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  Bayesian Model Selection for LISA Pathfinder

Karnesis, N., Nofrarias, M., Sopuerta, C. F., Gibert, F., Armano, M., Audley, H., et al. (2014). Bayesian Model Selection for LISA Pathfinder. Physical Review D, 89: 062001. doi:10.1103/PhysRevD.89.062001.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-8057-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-8058-D
Genre: Journal Article

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 Creators:
Karnesis, Nikolaos, Author
Nofrarias, Miquel, Author
Sopuerta, Carlos F., Author
Gibert, Ferran, Author
Armano, Michele, Author
Audley, H.1, Author              
Congedo, Giuseppe, Author
Diepholz, Ingo1, Author              
Ferraioli, Luigi, Author
Hewitson, Martin2, Author              
Hueller, Mauro, Author
Korsakova, Natalia1, Author
Plagnol, Eric, Author
Vitale, Stefano, Author
Affiliations:
1Laser Interferometry & Gravitational Wave Astronomy, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24010              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Physics, Data Analysis, Statistics and Probability, physics.data-an
 Abstract: The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate it: The Reversible Jump Markov Chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments where the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored.

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 Dates: 2013-04-162014-06-052014
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: arXiv: 1304.4436
DOI: 10.1103/PhysRevD.89.062001
URI: http://arxiv.org/abs/1304.4436
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Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
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Publ. Info: Lancaster, Pa. : American Physical Society
Pages: - Volume / Issue: 89 Sequence Number: 062001 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: http://pubman.mpdl.mpg.de/cone/journals/resource/111088197762258