Pith. sign in

REVIEW 7 cited by

Modular global-fit pipeline for LISA data analysis

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.10277 v2 pith:26OBMFUZ submitted 2025-01-17 gr-qc astro-ph.COastro-ph.IM

Modular global-fit pipeline for LISA data analysis

classification gr-qc astro-ph.COastro-ph.IM
keywords dataanalysislisachallengegravitationalpopulationsseveralsignals
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

We anticipate that the data acquired by the Laser Interferometer Space Antenna (LISA) will be dominated by the gravitational wave signals from several astrophysical populations. The analysis of these data is a new challenge and is the main focus of this paper. Numerous gravitational wave signals overlap in the time and/or frequency domain, and the possible correlation between them has to be taken into account during their detection and characterization. In this work, we present a method to address the LISA data analysis challenge; it is flexible and scalable for a number of sources and across several populations. Its performance is demonstrated on the simulated data LDC2a.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    gr-qc 2025-12 accept novelty 7.0

    Moderately mitigated glitch streams induce negligible to minor biases (0.04–0.6σ) in EMRI parameters while weakly mitigated streams with higher-SNR events can reach ~1σ biases, making EMRI inference more robust than f...

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

    astro-ph.HE 2026-07 conditional novelty 6.0

    Residual Galactic binaries left after catalog-matched recovery inflate the LISA flat-SGWB amplitude uncertainty by 13.6% when their amplitude is marginalized, and would bias it by ~120σ if omitted.

  3. Neural posterior estimation of Galactic Binary signals for the LISA mission

    astro-ph.IM 2026-06 unverdicted novelty 6.0

    Conditional normalizing flows perform likelihood-free parameter estimation for single and overlapping LISA galactic binaries, generating thousands of posterior samples per second after training on simulations.

  4. Inferring the population properties of galactic binaries from LISA's stochastic foreground

    astro-ph.HE 2026-02 unverdicted novelty 6.0

    A neural posterior estimator trained on simulated LISA foreground spectra recovers galactic binary population parameters, including total number, with good accuracy in validation tests.

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

    gr-qc 2026-02 accept novelty 6.0

    Omitting higher-order waveform harmonics can cause severe, spin-dependent parameter biases for massive LISA black-hole binaries, including confident localization in the wrong sky region.

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

    gr-qc 2026-05 unverdicted novelty 5.0

    Numerical ringdown waveforms for black holes in Dehnen dark matter profiles are generated and analyzed for detectability and parameter inference using second-generation TDI in space-based detectors such as LISA, Taiji...

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

    gr-qc 2025-10 conditional novelty 5.0

    A duration-scan self-consistency test on vacuum EMRI parameter posteriors flags unmodeled environmental effects without adding environmental parameters.