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Probing black holes at low redshift using LISA EMRI observations

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 Added by Jonathan R. Gair
 Publication date 2009
  fields Physics
and research's language is English




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One of the most exciting potential sources of gravitational waves for the Laser Interferometer Space Antenna (LISA) are the inspirals of approximately solar mass compact objects into massive black holes in the centres of galaxies - extreme mass ratio inspirals (EMRIs). LISA should observe between a few tens and a few hundred EMRIs over the mission lifetime, mostly at low redshifts (z < 1). Each observation will provide a measurement of the parameters of the host system to unprecendented precision. LISA EMRI observations will thus offer a new and unique way to probe black holes at low redshift. In this article we provide a description of the population of EMRI events that LISA is likely to observe, and describe how the numbers of events vary with changes in the underlying assumptions about the black hole population. We also provide fitting functions that characterise LISAs ability to detect EMRIs and which will allow LISA event rates to be computed for arbitrary population models. We finish with a discussion of an ongoing programme that will use these results to assess what constraints LISA observations could place on galaxy evolution models.



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Extreme mass ratio inspirals (EMRIs) are thought to be one of the most exciting gravitational wave sources to be detected with LISA. Due to their complicated nature and weak amplitudes the detection and parameter estimation of such sources is a challenging task. In this paper we present a statistical methodology based on Bayesian inference in which the estimation of parameters is carried out by advanced Markov chain Monte Carlo (MCMC) algorithms such as parallel tempering MCMC. We analysed high and medium mass EMRI systems that fall well inside the low frequency range of LISA. In the context of the Mock LISA Data Challenges, our investigation and results are also the first instance in which a fully Markovian algorithm is applied for EMRI searches. Results show that our algorithm worked well in recovering EMRI signals from different (simulated) LISA data sets having single and multiple EMRI sources and holds great promise for posterior computation under more realistic conditions. The search and estimation methods presented in this paper are general in their nature, and can be applied in any other scenario such as AdLIGO, AdVIRGO and Einstein Telescope with their respective response functions.
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