Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.
Report on the second Mock LISA Data Challenge
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abstract
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 sets containing simulated instrument noise and gravitational-wave sources of undisclosed parameters. Participants are asked to analyze the data sets and report the maximum information about source parameters. The challenges are being released in rounds of increasing complexity and realism: in this proceeding we present the results of Challenge 2, issued in January 2007, which successfully demonstrated the recovery of signals from supermassive black-hole binaries, from ~20,000 overlapping Galactic white-dwarf binaries, and from the extreme-mass-ratio inspirals of compact objects into central galactic black holes.
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Inpainting over the cracks: challenges of applying pre-merger searches for massive black hole binaries to realistic LISA datasets
Inpainting allows recovery of pre-merger massive black hole binary signals in LISA data despite gaps and overlaps.