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Journal Article

Fully automated end-to-end pipeline for massive black hole binary signal extraction from LISA data


Katz,  Michael L.
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Katz, M. L. (2022). Fully automated end-to-end pipeline for massive black hole binary signal extraction from LISA data. Physical Review D, 105(415 ): 044055. doi:10.1103/PhysRevD.105.044055.

Cite as: https://hdl.handle.net/21.11116/0000-0009-787E-4
The LISA Data Challenges Working Group within the LISA Consortium has started
publishing datasets to benchmark, compare, and build LISA data analysis
infrastructure as the Consortium prepares for the launch of the mission. We
present our solution to the dataset from LISA Data Challenge (LDC) 1A
containing a single massive black hole binary signal. This solution is built
from a fully-automated and GPU-accelerated pipeline consisting of three
segments: a brute-force initial search; a refining search that uses the
efficient Likelihood computation technique of Relative Binning (also called
Heterodyning) to locate the maximum Likelihood point; and a parameter
estimation portion that also takes advantage of the speed of the Relative
Binning method. This pipeline takes tens of minutes to evolve from randomized
initial parameters throughout the prior volume to a converged final posterior
distribution. Final posteriors are shown for both datasets from LDC 1A: one
noiseless data stream and one containing additive noise. A posterior
distribution including higher harmonics is also shown for a self-injected
waveform with the same source parameters as is used in the original LDC 1A
dataset. This higher-mode posterior is shown in order to provide a more
realistic distribution on the parameters of the source.