Approximate waveform templates for detection of extreme mass ratio inspirals with LISA

The inspirals of compact objects into massive black holes are some of the most exciting of the potential sources of gravitational waves for the planned Laser Interferometer Space Antenna (LISA). Observations of such extreme mass ratio inspirals (EMRIs) will not only reveal to us the properties of black holes in the Universe, but will allow us to verify that the space-time structure around massive compact objects agrees with the predictions of relativity. Detection of EMRI signals via matched filtering and interpretation of the observations will require models of the gravitational waveforms. The extreme mass ratio allows accurate waveforms to be computed from black hole perturbation theory, but this is computationally expensive and has not yet been fully developed. Ongoing research to scope out LISA data analysis algorithms requires waveforms that can be generated quickly in large numbers. To fulfil this purpose, families of approximate, "kludge", EMRI waveforms have been developed that capture the main features of true EMRI waveforms, but that can also be generated for a comparatively small computational cost. In this proceedings article, we briefly outline one such waveform family (the "numerical kludge"), its accuracy and some possible ways in which it might be improved in the future. Although accurate parameter extraction will require use of perturbative waveforms, these approximate waveforms are sufficiently faithful to the true waveforms that they may be able to play a role in detection of EMRIs in the LISA data.Comment: 3 pages; to appear in Proceedings of the Eleventh Marcel Grossmann meetin

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 inspirals with LISA using time-frequency methods II: search characterization

The inspirals of stellar-mass compact objects into supermassive black holes constitute some of the most important sources for LISA. Detection of these sources using fully coherent matched filtering is computationally intractable, so alternative approaches are required. In a previous paper (Wen and Gair 2005, gr-qc/0502100), we outlined a detection method based on looking for excess power in a time-frequency spectrogram of the LISA data. The performance of the algorithm was assessed using a single `typical' trial waveform and approximations to the noise statistics. In this paper we present results of Monte Carlo simulations of the search noise statistics and examine its performance in detecting a wider range of trial waveforms. We show that typical extreme mass ratio inspirals (EMRIs) can be detected at distances of up to 1--3 Gpc, depending on the source parameters. We also discuss some remaining issues with the technique and possible ways in which the algorithm can be improved.Comment: 15 pages, 9 figures, to appear in proceedings of GWDAW 9, Annecy, France, December 200

Detecting LISA sources using time-frequency techniques

The planned Laser Interferometer Space Antenna (LISA) will detect gravitational wave signals from a wide range of sources. However, disentangling individual signals from the source-dominated data stream is a challenging problem and the focus of much current research. The problems are particularly acute for detection of extreme mass ratio inspirals (EMRIs), for which the instantaneous signal amplitude is an order of magnitude below the level of the instrumental noise, and the parameter space of possible signals is too large to permit fully-coherent matched filtering. One possible approach is to attempt to identify sources in a time-frequency spectrogram of the LISA data. This is a computationally cheap method that may be useful as a first stage in a hierarchical analysis. Initial results, evaluated using a significantly simplified model of the LISA data stream, suggest that time-frequency techniques might be able to detect the nearest few tens of EMRI events. In this proceedings article, we briefly outline the methods that have so far been applied to the problem, initial results and possible future directions for the research.Comment: 3 pages; to appear in Proceedings of the Eleventh Marcel Grossmann meetin

Approximate Waveforms for Extreme-Mass-Ratio Inspirals in Modified Gravity Spacetimes

Extreme-mass-ratio inspirals, in which a stellar-mass compact object spirals into a supermassive black hole, are prime candidates for detection with space-borne milliHertz gravitational wave detectors, similar to the Laser Interferometer Space Antenna. The gravitational waves generated during such inspirals encode information about the background in which the small object is moving, providing a tracer of the spacetime geometry and a probe of strong-field physics. In this paper, we construct approximate, "analytic-kludge" waveforms for such inspirals with parameterized post-Einsteinian corrections that allow for generic, model-independent deformations of the supermassive black hole background away from the Kerr metric. These approximate waveforms include all of the qualitative features of true waveforms for generic inspirals, including orbital eccentricity and relativistic precession. The deformations of the Kerr metric are modeled using a recently proposed, modified gravity bumpy metric, which parametrically deforms the Kerr spacetime while ensuring that three approximate constants of the motion remain for geodesic orbits: a conserved energy, azimuthal angular momentum and Carter constant. The deformations represent modified gravity effects and have been analytically mapped to several modified gravity black hole solutions in four dimensions. In the analytic kludge waveforms, the conservative motion is modeled by a post-Newtonian expansion of the geodesic equations in the deformed spacetimes, which in turn induce modifications to the radiation-reaction force. These analytic-kludge waveforms serve as a first step toward complete and model-independent tests of General Relativity with extreme mass-ratio inspirals.Comment: v1: 28 pages, no figures; v2: minor changes for consistency with accepted version, 2 figures added showing sample waveforms; accepted by Phys. Rev.

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

Detecting extreme mass ratio inspirals with LISA using time–frequency methods

The inspirals of stellar-mass compact objects into supermassive black holes are some of the most important sources for LISA. Detection techniques based on fully coherent matched filtering have been shown to be computationally intractable. We describe an efficient and robust detection method that utilizes the time–frequency evolution of such systems. We show that a typical extreme mass ratio inspiral (EMRI) source could possibly be detected at distances of up to ~2 Gpc, which would mean ~tens of EMRI sources can be detected per year using this technique. We discuss the feasibility of using this method as a first step in a hierarchical search