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

Abstract

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

14 pages, 6 figures

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Cited by in corpus (8)

• Searches for Continuous-Wave Gravitational Radiation
• Recent searches for continuous gravitational waves
• Search methods for continuous gravitational-wave signals from unknown sources in the advanced-detector era
• Results of the deepest all-sky survey for continuous gravitational waves on LIGO S6 data running on the Einstein@Home volunteer distributed computing project
• An Einstein@home search for continuous gravitational waves from Cassiopeia A
• Optimal Template Banks
• Application of a hierarchical MCMC follow-up to Advanced LIGO continuous gravitational-wave candidates
• Toward a computationally-efficient follow-up pipeline for blind continuous gravitational-wave searches