142 citations · 1k across the 19 of their papers we have counts for
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Comparison of Signals from Gravitational Wave Detectors with Instantaneous Time-Frequency Maps
Alexander Stroeer, Lindy Blackburn, Jordan Camp
Gravitational wave astronomy relies on the use of multiple detectors, so that coincident detections may distinguish real signals from instrumental artifacts, and also so that relat…
The influence of short term variations in AM CVn systems on LISA measurements
Alexander Stroeer, Gijs Nelemans
We study the effect of short term variations of the evolution of AM CVn systems on their gravitational wave emissions and in particular LISA observations. We model the systems acco…
Status of NINJA: the Numerical INJection Analysis project
Benjamin Aylott, John G. Baker, William D. Boggs +74
The 2008 NRDA conference introduced the Numerical INJection Analysis project (NINJA), a new collaborative effort between the numerical relativity community and the data analysis co…
Ninja data analysis with a detection pipeline based on the Hilbert-Huang Transform
Alexander Stroeer, Jordan Camp
The Ninja data analysis challenge allowed the study of the sensitivity of data analysis pipelines to binary black hole numerical relativity waveforms in simulated Gaussian noise at…
Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project
Benjamin Aylott, John G. Baker, William D. Boggs +74
The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose…
Report on the second Mock LISA Data Challenge
Stanislav Babak, John G. Baker, Matthew J. Benacquista +40
The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of several data set…
Inference on inspiral signals using LISA MLDC data
Christian Röver, Alexander Stroeer, Ed Bloomer +11
In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Chal…
Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets
Alexander Stroeer, John Veitch, Christian Roever +11
We report on the analysis of selected single source data sets from the first round of the Mock LISA Data Challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pi…
Report on the first round of the Mock LISA Data Challenges
K. A. Arnaud, G. Auger, S. Babak +42
The Mock LISA Data Challenges (MLDCs) have the dual purpose of fostering the development of LISA data analysis tools and capabilities, and demonstrating the technical readiness alr…
Automatic Bayesian inference for LISA data analysis strategies
Alexander Stroeer, Jonathan Gair, Alberto Vecchio
We demonstrate the use of automatic Bayesian inference for the analysis of LISA data sets. In particular we describe a new automatic Reversible Jump Markov Chain Monte Carlo method…