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The search for continuous gravitational waves in a wide parameter space atfixed computing cost is most efficiently done with semicoherent methods, e.g.StackSlide, due to the prohibitive computing cost of the fully coherent searchstrategies. Prix&Shaltev arXiv:1201.4321 have developed a semi-analytic methodfor finding \emph{optimal} StackSlide parameters at fixed computing cost underideal data conditions, i.e. gap-less data and constant noise floor. In thiswork we consider more realistic conditions by allowing for gaps in the data andchanges in noise level. We show how the sensitivity optimization can bedecoupled from the data selection problem. To find optimal semicoherent searchparameters we apply a numerical optimization using as example the semicoherentStackSlide search. We also describe three different data selection algorithms.Thus the outcome of the numerical optimization consists of the optimal searchparameters and the selected dataset. We first test the numerical optimizationprocedure under ideal conditions and show that we can reproduce the results ofthe analytical method. Then we gradually relax the conditions on the data andfind that a compact data selection algorithm yields higher sensitivity comparedto a greedy data selection procedure.

Optimizing StackSlide setup and data selection for continuous-gravitational-wave searches in realistic detector data