Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T10:17:45.186954Z
Paper Citation Record · LEDGER
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion
As of 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2508.00348.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
78 of 78 outbound references displayed
- verified exact0
- verified fuzzy71
- unresolved7
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- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 22c74ebf-c0d9-497f-b102-c0768997c9c7 · outbound
This paper cites Advanced LIGO.
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.370414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ec3c220a-bdae-4595-9d1a-7f7bb16233f9 · outbound
This paper cites Observation of Gravitational Waves from a Binary Black Hole Merger.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Observation of Gravitational Waves from a Binary Black Hole Merger
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.347353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0a519e1c-4e4e-4332-a6fc-0c1fe2031922 · outbound
This paper cites LIGO-Virgo-KAGRA Announce the 200th Gravitational Wave Detection of O4! ����������������������������������������������.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion LIGO-Virgo-KAGRA Announce the 200th Gravitational Wave Detection of O4! ����������������������������������������������
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.314638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 031c0e88-9c12-4c39-a72d-0945a551cc57 · outbound
This paper cites GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.291995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a1494659-28cb-41cc-ac48-8d9e77006350 · outbound
This paper cites GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo during the Second Part of the Third Observing Run
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.269242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d3da82d0-be6d-4c29-9529-6c39ad594623 · outbound
This paper cites The LISA-Taiji Network: Precision Local- ization of Coalescing Massive Black Hole Binaries.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion The LISA-Taiji Network: Precision Local- ization of Coalescing Massive Black Hole Binaries
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.246343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b54883be-b25b-4dd3-a71f-c3fbe4db7ef3 · outbound
This paper cites Verification of Laser Heterodyne Interferometric Bench for Chinese Spaceborne Gravitational Wave Detection Missions.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Verification of Laser Heterodyne Interferometric Bench for Chinese Spaceborne Gravitational Wave Detection Missions
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.220478Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 00c099ce-4de0-417d-8c25-26197f5e66aa · outbound
This paper cites The Taiji Program in Space for gravitational wave physics and the nature of gravity.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion The Taiji Program in Space for gravitational wave physics and the nature of gravity
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.196982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 74af8c17-5199-4343-8b0b-6d9c3c7ff89c · outbound
This paper cites TianQin: a space-borne gravitational wave detector.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion TianQin: a space-borne gravitational wave detector
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.174337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3dd57bc6-22f5-4695-881d-53d9742cd145 · outbound
This paper cites Laser Interferometer Space Antenna.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Laser Interferometer Space Antenna
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:17:44.698825Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f17b6156-1807-48b9-b712-f39f1fb83789 · outbound
This paper cites Science with the space-based interferometer LISA.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Science with the space-based interferometer LISA
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.151054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 913ded13-ebc0-4077-a41c-88de5f82f780 · outbound
This paper cites Testing General Relativity with Low-Frequency, Space-Based Gravitational-Wave Detectors.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Testing General Relativity with Low-Frequency, Space-Based Gravitational-Wave Detectors
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.130567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 94d54461-4910-4c3c-976f-e64854746945 · outbound
This paper cites Gravitational-wave cosmology with extreme mass- ratio inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Gravitational-wave cosmology with extreme mass- ratio inspirals
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.108145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation efa0074a-c905-429b-8a05-54c1a0470b17 · outbound
This paper cites Science with the space-based interferometer LISA.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Science with the space-based interferometer LISA
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.081910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8363d06e-eb95-464f-8613-8934d9efaf49 · outbound
This paper cites Extreme- and intermediate-mass ratio inspirals in dynamical Chern- Simons modified gravity.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Extreme- and intermediate-mass ratio inspirals in dynamical Chern- Simons modified gravity
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.055484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1e038e6a-c1e1-4591-ad97-9bf7e9469b42 · outbound
This paper cites Using LISA extreme-mass-ratio inspiral sources to test off-Kerr devi- ations in the geometry of massive black holes.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Using LISA extreme-mass-ratio inspiral sources to test off-Kerr devi- ations in the geometry of massive black holes
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.022394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9d9b1781-96e5-42eb-98a3-e9a6c311f686 · outbound
This paper cites Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:47.002191Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a6375a79-2c72-4223-b3c8-60d2ce3be7cc · outbound
This paper cites Constraint on the Deviation of Kerr Metric via Bumpy Parameterization and Particle Swarm Optimization in Extreme Mass-Ratio Inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Constraint on the Deviation of Kerr Metric via Bumpy Parameterization and Particle Swarm Optimization in Extreme Mass-Ratio Inspirals
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.983744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 013c5da3-f6d3-4188-8099-3f93eb2c65ac · outbound
This paper cites Gravitational waves from extreme-mass-ratio inspirals using general parametrized metrics.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Gravitational waves from extreme-mass-ratio inspirals using general parametrized metrics
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.920009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ea5c357-f8d1-4335-b86d-af72731cc2f0 · outbound
This paper cites LISA extreme-mass-ratio inspiral events as probes of the black hole mass function.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion LISA extreme-mass-ratio inspiral events as probes of the black hole mass function
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.880829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e5068cb0-eeef-4a90-9ced-fc24659b0bec · outbound
This paper cites Probing Accretion Physics with Gravitational Waves.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Probing Accretion Physics with Gravitational Waves
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.843177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e42c8a66-c615-48e7-abea-78e02c775e06 · outbound
This paper cites Disks, spikes, and clouds: distinguishing environmental effects on BBH gravitational waveforms.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Disks, spikes, and clouds: distinguishing environmental effects on BBH gravitational waveforms
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.817970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0bc15fc8-24c6-4771-8249-f2468096af07 · outbound
This paper cites Extreme dark matter tests with extreme mass ratio inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Extreme dark matter tests with extreme mass ratio inspirals
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.791732Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 69af369a-33c5-43ac-9168-abff2467457e · outbound
This paper cites Dark Matter: An Efficient Catalyst for Intermediate-mass- ratio-inspiral Events.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Dark Matter: An Efficient Catalyst for Intermediate-mass- ratio-inspiral Events
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.770594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 04fb666b-83aa-4bab-b87c-f26ae5e29169 · outbound
This paper cites Event rate estimates for LISA extreme mass ratio capture sources.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Event rate estimates for LISA extreme mass ratio capture sources
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.736851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1f180bed-4464-4821-b206-f6ff807a7008 · outbound
This paper cites EMRI_MC: A GPU-based Python code for Bayesian inference of EMRI waveforms.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion EMRI_MC: A GPU-based Python code for Bayesian inference of EMRI waveforms
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:17:44.818495Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1712176-d1da-4570-998c-d1e7fbdcca80 · outbound
This paper cites Rapid generation of fully relativis- tic extreme-mass-ratio-inspiral waveform templates for LISA data analysis.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Rapid generation of fully relativis- tic extreme-mass-ratio-inspiral waveform templates for LISA data analysis
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.703231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 698f347d-001c-4c53-ab20-fcc80597184d · outbound
This paper cites The Mock LISA Data Challenges: From Challenge 3 to Challenge 4.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion The Mock LISA Data Challenges: From Challenge 3 to Challenge 4
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.676088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 69555811-83ca-4411-ba9a-a17598b3e284 · outbound
This paper cites Nonlocal parameter degeneracy in the intrinsic space of gravitational- wave signals from extreme-mass-ratio inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Nonlocal parameter degeneracy in the intrinsic space of gravitational- wave signals from extreme-mass-ratio inspirals
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.647688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f36b68c5-2769-47b7-955c-d1200937182a · outbound
This paper cites Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Swarm Intelligence Methods for Extreme Mass Ratio Inspiral Search: First Application of Particle Swarm Optimization
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.616937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d825aa79-b45f-4003-8f67-a6da77437d59 · outbound
This paper cites Flow Matching for Scalable Simulation-Based Inference.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Flow Matching for Scalable Simulation-Based Inference
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:17:44.860638Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e805c255-7a31-4c3e-9bd3-f10c67a63921 · outbound
This paper cites Detection strategies for extreme mass ratio inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Detection strategies for extreme mass ratio inspirals
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.580581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32a77cdb-1338-4e9d-af51-cf5c7ada5597 · outbound
This paper cites An algorithm for the detection of extreme mass ratio inspirals in LISA data.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion An algorithm for the detection of extreme mass ratio inspirals in LISA data
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.545208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3aaaed9b-4ac7-4eff-a8a1-a5fc65c53765 · outbound
This paper cites Flow Matching for Generative Modeling.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Flow Matching for Generative Modeling
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:17:44.881582Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 152a6181-2ce0-4688-825e-6b32ecf9dd56 · outbound
This paper cites Eryn: a multipurpose sampler for Bayesian inference.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Eryn: a multipurpose sampler for Bayesian inference
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.519450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2ed9c655-9db7-4e30-80c9-f5e080394489 · outbound
This paper cites emcee: The MCMC Hammer.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion emcee: The MCMC Hammer
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.483699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bc456b01-b925-4d40-8e2e-5d2e1fb48a08 · outbound
This paper cites Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Fast extreme-mass-ratio-inspiral waveforms: New tools for millihertz gravitational-wave data analysis
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.451052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 935d7bbe-ac0b-4e42-bc3c-a5513e86494a · outbound
This paper cites Eryn: a multipurpose sampler for Bayesian inference.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Eryn: a multipurpose sampler for Bayesian inference
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.413870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a7613d00-8442-446d-a5b9-85f103965e85 · outbound
This paper cites ¡tt¿emcee¡/tt¿: The MCMC Hammer.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion ¡tt¿emcee¡/tt¿: The MCMC Hammer
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.382526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8efc41b5-415b-4d65-b2d2-06392846a31f · outbound
This paper cites An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.340131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d85b8d6c-0c10-408c-a8a0-55e99b37c07e · outbound
This paper cites Binary Black Hole Mergers in the First Advanced LIGO Observing Run.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Binary Black Hole Mergers in the First Advanced LIGO Observing Run
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.319260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c73f358b-de07-45fe-b45b-2987bccc5d7b · outbound
This paper cites ASTROPHYSICAL IMPLICATIONS OF THE BI- NARY BLACK HOLE MERGER GW150914.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion ASTROPHYSICAL IMPLICATIONS OF THE BI- NARY BLACK HOLE MERGER GW150914
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.293345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d100171e-ea9b-4f26-8938-c35792a206d9 · outbound
This paper cites Testing General Relativity with Low-Frequency, Space-Based Gravitational-Wave Detectors.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Testing General Relativity with Low-Frequency, Space-Based Gravitational-Wave Detectors
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.264212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation dd1358ba-8d28-4ed1-9a69-b494f30d2ba5 · outbound
This paper cites Gravitational Wave- forms for Compact Binaries from Second-Order Self-Force Theory.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Gravitational Wave- forms for Compact Binaries from Second-Order Self-Force Theory
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.235764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 859cf66d-8a6e-4f8c-bb2d-dff95889e55f · outbound
This paper cites Theoretical physics implications of the binary black-hole mergers GW150914 and GW151226.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Theoretical physics implications of the binary black-hole mergers GW150914 and GW151226
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.201308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ce64bb09-53f0-4a01-8d9e-77e1ffcc262f · outbound
This paper cites Tests of General Relativity with GW150914.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Tests of General Relativity with GW150914
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.177062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 85b4e21a-3884-4df5-9336-adac88432748 · outbound
This paper cites Self-force and radiation reaction in general relativity.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Self-force and radiation reaction in general relativity
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.152057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9290501b-c51e-4f6d-8737-b5f6a85ffb73 · outbound
This paper cites Black hole perturbation theory and gravitational self-force.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Black hole perturbation theory and gravitational self-force
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:17:44.976270Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d96a44c-ef93-4dc3-97ee-28ce9098ba1e · outbound
This paper cites Gravitational self-force on generic bound geodesics in Kerr spacetime.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Gravitational self-force on generic bound geodesics in Kerr spacetime
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.126707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 65dbd8f2-51bc-4a8d-9805-48e8204af1b9 · outbound
This paper cites Second-Order Self-Force Calculation of Grav- itational Binding Energy in Compact Binaries.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Second-Order Self-Force Calculation of Grav- itational Binding Energy in Compact Binaries
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.104880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b8a1d91c-d4ca-41e9-b47d-edea8d118090 · outbound
This paper cites Gravitational-Wave Energy Flux for Compact Binaries through Second Order in the Mass Ratio.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Gravitational-Wave Energy Flux for Compact Binaries through Second Order in the Mass Ratio
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.085020Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26166b57-23e3-4618-869a-416abef6b948 · outbound
This paper cites an unresolved cited work.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T10:17:46.063523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 78bb76f7-20d0-4b42-90f4-f0ca5783725e · outbound
This paper cites Influence of mass-ratio corrections in extreme-mass-ratio inspirals for testing general relativity.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Influence of mass-ratio corrections in extreme-mass-ratio inspirals for testing general relativity
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.039962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2a69f757-8ca2-4b2c-bc0b-82a419e366e7 · outbound
This paper cites LISA capture sources: Approximate waveforms, signal-to-noise ratios, and parameter estimation accuracy.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion LISA capture sources: Approximate waveforms, signal-to-noise ratios, and parameter estimation accuracy
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.014931Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8ffb4c20-380e-4549-8235-97a16d89a964 · outbound
This paper cites ’Kludge’ gravitational waveforms for a test-body orbiting a Kerr black hole.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion ’Kludge’ gravitational waveforms for a test-body orbiting a Kerr black hole
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.993554Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e453948a-5778-460d-a0e3-8f3fd9bcf735 · outbound
This paper cites Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.971474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ae47a083-c03f-435b-99bc-afc967d6b2e6 · outbound
This paper cites Augmented kludge waveforms for detecting extreme-mass- ratio inspirals.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Augmented kludge waveforms for detecting extreme-mass- ratio inspirals
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.942326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation de56c5f8-fff5-4df4-8362-bf05538ca9f7 · outbound
This paper cites Improved approximate inspirals of test-bodies into Kerr black holes.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Improved approximate inspirals of test-bodies into Kerr black holes
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.923182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5c62bc4c-efda-4e18-b33b-ae264a8e28fe · outbound
This paper cites Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.900612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 405e95f5-c2a3-4250-bc7c-bcf71f60e694 · outbound
This paper cites LISA Data Challenge Manual.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion LISA Data Challenge Manual
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.881544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9816d527-ab29-4b4e-b5f9-e863d1cd77a2 · outbound
This paper cites Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Advancing Space-Based Gravitational Wave Astronomy: Rapid Detection and Parameter Estimation Using Normalizing Flows
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.857489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3d610029-4c3b-420e-8d0e-df3fdcb92191 · outbound
This paper cites Time-Delay Interferometry Simulations for the Laser Interferometer Space Antenna.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Time-Delay Interferometry Simulations for the Laser Interferometer Space Antenna
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.806291Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3e5e5299-5401-49e4-b97c-0338b5308987 · outbound
This paper cites Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.777400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f80e2dbb-3a1c-4e3a-bc97-c2945ef7fef5 · outbound
This paper cites Accuracy Requirements: Assessing the Impor- tance of First Post-Adiabatic Terms for Small-Mass-Ratio Binaries.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Accuracy Requirements: Assessing the Impor- tance of First Post-Adiabatic Terms for Small-Mass-Ratio Binaries
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.749339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation eac5443a-81bd-4d44-8ab3-a1f25993c6b7 · outbound
This paper cites Overview and progress on the Laser Interferometer Space Antenna mission.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Overview and progress on the Laser Interferometer Space Antenna mission
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.729206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 871f0e78-a78c-480f-ae53-a31df9883c49 · outbound
This paper cites A roadmap of gravitational wave data analysis.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion A roadmap of gravitational wave data analysis
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.707845Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 92c9e98d-d0b6-45fc-940f-7cd3ad768bf6 · outbound
This paper cites Numerical simulation of sky localization for LISA-TAIJI joint observation.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Numerical simulation of sky localization for LISA-TAIJI joint observation
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.686872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4a156d5a-ae09-4627-a5e0-e49df8a0e295 · outbound
This paper cites Extreme Mass Ratio Inspirals: Perspectives for Their De- tection.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Extreme Mass Ratio Inspirals: Perspectives for Their De- tection
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.663971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26d2da9e-3c70-429e-aeb7-57a13b9bd46d · outbound
This paper cites Fast �-free Inference of Simulation Models with Bayesian Con- ditional Density Estimation.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Fast �-free Inference of Simulation Models with Bayesian Con- ditional Density Estimation
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.624832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5c520786-50c5-4277-980f-fb77ced926ef · outbound
This paper cites Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using ODE-Based Generative Models.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using ODE-Based Generative Models
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.594512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation dda602b6-b71e-4b0d-a606-9095bed1eeb5 · outbound
This paper cites Real-Time Gravitational Wave Science with Neural Posterior Estimation.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Real-Time Gravitational Wave Science with Neural Posterior Estimation
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.562759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5975a8aa-6600-43c5-836f-c8101d773869 · outbound
This paper cites Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.512927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 027b265e-4c7a-49a4-8e53-a83856db9c2a · outbound
This paper cites Variational inference with normalizing flows.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Variational inference with normalizing flows
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.462696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e1cd21b7-962c-4ff9-954b-a1b37ba7a8b4 · outbound
This paper cites Normal- izing Flows for Probabilistic Modeling and Inference.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Normal- izing Flows for Probabilistic Modeling and Inference
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.442649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation de1947fd-ac28-4aba-bf63-cd3967fc1090 · outbound
This paper cites ��� : ��������������������������������.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion ��� : ��������������������������������
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.486016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e57a7795-44f5-4ac5-93fc-23214cac8583 · outbound
This paper cites Neural Ordinary Differential Equa- tions.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Neural Ordinary Differential Equa- tions
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:45.412776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4d56f865-47a0-4b5c-8a08-8a4a06def1f3 · outbound
This paper cites an unresolved cited work.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion Unresolved cited work
Reference 2023
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T10:17:45.829452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 27b900b3-08f8-4a96-a022-2d56e06ceb74 · outbound
This paper cites ��� : ��������������������������������.
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion ��� : ��������������������������������
Reference 2025
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T10:17:46.946895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.