The archive contains simulated LISA output data representing the instrument's response to several classes of gravitational wave sources. State-of-the-art model waveforms from these sources have been run through a simulation of LISA's response. The MLDA currently contains four source classes: Supermassive Black Hole Binaries; Extreme Mass Ratio Captures; Galactic Binaries; and realizations of the Galactic Background. The present simulations of LISA's response have been produced with the publicly available LISA Simulator (http://www.physics.montana.edu/lisa/). New source data and more advanced response simulations will be added as they become available. Contributions to the MLDA are most welcome.
The MLDA provides for the development of techniques to tackle each source class individually, or the signals can be combined to produce a more realistic test of a data analysis procedure. A fixed signal stream can be combined with multiple noise realizations, which is useful for Monte-Carlo studies of algorithm performance.
One of the goals of the MLDA is to provide a common ground for comparing different approaches to LISA data analysis. Each simulated data stream comes with reference files containing a description of the sources that have been modeled. Thus, the MLDA can serve as a training ground for the LISA data analysis community.
At some time in the future, prior to the launch of the LISA observatory, the MLDA hopes to host a Mock LISA Data Challenge to see which algorithms perform best in a blind test using realistic multi-source Mock Data sets.
The MLDA is designed to be a community resource, with input from the entire LISA community. We invite interested researchers to become involved in LISA data analysis, to utilize the archive, and to participate in its further development. Please let us know what you think, and how you would like to be involved.
MLDA Steering Committee:
Neil Cornish, Convener, Montana State University,
John Baker, GSFC,
Matt Benacquista, Montana State University-Billings,
Joan Centrella, GSFC,
Scott Hughes, MIT,
Shane Larson, Caltech.