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In the last few years there has been an enormous effort in parameterestimation studies for different sources with the space based gravitationalwave detector, LISA. While these studies have investigated sources of differingcomplexity, the one thing they all have in common is they assume continuousdata streams. In reality, the LISA data stream will contain gaps from suchpossible events such as repointing of the satellite antennae, to dischargingstatic charge build up on the satellites, to disruptions due to micro-meteorstrikes. In this work we conduct a large scale Monte Carlo parameter estimationsimulation for galactic binaries assuming data streams containing gaps. As theexpected duration and frequency of the gaps are currently unknown, we havedecided to focus on gaps of approximately one hour, occurring either once perday or once per week. We also study the case where, as well as the expectedperiodic gaps, we have a data drop-out of one continuous week. Our results showthat for for galactic binaries, a gap of once per week introduces a bias ofbetween 0.5% and 1% in the estimation of parameters, for the most importantparameters such as the sky position, amplitude and frequency. This number risesto between 3% and 7% for the case of one gap a day, and to between 4% and 9%when we have one gap a day and a spurious gap of a week. A future study willinvestigate the effect of data gaps on supermassive black hole binaries andextreme mass ratio inspirals.

The Effect of Data Gaps on LISA Galactic Binary Parameter Estimation