Mock LISA data challenge for the galactic white dwarf binaries

TL;DR

This paper discusses data analysis methods for detecting and estimating parameters of gravitational wave signals from white dwarf binaries in the mock LISA data challenge, achieving reliable detection of over ten thousand signals.

Contribution

It introduces a maximum likelihood detection pipeline with iterative refinement for analyzing complex LISA data containing millions of galactic binaries.

Findings

Reliable detection of over ten thousand white dwarf binary signals

Effective reduction of signal confusion above 5 mHz

Successful parameter estimation using iterative filtering

Abstract

We present data analysis methods used in detection and the estimation of parameters of gravitational wave signals from the white dwarf binaries in the mock LISA data challenge. Our main focus is on the analysis of challenge 3.1, where the gravitational wave signals from more than 50 mln. Galactic binaries were added to the simulated Gaussian instrumental noise. Majority of the signals at low frequencies are not resolved individually. The confusion between the signals is strongly reduced at frequencies above 5 mHz. Our basic data analysis procedure is the maximum likelihood detection method. We filter the data through the template bank at the first step of the search, then we refine parameters using the Nelder-Mead algorithm, we remove the strongest signal found and we repeat the procedure. We detect reliably and estimate parameters accurately of more than ten thousand signals from white dwarf binaries.