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  Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models

Colleoni, M., Mateu-Lucena, M., Estellés, H., García-Quirós, C., Keitel, D., Pratten, G., et al. (in preparation). Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models.

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2010.05830.pdf (Preprint), 5MB
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 Creators:
Colleoni, Marta, Author
Mateu-Lucena, Maite, Author
Estellés, Héctor, Author
García-Quirós, Cecilio, Author
Keitel, David, Author
Pratten, Geraint, Author
Ramos Buades, Antoni1, Author           
Husa, Sascha, Author
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, ou_1933290              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE
 Abstract: We re-analyse the gravitational-wave event GW190412 with state-of-the-art
phenomenological waveform models. This event, which has been associated with a
black hole merger, is interesting due to the significant contribution from
subdominant harmonics. We use both frequency-domain and time-domain waveform
models. The PhenomX waveform models constitute the fourth generation of
frequency-domain phenomenological waveforms for black hole binary coalescence;
they have more recently been complemented by the time-domain PhenomT models,
which open up new strategies to model precession and eccentricity, and to
perform tests of general relativity with the phenomenological waveforms
approach. Both PhenomX and PhenomT have been constructed with similar
techniques and accuracy goals, and due to their computational efficiency this
"generation X" model family allows the routine use of subdominant spherical
harmonics in Bayesian inference. We show the good agreement between these and
other state-of-the-art waveform models for GW190412, and discuss the
improvements over the previous generation of phenomenological waveform models.
We also discuss practical aspects of Bayesian inference such as run
convergence, variations of sampling parameters, and computational cost.

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 Dates: 2020-10-122020-10-13
 Publication Status: Not specified
 Pages: 25 pages, 18 figures. Data release: https://doi.org/10.5281/zenodo.4079188
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2010.05830
 Degree: -

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