbound

This paper cites Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware.

Black Hole Spectroscopy with Conditional Variational Autoencoder Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

Reference 69

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Observation cd1ac30f-e649-418b-823d-b567baed6341 · outbound

This paper cites Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders.

Black Hole Spectroscopy with Conditional Variational Autoencoder Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders

Reference 70

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Observation 930059ad-d791-4b50-a5bc-d43d5b7574e9 · outbound

This paper cites An introduction to variational autoencoders,.

Black Hole Spectroscopy with Conditional Variational Autoencoder An introduction to variational autoencoders,

Reference 71

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Observation 466eee91-73c5-46ed-805c-8a54caf1077f · outbound

This paper cites Tutorial on variational autoencoders,.

Black Hole Spectroscopy with Conditional Variational Autoencoder Tutorial on variational autoencoders,

Reference 72

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Observation d2883043-7a70-4b51-bb4c-3810eac608fd · outbound

This paper cites Controlling Structured Output Representations from Attributes using Conditional Generative Models.

Black Hole Spectroscopy with Conditional Variational Autoencoder Controlling Structured Output Representations from Attributes using Conditional Generative Models

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local_arxiv, observed 2026-08-15T19:11:25.603097Z

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Observation 0053e6ae-e193-47cc-aaf0-79cfca94951c · outbound

This paper cites Auto-Encoding Variational Bayes.

Black Hole Spectroscopy with Conditional Variational Autoencoder Auto-Encoding Variational Bayes

Reference 74

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Observation ea75bca6-5905-40cc-9521-c2030506243f · outbound

This paper cites Exciting black hole modes via misaligned coalescences: II. The mode content of late-time coalescence waveforms.

Black Hole Spectroscopy with Conditional Variational Autoencoder Exciting black hole modes via misaligned coalescences: II. The mode content of late-time coalescence waveforms

Reference 75

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source=pdf_text observed=2026-08-15T19:11:25.368800Z digest=sha256:63e940dc9066a29362427ad1e1d4330e06f6024843b463d88b9990b522bb89f0

Observation 720b4944-4aa0-4d8b-871d-b0570b7ab409 · outbound

This paper cites Angular emission patterns of remnant black holes.

Black Hole Spectroscopy with Conditional Variational Autoencoder Angular emission patterns of remnant black holes

Reference 76

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source=pdf_text observed=2026-08-15T19:11:25.374004Z digest=sha256:fd4831130275a186d54e7145e1e838dfe85fca2c61e8f4b83b19d680a8477a82

Observation fe71008e-3547-4ff3-8773-abf224b98b51 · outbound

This paper cites Brane-world gravity.

Black Hole Spectroscopy with Conditional Variational Autoencoder Brane-world gravity

Reference 77

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unresolved
no resolver link, observed 2026-08-15T19:11:25.379072Z

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Unavailable: canonical work link unavailable.

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Observation 3ecf3764-9b7d-40e3-8c70-1b3e6243f14d · outbound

This paper cites Advanced LIGO.

Black Hole Spectroscopy with Conditional Variational Autoencoder Advanced LIGO

Reference 78

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no resolver link, observed 2026-08-15T19:11:25.358968Z

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Observation 466a811a-8537-4b98-8868-f0c7e4522830 · outbound

This paper cites Advanced LIGO detector performance in the fourth observing run.

Black Hole Spectroscopy with Conditional Variational Autoencoder Advanced LIGO detector performance in the fourth observing run

Reference 79

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unresolved
no resolver link, observed 2026-08-15T19:11:25.363654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f68e2e4e-8320-440e-b786-d78d6e1f8705 · outbound

This paper cites An Introduction to Extra Dimensions.

Black Hole Spectroscopy with Conditional Variational Autoencoder An Introduction to Extra Dimensions

Reference 83

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verified exact
local_arxiv, observed 2026-08-15T19:11:25.484750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 0bd38248-0dda-4209-815f-13a2176ab1e9 · outbound

This paper cites an unresolved cited work.

Black Hole Spectroscopy with Conditional Variational Autoencoder Unresolved cited work

Reference 2015

Resolution
unresolved
raw_fallback, observed 2026-08-15T19:11:27.198143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 7ec88026-703e-416c-beb3-7143ec709efa · outbound

This paper cites Tutorial on Variational Autoencoders.

Black Hole Spectroscopy with Conditional Variational Autoencoder Tutorial on Variational Autoencoders

Reference 2021

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unresolved
no resolver link, observed 2026-08-15T19:11:25.354060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 96198f8e-e9b3-4592-bd44-53b68defe730 · outbound

This paper cites an unresolved cited work.

Black Hole Spectroscopy with Conditional Variational Autoencoder Unresolved cited work

Reference 2022

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Pith citing papers

No inbound Pith citation observations are available.