Laser Interferometer Space Antenna Data Research Articles

Global analysis of the gravitational wave signal from Galactic binaries

Galactic ultra compact binaries are expected to be the dominant source of gravitational waves in the milli-Hertz frequency band. Of the tens of millions of galactic binaries with periods shorter than an hour, it is estimated that a few tens of thousand will be resolved by the future Laser Interferometer Space Antenna (LISA). The unresolved remainder will be the main source of ``noise'' between 1-3 milli-Hertz. Typical galactic binaries are millions of years from merger, and consequently their signals will persist for the the duration of the LISA mission. Extracting tens of thousands of overlapping galactic signals and characterizing the unresolved component is a central challenge in LISA data analysis, and a key contribution to arriving at a global solution that simultaneously fits for all signals in the band. Here we present an end-to-end analysis pipeline for galactic binaries that uses trans-dimensional Bayesian inference to develop a time-evolving catalog of sources as data arrive from the LISA constellation.

Expanding the LISA Horizon from the Ground.

The Laser Interferometer Space Antenna (LISA) gravitational-wave (GW) observatory will be limited in its ability to detect mergers of binary black holes (BBHs) in the stellar-mass range. A future ground-based detector network, meanwhile, will achieve by the LISA launch date a sensitivity that ensures complete detection of all mergers within a volume >O(10) Gpc^{3}. We propose a method to use the information from the ground to revisit the LISA data in search for subthreshold events. By discarding spurious triggers that do not overlap with the ground-based catalogue, we show that the signal-to-noise threshold ρ_{LISA} employed in LISA can be significantly lowered, greatly boosting the detection rate. The efficiency of this method depends predominantly on the rate of false-alarm increase when the threshold is lowered and on the uncertainty in the parameter estimation for the LISA events. As an example, we demonstrate that while all current LIGO BBH-merger detections would have evaded detection by LISA when employing a standard ρ_{LISA}=8 threshold, this method will allow us to easily (possibly) detect an event similar to GW150914 (GW170814) in LISA. Overall, we estimate that the total rate of stellar-mass BBH mergers detected by LISA can be boosted by a factor ∼4 (≳8) under conservative (optimistic) assumptions. This will enable new tests using multiband GW observations, significantly aided by the greatly increased lever arm in frequency.

Detection pipeline for Galactic binaries in LISA data

The Galaxy is suspected to contain hundreds of millions of binary white dwarf systems, a large fraction of which will have sufficiently small orbital periods to emit gravitational radiation in band for space-based gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA). LISA's main science goal is the detection of cosmological events (supermassive black hole mergers, etc.) however the gravitational signal from the galaxy will be the dominant contribution to the data---including instrumental noise---over approximately two decades in frequency. The catalog of detectable binary systems will serve as an unparalleled means of studying the Galaxy. Furthermore, to maximize the scientific return from the mission, the data must be cleansed of the galactic foreground. We will present an algorithm that can accurately resolve and subtract $\ensuremath{\gtrsim}10\text{ }000$ of these sources from simulated data supplied by the Mock LISA Data Challenge Task Force. Using the time evolution of the gravitational wave frequency, we will reconstruct the position of the recovered binaries and show how LISA will sample the entire compact binary population in the Galaxy.

Detecting Double Degenerate Progenitors of SNe Ia with LISA

AbstractThe Galactic population of close white dwarf binaries is expected to provide the largest number of gravitational wave sources for low frequency detectors such as the Laser Interferometer Space Antenna (LISA). Current data analysis techniques have demonstrated the capability of resolving on the order of 104 white dwarf binaries from a 2 year observation. Resolved binaries are either at high frequencies or large amplitudes. Such systems are more likely to be high-mass binaries, a subset of which will be progenitors of SNe Ia in the double degenerate scenario. We report on results of a study of the properties of resolved binaries using a population synthesis model of the Galactic white dwarf binaries and a LISA data analysis algorithm using Mock LISA Data Challenge tools.

Characterizing spinning black hole binaries in eccentric orbits with LISA

The Laser Interferometer Space Antenna (LISA) is designed to detect gravitational wave signals from astrophysical sources, including those from coalescing binary systems of compact objects such as black holes. Colliding galaxies have central black holes that sink to the center of the merged galaxy and begin to orbit one another and emit gravitational waves. Some galaxy evolution models predict that the binary black hole system will enter the LISA band with significant orbital eccentricity, while other models suggest that the orbits will already have circularized. Using a full 17 parameter waveform model that includes the effects of orbital eccentricity, spin precession, and higher harmonics, we investigate how well the source parameters can be inferred from simulated LISA data. Defining the reference eccentricity as the value one year before merger, we find that for typical LISA sources, it will be possible to measure the eccentricity to an accuracy of parts in a thousand. The accuracy with which the eccentricity can be measured depends only very weakly on the eccentricity, making it possible to distinguish circular orbits from those with very small eccentricities. LISA measurements of the orbital eccentricity can help constraints theories of galaxy mergers in the early universe. Failing to account for the eccentricity in the waveform modeling can lead to a loss of signal power and bias the estimation of parameters such as the black hole masses and spins.

Discriminating between a stochastic gravitational wave background and instrument noise

The detection of a stochastic background of gravitational waves could significantly impact our understanding of the physical processes that shaped the early Universe. The challenge lies in separating the cosmological signal from other stochastic processes such as instrument noise and astrophysical foregrounds. One approach is to build two or more detectors and cross correlate their output, thereby enhancing the common gravitational wave signal relative to the uncorrelated instrument noise. When only one detector is available, as will likely be the case with the Laser Interferometer Space Antenna (LISA), alternative analysis techniques must be developed. Here we show that models of the noise and signal transfer functions can be used to tease apart the gravitational and instrument noise contributions. We discuss the role of gravitational wave insensitive "null channels" formed from particular combinations of the time delay interferometry, and derive a new combination that maintains this insensitivity for unequal arm length detectors. We show that, in the absence of astrophysical foregrounds, LISA could detect signals with energy densities as low as $\Omega_{\rm gw} = 6 \times 10^{-13}$ with just one month of data. We describe an end-to-end Bayesian analysis pipeline that is able to search for, characterize and assign confidence levels for the detection of a stochastic gravitational wave background, and demonstrate the effectiveness of this approach using simulated data from the third round of Mock LISA Data Challenges.

Classifying LISA gravitational wave burst signals using Bayesian evidence

We consider the problem of characterization of burst sources detected by 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 characterize 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.

The search for black hole binaries using a genetic algorithm

In this work, we use a genetic algorithm to search for the gravitational wave signal from the inspiralling massive black hole binaries in the simulated Laser Interferometer Space Antenna (LISA) data. We consider a single signal in the Gaussian instrumental noise. This is a first step in preparation for analysis of the third round of the mock LISA data challenge. We have extended a genetic algorithm utilizing the properties of the signal and the detector response function. The performance of this method is comparable, if not better, to already existing algorithms.

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

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.

Bayesian approach to the study of white dwarf binaries in LISA data: The application of a reversible jump Markov chain Monte Carlo method

The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme-mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals ``out of the box'', handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.

The Galactic gravitational wave foreground

I present an overview of the Galactic binaries that form the foreground for the ESA/NASA Laser Interferometer Space Antenna (LISA). The currently known population is discussed as well as current and near-future large-scale surveys that will find new systems. The astrophysics that can be done when the LISA data become available is presented, with particular attention to verification binaries, the overall Galactic populations, neutron star and black hole binaries and sources in globular clusters. I discuss the synergy with electro-magnetic observations and correct an error in the estimate of the number of LISA systems that can be found in the optical compared to Nelemans (2006a American Institute of Phys. Conf. Ser. vol 873) and conclude that at least several hundreds of systems should be detectable.

Simulations of extreme-mass-ratio inspirals using pseudospectral methods

Extreme-mass-ratio inspirals (EMRIs), stellar-mass compact objects (SCOs) inspiralling into a massive black hole, are one of the main sources of gravitational waves expected for the Laser Interferometer Space Antenna (LISA). To extract the EMRI signals from the expected LISA data stream, which will also contain the instrumental noise as well as other signals, we need very accurate theoretical templates of the gravitational waves that they produce. In order to construct those templates we need to account for the gravitational backreaction, that is, how the gravitational field of the SCO affects its own trajectory. In general relativity, the backreaction can be described in terms of a local self-force, and the foundations to compute it have been laid recently. Due to its complexity, some parts of the calculation of the self-force have to be performed numerically. Here, we report on an ongoing effort towards the computation of the self-force based on time-domain multi-grid pseudospectral methods.

Characterizing the gravitational wave signature from cosmic string cusps

Cosmic strings are predicted to form kinks and cusps that travel along the string at close to the speed of light. These disturbances are radiated away as highly beamed gravitational waves that produce a burst like pulse as the cone of emission sweeps past an observer. Gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA) and the Laser Interferometer Gravitational wave Observatory (LIGO) will be capable of detecting these bursts for a wide class of string models. Such a detection would illuminate the fields of string theory, cosmology, and relativity. Here we develop template based Markov Chain Monte Carlo (MCMC) techniques that can efficiently detect and characterize the signals from cosmic string cusps. We estimate how well the signal parameters can be recovered by the advanced LIGO-Virgo network and the LISA detector using a combination of MCMC and Fisher matrix techniques. We also consider joint detections by the ground and space based instruments. We show that a parallel tempered MCMC approach can detect and characterize the signals from cosmic string cusps, and we demonstrate the utility of this approach on simulated data from the third round of Mock LISA Data Challenges (MLDCs).

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

We are developing a Bayesian approach based on Markov chain Monte Carlo techniques to search for and extract information about white dwarf binary systems with the laser interferometer space antenna (LISA). Here we present results obtained by applying an initial implementation of this method to some of the data sets released in round 1B of the Mock LISA Data Challenges. For Challenges 1B.1.1a and 1b the signals were recovered with parameters lying within the 95.5% posterior probability interval and the correlation between the true and recovered waveform is in excess of 99%. Results were not submitted for Challenge 1B.1.1c due to some convergence problems of the algorithm; despite this, the signal was detected in a search over a 2 mHz band.

Improved time–frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ∼100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes. The long duration and large parameter space of EMRI signals make data analysis for these signals a challenging problem. One approach to EMRI data analysis is to use time–frequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a time–frequency spectrogram and (ii) extracting parameter estimates from the tracks. In this paper we discuss the results of applying these techniques to the latest round of the Mock LISA Data Challenge, Round 1B. This analysis included three new techniques not used in previous analyses: (i) a new chirp-based algorithm for track search for track detection; (ii) estimation of the inclination of the source to the line of sight; (iii) a Metropolis–Hastings Monte Carlo over the parameter space in order to find the best fit to the tracks.

Search for a stochastic gravitational-wave signal in the second round of the Mock LISA Data Challenges

The analysis method currently proposed to search for isotropic stochastic radiation with the Laser Interferometer Space Antenna (LISA) relies on the combined use of two LISA channels, one of which is insensitive to gravitational waves, such as the symmetrized Sagnac. For this method to work, it is essential to know how the instrumental noise power in the two channels are related to one another; however, no quantitative estimates of this key information are available to date. The purpose of our study is to assess the performance of the symmetrized Sagnac method for different levels of prior information regarding the instrumental noise. We develop a general approach in the framework of Bayesian inference and an end-to-end analysis algorithm based on Markov chain Monte Carlo methods to compute the posterior probability density functions of the relevant model parameters. We apply this method to data released as part of the second round of the Mock LISA Data Challenges. For the selected (and somewhat idealized) example cases considered here, we find that for a signal whose amplitude dominates the instrumental noise by a factor ≈25, a prior uncertainty of a factor ≈2 in the ratio between the power of the instrumental noise contributions in the two channels allows for the detection of isotropic stochastic radiation. More importantly, we provide a framework for more realistic studies of LISA's performance and development of analysis techniques in the context of searches for stochastic signals.

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

Extreme-mass-ratio inspirals (EMRIs) of compact objects with mass m ∼ 1–10 M⊙ into massive black holes with mass M ∼ 106 M⊙ can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ∼ 100 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.

Python and XML for Agile Scientific Computing

To set up a mock data challenge, the authors needed to string together a lot of existing and new code. In the mock LISA data challenges (MLDCs; http:// astrogravs.nasa.gov/docs/mldc), the challengers distribute several simulated LISA data sets to challenge participants; the data include GW signals from sources of undisclosed parameters as well as realistic instrument noise. Participants must analyze the data and report their estimates for the GW source parameters. These challenges are meant to be blind tests but not really contests: their greatest benefit comes from the quantitative comparison of results, analysis methods, and algorithm implementations. The laser interferometer space antenna (LISA), a space-borne GW observatory planned jointly by NASA and the European Space Agency, will assuredly detect a wide variety of GW sources throughout the universe, thanks mostly to the quietness of the space environment and to LISA's very long 5-million-km baseline.

Gaussianity of LISA’s confusion backgrounds

Data analysis for the proposed Laser Interferometer Space Antenna (LISA) will be complicated by the huge number of sources in the LISA band. In the frequency band ~10-^4 - 2×10^-3 Hz, galactic white dwarf binaries (GWDBs) are sufficiently dense in frequency space that it will be impossible to resolve most of them, and “confusion noise” from the unresolved Galactic binaries will dominate over instrumental noise in determining LISA's sensitivity to other sources in that band. Confusion noise from unresolved extreme-mass-ratio inspirals (EMRIs) could also contribute significantly to LISA's total noise curve. To date, estimates of the effect of LISA's confusion noise on matched-filter searches and their detection thresholds have generally approximated the noise as Gaussian, based on the central limit theorem. However in matched-filter searches, the appropriate detection threshold for a given class of signals may be located rather far out on the tail of the signal-to-noise probability distribution, where a priori it is unclear whether the Gaussian approximation is reliable. Using the Edgeworth expansion and the theory of large deviations, we investigate the probability distribution of the usual matched-filter detection statistic, far out on the tail of the distribution. We apply these tools to four somewhat idealized versions of LISA data searches: searches for EMRI signals buried in GWDB confusion noise, and searches for massive black hole binary signals buried in (i) GWDB noise, (ii) EMRI noise, and (iii) a sum of EMRI noise and Gaussian noise. Assuming reasonable short-distance cutoffs in the populations of confusion sources (since the very closest and hence strongest sources will be individually resolvable), modifications to the appropriate detection threshold, due to the non-Gaussianity of the confusion noise, turn out to be quite small for realistic cases. The smallness of the correction is partly due to the fact that these three types of sources evolve on quite different time scales, so no single background source closely resembles any search template. We also briefly discuss other types of LISA searches where the non-Gaussianity of LISA's confusion backgrounds could perhaps have a much greater impact on search reliability and efficacy.

A three-stage search for supermassive black-hole binaries in LISA data

Gravitational waves from the inspiral and coalescence of supermassive black-hole (SMBH) binaries with masses m1 ∼ m2 ∼ 106M⊙ are likely to be among the strongest sources for the Laser Interferometer Space Antenna (LISA). We describe a three-stage data-analysis pipeline designed to search for and measure the parameters of SMBH binaries in LISA data. The first stage uses a time–frequency track-search method to search for inspiral signals and provide a coarse estimate of the black-hole masses m1, m2 and the coalescence time of the binary tc. The second stage uses a sequence of matched-filter template banks, seeded by the first stage, to improve the measurement accuracy of the masses and coalescence time. Finally, a Markov chain Monte Carlo search is used to estimate all nine physical parameters of the binary (masses, coalescence time, distance, initial phase, sky position and orientation). Using results from the second stage substantially shortens the Markov chain burn-in time and allows us to determine the number of SMBH-binary signals in the data before starting parameter estimation. We demonstrate our analysis pipeline using simulated data from the first Mock LISA Data Challenge. We discuss our plan for improving this pipeline and the challenges that will be faced in real LISA data analysis.