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A number of open problems hinder our present ability to extract scientific information from data that will be gathered by the near-future gravitational-wave mission LISA.
The unique potential of extreme mass-ratio inspirals for gravitational-wave astronomy
C. P. L. Berry et al · 1903
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Stochastic template placement algorithm for gravitational wave data analysis
I. W. Harry, B. Allen, and B. S. Sathyaprakash · 1912
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Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
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Angular resolution of the LISA gravitational wave detector
C. Cutler · 1998
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J. W. Armstrong, F. B. Estabrook, and M. Tinto · 1999
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Comparison of search templates for gravitational waves from binary inspiral
T. Damour, B. R. Iyer, and B. S. Sathyaprakash · 2001
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Information theory, inference, and learning algorithms
D. J. C. MacKay · 2003
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Iterative methods for sparse linear systems
Y. Saad · 2003
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LISA capture sources: Approximate waveforms, signal-to-noise ratios, and parameter estimation accuracy
L. Barack and C. Cutler · 2004
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Event rate estimates for LISA extreme mass ratio capture sources
J. R. Gair et al · 2004
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Scattered data approximation
H. Wendland · 2004
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Gaussian processes for machine learning
C. E. Rasmussen and C. K. I. Williams · 2006
Cited alongside, same era.
Gravitational wave snapshots of generic extreme mass ratio inspirals
S. Drasco and S. A. Hughes · 2006
Cited alongside, same era.
LISA detections of massive black hole inspirals: Parameter extraction errors due to inaccurate template waveforms
C. Cutler and M. Vallisneri · 2007
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“Kludge” gravitational waveforms for a test-body orbiting a Kerr black hole
S. Babak et al · 2007
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LISACode: A scientific simulator of LISA
A. Petiteau et al · 2008
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Novel method for incorporating model uncertainties into gravitational wave parameter estimates
C. J. Moore and J. R. Gair · 2014
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Improved analytic extreme-mass-ratio inspiral model for scoping out eLISA data analysis
A. J. K. Chua and J. R. Gair · 2015
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Improving gravitational-wave parameter estimation using Gaussian process regression
C. J. Moore, C. P. L. Berry, A. J. K. Chua, and J. R. Gair · 2016
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Fast methods for training Gaussian processes on large datasets
C. J. Moore, A. J. K. Chua, C. P. L. Berry, and J. R. Gair · 2016
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LSC Algorithm Library Suite, 2016
LIGO Scientific Collaboration · 2016
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Augmented kludge waveforms and Gaussian process regression for EMRI data analysis
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Building a stochastic template bank for detecting massive black hole binaries
S. Babak · 2008
Cited alongside, same era.
Gravitational self-force in extreme mass-ratio inspirals
L. Barack · 2009
Cited alongside, same era.
An algorithm for the detection of extreme mass ratio inspirals in LISA data
S. Babak, J. R. Gair, and E. K. Porter · 2009
Cited alongside, same era.
Random template banks and relaxed lattice coverings
C. Messenger, R. Prix, and M. A. Papa · 2009
Cited alongside, same era.
Matching post-Newtonian and numerical relativity waveforms: Systematic errors and a new phenomenological model for nonprecessing black hole binaries
L. Santamaría et al · 2010
Cited alongside, same era.
The motion of point particles in curved spacetime
E. Poisson, A. Pound, and I. Vega · 2011
Cited alongside, same era.
A. J. K. Chua · 2016
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LISA: Laser Interferometer Space Antenna
K. Danzmann et al · 2017
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Science with the space-based interferometer LISA. V. Extreme mass-ratio inspirals
S. Babak et al · 2017
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Augmented kludge waveforms for detecting extreme-mass-ratio inspirals
A. J. K. Chua, C. J. Moore, and J. R. Gair · 2017
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Impact of galactic foreground characterization on a global analysis for the LISA gravitational wave observatory
T. Robson and N. Cornish · 2017
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EMRI Kludge Suite, version 0.5.0, 2019
A. J. K. Chua, J. R. Gair, and M. L. Katz · 2019
Closest in time.
LISA Data Challenges, 2019
LISA Data Challenge Working Group · 2019
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