Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Physical Review D Année : 2020

Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation

Résumé

A number of open problems hinder our present ability to extract scientific information from data that will be gathered by the near-future gravitational-wave mission LISA. Many of these relate to the modeling, detection, and characterization of signals from binary inspirals with an extreme component-mass ratio of ≲10-4. In this paper, we draw attention to the issue of systematic error in parameter estimation due to the use of fast but approximate waveform models; this is found to be relevant for extreme-mass-ratio inspirals even in the case of waveforms with ≳90% overlap accuracy and moderate (≳30) signal-to-noise ratios. A scheme that uses Gaussian processes to interpolate and marginalize over waveform error is adapted and investigated as a possible precursor solution to this problem. Several new methodological results are obtained, and the viability of the technique is successfully demonstrated on a three-parameter example in the setting of the LISA Data Challenge.
Fichier principal
Vignette du fichier
chua2020.pdf (667.15 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-02475263 , version 1 (30-05-2024)

Identifiants

Citer

Alvin J.K. Chua, Natalia Korsakova, Christopher J. Moore, Jonathan R. Gair, Stanislav Babak. Gaussian processes for the interpolation and marginalization of waveform error in extreme-mass-ratio-inspiral parameter estimation. Physical Review D, 2020, 101 (4), pp.044027. ⟨10.1103/PhysRevD.101.044027⟩. ⟨hal-02475263⟩
86 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More