Paper Citation Record · LEDGER

An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2405.04690.

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pith.paper-citation-record.v1
2405.04690 v2

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Pith citing papers itemized under the disclosed page cap.

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Pith citing papers

Observation 25f7f51a-507f-4963-86ad-f908de23ad9f · inbound

Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh cites this paper.

Searching for stellar-origin binary black holes in LISA Data Challenge 1b: Yorsh An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 21

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Angular Resolution of a Bayesian Search for Anisotropic Stochastic Gravitational Wave Backgrounds with LISA cites this paper.

Angular Resolution of a Bayesian Search for Anisotropic Stochastic Gravitational Wave Backgrounds with LISA An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 51

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Observation f48b6a08-b969-490d-9af1-bb9ddedf4f2c · inbound

Modular global-fit pipeline for LISA data analysis cites this paper.

Modular global-fit pipeline for LISA data analysis An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 7

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Observation 7a367644-e01a-4b69-bfbe-7d5c4eb07da4 · inbound

Reconstructing Primordial Curvature Perturbations via Scalar-Induced Gravitational Waves with LISA cites this paper.

Reconstructing Primordial Curvature Perturbations via Scalar-Induced Gravitational Waves with LISA An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 58

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Observation 8a9c81b4-fefc-455f-a97f-17368e243ac6 · inbound

The implications of stochastic gas torques for asymmetric binaries in the LISA band cites this paper.

The implications of stochastic gas torques for asymmetric binaries in the LISA band An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 98

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Observation dc6ffc27-4cb6-4bc8-b40d-c567bb0d71f9 · inbound

Accelerating the time-domain LISA response model with central finite differences and hybridization techniques cites this paper.

Accelerating the time-domain LISA response model with central finite differences and hybridization techniques An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 30

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Observation bc82d4ab-36da-4fe8-ac73-c5a431e8e900 · inbound

Sequential simulation-based inference for extreme mass ratio inspirals cites this paper.

Sequential simulation-based inference for extreme mass ratio inspirals An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 120

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Observation 2bb23aa0-67f3-4102-9612-84175684c8b9 · inbound

A pipeline for searching and fitting instrumental glitches in LISA data cites this paper.

A pipeline for searching and fitting instrumental glitches in LISA data An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 64

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Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data cites this paper.

Multiband parameter estimation with phase coherence and extrinsic marginalization: Extracting more information from low-SNR CBC signals in LISA data An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 102

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Observation 4795d657-5d0c-4765-a01e-6c55ea376026 · inbound

Across the Horizon: On Gravitational Wave Flux Laws and Tests of Gravity cites this paper.

Across the Horizon: On Gravitational Wave Flux Laws and Tests of Gravity An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 180

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Observation cc46e73f-dbee-4e37-86b7-a10f494849e6 · inbound

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises cites this paper.

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 26

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Observation d47c40d9-cf11-45f6-9510-0418d44e9339 · inbound

A Fog Over the Cosmological SGWB: Unresolved Massive Black Hole Binaries in the LISA Band cites this paper.

A Fog Over the Cosmological SGWB: Unresolved Massive Black Hole Binaries in the LISA Band An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 50

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Observation a55e7533-df2e-4881-a68a-804b415d4bcd · inbound

Science of the LISA mission: A Summary for the European Strategy for Particle Physics cites this paper.

Science of the LISA mission: A Summary for the European Strategy for Particle Physics An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 21

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\texttt{GWBird}: a toolkit for the characterization of the Stochastic Gravitational Wave Background for Ground, Space, and Pulsar Timing Array detectors cites this paper.

\texttt{GWBird}: a toolkit for the characterization of the Stochastic Gravitational Wave Background for Ground, Space, and Pulsar Timing Array detectors An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 23

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Observation 957583c5-4bc5-4f65-bade-eb07004b0aa8 · inbound

Resonant interactions from dynamical perturbers on generic orbits around an extreme mass ratio inspiral cites this paper.

Resonant interactions from dynamical perturbers on generic orbits around an extreme mass ratio inspiral An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 34

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Observation 4efc5dcc-19cf-4015-b681-af85be0b1dc2 · inbound

Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA cites this paper.

Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:09.566587Z

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Observation 2abe45be-968b-421f-95c2-8eb675e2c92b · inbound

When vacuum breaks: a self-consistency test for astrophysical environments in extreme mass ratio inspirals cites this paper.

When vacuum breaks: a self-consistency test for astrophysical environments in extreme mass ratio inspirals An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 65

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Observation d97ba288-1446-48ee-b125-c4dc7f43ade1 · inbound

First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals cites this paper.

First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 53

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Toward claiming a detection of gravitational memory cites this paper.

Toward claiming a detection of gravitational memory An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 197

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Observation 74e852c3-0a96-496c-8534-9fcbc067a564 · inbound

Systematic biases in parameter estimation on LISA binaries. II. The effect of excluding higher harmonics for spin-aligned, high-mass binaries cites this paper.

Systematic biases in parameter estimation on LISA binaries. II. The effect of excluding higher harmonics for spin-aligned, high-mass binaries An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 57

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Observation f5367f24-3417-46ac-be9f-4bbabc541f25 · inbound

Inferring the population properties of galactic binaries from LISA's stochastic foreground cites this paper.

Inferring the population properties of galactic binaries from LISA's stochastic foreground An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:40:18.759411Z

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Observation b32bdd68-55ce-4a10-b74b-626c4b3a8598 · inbound

Pre-localization of Massive Black Hole Binaries in the Millihertz Band cites this paper.

Pre-localization of Massive Black Hole Binaries in the Millihertz Band An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 22

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Observation 34ffbec5-66d2-4e14-a496-73cc8cd3386a · inbound

Ringdown Signatures of Dehnen Dark Matter Halos: Fluid Modes and Detectability with Space-Based Detectors cites this paper.

Ringdown Signatures of Dehnen Dark Matter Halos: Fluid Modes and Detectability with Space-Based Detectors An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 102

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Observation 91517a6b-8820-4c7e-8f13-d84a828cd720 · inbound

The WDM Time-Frequency Transform in Gravitational-Wave Data Analysis I: Formalism cites this paper.

The WDM Time-Frequency Transform in Gravitational-Wave Data Analysis I: Formalism An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:29:38.636889Z

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Observation 3866e938-7023-4444-a4ae-d2703218e6b6 · inbound

Neural posterior estimation of Galactic Binary signals for the LISA mission cites this paper.

Neural posterior estimation of Galactic Binary signals for the LISA mission An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:14:25.954124Z

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Observation 0a1d6d5b-ad2f-41aa-8f42-3e76c58c7cc6 · inbound

Residual Galactic binary foreground in LISA stochastic gravitational-wave background inference: source power concentration and spectral degeneracy cites this paper.

Residual Galactic binary foreground in LISA stochastic gravitational-wave background inference: source power concentration and spectral degeneracy An efficient GPU-accelerated multi-source global fit pipeline for LISA data analysis

Reference 21

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