Mock LISA data challenge for the Galactic white dwarf binaries

Błaut, Arkadiusz; Babak, S.; Królak, A.

doi:10.1103/physrevd.81.063008

Cited by 43 publications

(41 citation statements)

References 36 publications

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“…Several studies have looked at parameter estimation for individual white dwarf binaries in the Milky Way [35][36][37]. We extend these studies to consider how the individual observations can be combined to infer the spatial and mass distributions of white dwarf binaries in the Galaxy.…”

Section: White Dwarf Binaries In the Milky Waymentioning

confidence: 99%

Astrophysical model selection in gravitational wave astronomy

2012

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Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve $5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

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Section: White Dwarf Binaries In the Milky Waymentioning

confidence: 99%

Astrophysical model selection in gravitational wave astronomy

2012

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“…The BAM codes themselves, which have been lost to the sands of time, did not model the evolution of a binary's orbital period (nor did Challenge 2). Other existing algorithms which have worn their teeth on the MLDC data sets include an F-statistic maximization scheme using a Nelder-Mead simplex algorithm [11], a hierarchical cleaning algorithm (also employing the Fstatistic) [12] and an MCMC search algorithm featuring a Markovian delayed rejection proposal distribution [13]. Beyond MLDC entries there have been numerous proof of principal studies including (but not limited to), Refs.…”

Section: Introductionmentioning

confidence: 99%

Detection pipeline for Galactic binaries in LISA data

Littenberg

2011

Phys. Rev. D

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The Galaxy is suspected to contain hundreds of millions of binary white dwarf systems, a large fraction of which will have sufficiently small orbital period 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 catalogue 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 > ∼ 10000 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.

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“…First, in the case of model (23) one can show that Eq. (21) can be re-written in the form of a linear combination of oscillating functions [32][33][34] as…”

Section: Frequency Domain Model For Gravitational-wave Signalmentioning

confidence: 99%

Gravitational-wave parameter estimation with gaps in LISA: A Bayesian data augmentation method

et al. 2019

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By listening to gravity in the low frequency band, between 0.1 mHz and 1 Hz, the future spacebased gravitational-wave observatory LISA will be able to detect tens of thousands of astrophysical sources from cosmic dawn to the present. The detection and characterization of all resolvable sources is a challenge in itself, but LISA data analysis will be further complicated by interruptions occurring in the interferometric measurements. These interruptions will be due to various causes occurring at various rates, such as laser frequency switches, high-gain antenna re-pointing, orbit corrections, or even unplanned random events. Extracting long-lasting gravitational-wave signals from gapped data raises problems such as noise leakage and increased computational complexity. We address these issues by using Bayesian data augmentation, a method that reintroduces the missing data as auxiliary variables in the sampling of the posterior distribution of astrophysical parameters. This provides a statistically consistent way to handle gaps while improving the sampling efficiency and mitigating leakage effects. We apply the method to the estimation of galactic binaries parameters with different gap patterns, and we compare the results to the case of complete data.

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Mock LISA data challenge for the Galactic white dwarf binaries

Cited by 43 publications

References 36 publications

Astrophysical model selection in gravitational wave astronomy

Astrophysical model selection in gravitational wave astronomy

Detection pipeline for Galactic binaries in LISA data

Gravitational-wave parameter estimation with gaps in LISA: A Bayesian data augmentation method

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