Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.Comment: Amaldi-7 conference proceedings; requires jpconf style file

Detecting extreme mass ratio inspiral events in LISA data using the Hierarchical Algorithm for Clusters and Ridges (HACR)

One of the most exciting prospects for the Laser Interferometer Space Antenna (LISA) is the detection of gravitational waves from the inspirals of stellar-mass compact objects into supermassive black holes. Detection of these sources is an extremely challenging computational problem due to the large parameter space and low amplitude of the signals. However, recent work has suggested that the nearest extreme mass ratio inspiral (EMRI) events will be sufficiently loud that they might be detected using computationally cheap, template-free techniques, such as a time-frequency analysis. In this paper, we examine a particular time-frequency algorithm, the Hierarchical Algorithm for Clusters and Ridges (HACR). This algorithm searches for clusters in a power map and uses the properties of those clusters to identify signals in the data. We find that HACR applied to the raw spectrogram performs poorly, but when the data is binned during the construction of the spectrogram, the algorithm can detect typical EMRI events at distances of up to $\sim2.6$Gpc. This is a little further than the simple Excess Power method that has been considered previously. We discuss the HACR algorithm, including tuning for single and multiple sources, and illustrate its performance for detection of typical EMRI events, and other likely LISA sources, such as white dwarf binaries and supermassive black hole mergers. We also discuss how HACR cluster properties could be used for parameter extraction.Comment: 21 pages, 11 figures, submitted to Class. Quantum Gravity. Modified and shortened in light of referee's comments. Updated results consider tuning over all three HACR thresholds, and show 10-15% improvement in detection rat

Electromagnetic Fields of Separable Space-Times

Carter derived the forms of the metric and the vector potentials of the space-times in which the relativistic Schrodinger equation for the motion of a charged particle separates. Here we show that on each `spheroidal' surface a rotation rate exists such that relative to those rotating axes the electric and magnetic fields are parallel and orthogonal to the spheroid which is thus an equipotential in those axes. All the finite Carter separable systems without magnetic monopoles or gravomagnetic NUT monopoles have the same gyromagnetic ratio as the Dirac electron.Comment: 9 pages; accepted for publication in Class. Quantum Gra

Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm

We describe a hybrid evolutionary algorithm that can simultaneously search for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The algorithm mixes evolutionary computation, Metropolis-Hastings methods and Nested Sampling. The inspiral of SMBHBs presents an interesting problem for gravitational wave data analysis since, due to the LISA response function, the sources have a bi-modal sky solution. We show here that it is possible not only to detect multiple SMBHBs in the data stream, but also to investigate simultaneously all the various modes of the global solution. In all cases, the algorithm returns parameter determinations within $5\sigma$ (as estimated from the Fisher Matrix) of the true answer, for both the actual and antipodal sky solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure

Classifying LISA gravitational wave burst signals using Bayesian evidence

We consider the problem of characterisation of burst sources detected with the Laser Interferometer Space Antenna (LISA) using the multi-modal nested sampling algorithm, MultiNest. We use MultiNest as a tool to search for modelled bursts from cosmic string cusps, and compute the Bayesian evidence associated with the cosmic string model. As an alternative burst model, we consider sine-Gaussian burst signals, and show how the evidence ratio can be used to choose between these two alternatives. We present results from an application of MultiNest to the last round of the Mock LISA Data Challenge, in which we were able to successfully detect and characterise all three of the cosmic string burst sources present in the release data set. We also present results of independent trials and show that MultiNest can detect cosmic string signals with signal-to-noise ratio (SNR) as low as ~7 and sine-Gaussian signals with SNR as low as ~8. In both cases, we show that the threshold at which the sources become detectable coincides with the SNR at which the evidence ratio begins to favour the correct model over the alternative.Comment: 21 pages, 11 figures, accepted by CQG; v2 has minor changes for consistency with accepted versio

Facing the LISA Data Analysis Challenge

By being the first observatory to survey the source rich low frequency region of the gravitational wave spectrum, the Laser Interferometer Space Antenna (LISA) will revolutionize our understanding of the Cosmos. For the first time we will be able to detect the gravitational radiation from millions of galactic binaries, the coalescence of two massive black holes, and the inspirals of compact objects into massive black holes. The signals from multiple sources in each class, and possibly others as well, will be simultaneously present in the data. To achieve the enormous scientific return possible with LISA, sophisticated data analysis techniques must be developed which can mine the complex data in an effort to isolate and characterize individual signals. This proceedings paper very briefly summarizes the challenges associated with analyzing the LISA data, the current state of affairs, and the necessary next steps to move forward in addressing the imminent challenges.Comment: 4 pages, no figures, Proceedings paper for the TeV Particle Astrophysics II conference held Aug 28-31 at the Univ. of Wisconsi

Novel Method for Incorporating Model Uncertainties into Gravitational Wave Parameter Estimates

Posterior distributions on parameters computed from experimental data using Bayesian techniques are only as accurate as the models used to construct them. In many applications these models are incomplete, which both reduces the prospects of detection and leads to a systematic error in the parameter estimates. In the analysis of data from gravitational wave detectors, for example, accurate waveform templates can be computed using numerical methods, but the prohibitive cost of these simulations means this can only be done for a small handful of parameters. In this work a novel method to fold model uncertainties into data analysis is proposed; the waveform uncertainty is analytically marginalised over using with a prior distribution constructed by using Gaussian process regression to interpolate the waveform difference from a small training set of accurate templates. The method is well motivated, easy to implement, and no more computationally expensive than standard techniques. The new method is shown to perform extremely well when applied to a toy problem. While we use the application to gravitational wave data analysis to motivate and illustrate the technique, it can be applied in any context where model uncertainties exist.Comment: 6 pages, 3 figures, accepted for publication in Physical Review Letter

Constraining properties of the black hole population using LISA

LISA should detect gravitational waves from tens to hundreds of systems containing black holes with mass in the range from 10 thousand to 10 million solar masses. Black holes in this mass range are not well constrained by current electromagnetic observations, so LISA could significantly enhance our understanding of the astrophysics of such systems. In this paper, we describe a framework for combining LISA observations to make statements about massive black hole populations. We summarise the constraints that LISA observations of extreme-mass-ratio inspirals might be able to place on the mass function of black holes in the LISA range. We also describe how LISA observations can be used to choose between different models for the hierarchical growth of structure in the early Universe. We consider four models that differ in their prescription for the initial mass distribution of black hole seeds, and in the efficiency of accretion onto the black holes. We show that with as little as 3 months of LISA data we can clearly distinguish between these models, even under relatively pessimistic assumptions about the performance of the detector and our knowledge of the gravitational waveforms.Comment: 12 pages, 3 figures, submitted to Class. Quantum Grav. for proceedings of 8th LISA Symposium; v2 minor changes for consistency with accepted versio

Gravitational-Wave Signature of an Inspiral into a Supermassive Horizonless Object

Event horizons are among the most intriguing of general relativity's predictions. Although on firm theoretical footing, direct indications of their existence have yet to be observed. With this motivation in mind, we explore here the possibility of finding a signature for event horizons in the gravitational waves (GWs) produced during the inspiral of stellar-mass compact objects (COs) into the supermassive ($\sim 10^6 M_\odot$) objects that lie at the center of most galaxies. Such inspirals will be a major source for LISA, the future space-based GW observatory. We contrast supermassive black holes with models in which the central object is a supermassive boson star (SMBS). Provided the COs interact only gravitationally with the SMBS, stable orbits exist not just outside the Schwarzschild radius but also inside the surface of the SMBS as well. The absence of an event horizon allows GWs from these orbits to be observed. Here we solve for the metric in the interior of a fairly generic class of SMBS and evolve the trajectory of an inspiraling CO from the Schwarzschild exterior through the plunge into the exotic SMBS interior. We calculate the approximate waveforms for GWs emitted during this inspiral. Geodesics within the SMBS surface will exhibit extreme pericenter precession and other features making the emitted GWs readily distinguishable from those emitted during an inspiral into a black hole.Comment: 20 pages, 9 figures, submitted to PR

Reconstructing the massive black hole cosmic history through gravitational waves

The massive black holes we observe in galaxies today are the natural end-product of a complex evolutionary path, in which black holes seeded in proto-galaxies at high redshift grow through cosmic history via a sequence of mergers and accretion episodes. Electromagnetic observations probe a small subset of the population of massive black holes (namely, those that are active or those that are very close to us), but planned space-based gravitational-wave observatories such as the Laser Interferometer Space Antenna (LISA) can measure the parameters of ``electromagnetically invisible'' massive black holes out to high redshift. In this paper we introduce a Bayesian framework to analyze the information that can be gathered from a set of such measurements. Our goal is to connect a set of massive black hole binary merger observations to the underlying model of massive black hole formation. In other words, given a set of observed massive black hole coalescences, we assess what information can be extracted about the underlying massive black hole population model. For concreteness we consider ten specific models of massive black hole formation, chosen to probe four important (and largely unconstrained) aspects of the input physics used in structure formation simulations: seed formation, metallicity ``feedback'', accretion efficiency and accretion geometry. For the first time we allow for the possibility of ``model mixing'', by drawing the observed population from some combination of the ``pure'' models that have been simulated. A Bayesian analysis allows us to recover a posterior probability distribution for the ``mixing parameters'' that characterize the fractions of each model represented in the observed distribution. Our work shows that LISA has enormous potential to probe the underlying physics of structure formation.Comment: 24 pages, 16 figures, submitted to Phys. Rev.