Wednesday, April 17, 2013

1304.4436 (Nikolaos Karnesis et al.)

Bayesian Model Selection for LISA Pathfinder    [PDF]

Nikolaos Karnesis, Miquel Nofrarias, Carlos F. Sopuerta, Ferran Gibert, Michele Armano, Heather Audley, Giuseppe Congedo, Ingo Diepholz, Luigi Ferraioli, Martin Hewitson, Mauro Hueller, Natalia Korsakova, Eric Plagnol, and Stefano Vitale
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 LISA/eLISA concept. The Data Analysis (DA) 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 DA team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching to this problem is to recover the essential parameters of the LTP which describe 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 first to toy models and then, they are verified with full LTP models, where we investigate the correlation of the output of these methods with the design of the experiment itself.
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