M. Trias

Massive black-hole binary inspirals: results from the LISA parameter estimation taskforce

The LISA Parameter Estimation Taskforce was formed in September 2007 to provide the LISA Project with vetted codes, source distribution models and results related to parameter estimation. The Taskforces goal is to be able to quickly calculate the impact of any mission design changes on LISAs science capabilities, based on reasonable estimates of the distribution of astrophysical sources in the universe. This paper describes our Taskforces work on massive black-hole binaries (MBHBs). Given present uncertainties in the formation history of MBHBs, we adopt four different population models, based on (i) whether the initial black-hole seeds are small or large and (ii) whether accretion is efficient or inefficient at spinning up the holes. We compare four largely independent codes for calculating LISAs parameter-estimation capabilities. All codes are based on the Fisher-matrix approximation, but in the past they used somewhat different signal models, source parametrizations and noise curves. We show that once these differences are removed, the four codes give results in extremely close agreement with each other. Using a code that includes both spin precession and higher harmonics in the gravitational-wave signal, we carry out Monte Carlo simulations and determine the number of events that can be detected and accurately localized in our four population models.

Searching for gravitational waves from Cassiopeia A with LIGO

We describe a search underway for periodic gravitational waves from the central compact object in the supernova remnant Cassiopeia A. The object is the youngest likely neutron star in the Galaxy. Its position is well known, but the object does not pulse in any electromagnetic radiation band and thus presents a challenge in searching the parameter space of frequency and frequency derivatives. We estimate that a fully coherent search can, with a reasonable amount of time on a computing cluster, achieve a sensitivity at which it is theoretically possible (though not likely) to observe a signal even with the initial LIGO noise spectrum. Cassiopeia A is only the second object after the Crab pulsar for which this is true. The search method described here can also obtain interesting results for similar objects with current LIGO sensitivity.

LISA observations of supermassive black holes: Parameter estimation using full post-Newtonian inspiral waveforms

We study parameter estimation of supermassive black hole binary systems in the final stage of inspiral using the full post-Newtonian gravitational waveforms. We restrict our analysis to systems in circular orbit with negligible spins, in the mass range

The Mock LISA Data Challenges: from Challenge 1B to Challenge 3

{10}^{8}{M}_{\ensuremath{\bigodot}}\ensuremath{-}{10}^{5}{M}_{\ensuremath{\bigodot}}

Weak lensing effects in the measurement of the dark energy equation of state with LISA

, and compare the results with those arising from the commonly used restricted post-Newtonian approximation. The conclusions of this work are particularly important with regard to the astrophysical reach of future Laser Interferometer Space Antenna measurements. Our analysis clearly shows that modeling the inspiral with the full post-Newtonian waveform, not only extends the reach to higher mass systems, but also improves in general the parameter estimation. In particular, there are remarkable improvements in angular resolution and distance measurement for systems with a total mass higher than

Studying stellar binary systems with the Laser Interferometer Space Antenna using delayed rejection Markov chain Monte Carlo methods

5\ifmmode\times\else\texttimes\fi{}{10}^{6}{M}_{\ensuremath{\bigodot}}

LISA parameter estimation of supermassive black holes

, as well as a large improvement in the mass determination.

Markov chain Monte Carlo searches for galactic binaries in Mock LISA Data Challenge 1B data sets

The Mock LISA Data Challenges are a programme to demonstrate and encourage the development of LISA data-analysis capabilities, tools and techniques. At the time of this workshop, three rounds of challenges had been completed, and the next was about to start. In this paper we provide a critical analysis of the entries to the latest completed round, Challenge 1B. The entries confirm the consolidation of a range of data-analysis techniques for galactic and massive-black-hole binaries, and they include the first convincing examples of detection and parameter estimation of extreme-mass-ratio inspiral sources. In this paper we also introduce the next round, Challenge 3. Its data sets feature more realistic waveform models (e.g., galactic binaries may now chirp, and massive-black-hole binaries may precess due to spin interactions), as well as new source classes (bursts from cosmic strings, isotropic stochastic backgrounds) and more complicated nonsymmetric instrument noise.

Observing Supermassive Black Hole Binary Systems with LISA

The Laser Interferometer Space Antenna’s (LISA’s) observation of supermassive binary black holes (SMBBH) could provide a new tool for precision cosmography. Inclusion of subdominant signal harmonics in the inspiral signal allows for high-accuracy sky localization, dramatically improving the chances of finding the host galaxy and obtaining its redshift. A SMBBH merger can potentially have component masses from a wide range (105–108M⊙) over which parameter accuracies vary considerably. We perform an in-depth study in order to understand (i) what fraction of possible SMBBH mergers allow for sky localization, depending on the parameters of the source, and (ii) how accurately w can be measured when the host galaxy can be identified. We also investigate how accuracies on all parameters improve when a knowledge of the sky position can be folded into the estimation of errors. We find that w can be measured to within a few percent in most cases, if the only error in measuring the luminosity distance is due to LISA’s instrumental noise and the confusion background from Galactic binaries. However, weak lensing-induced errors will severely degrade the accuracy with which w can be obtained, emphasizing that methods to mitigate weak lensing effects would be required to take advantage of LISA’s full potential.

Delayed rejection schemes for efficient Markov-Chain Monte-Carlo sampling of multimodal distributions

Bayesian analysis of Laser Interferometer Space Antenna (LISA) data sets based on Markov chain Monte Carlo methods has been shown to be a challenging problem, in part due to the complicated structure of the likelihood function consisting of several isolated local maxima that dramatically reduces the efficiency of the sampling techniques. Here we introduce a new fully Markovian algorithm, a delayed rejection Metropolis?Hastings Markov chain Monte Carlo method, to efficiently explore these kind of structures and we demonstrate its performance on selected LISA data sets containing a known number of stellar-mass binary signals embedded in Gaussian stationary noise.