English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons40537

Shaltev,  Miroslav
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

1510.06427.pdf
(Preprint), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Shaltev, M. (2016). Optimizing StackSlide setup and data selection for continuous-gravitational-wave searches in realistic detector data. Physical Review D, 93: 044058. doi:10.1103/PhysRevD.93.044058.


Cite as: http://hdl.handle.net/21.11116/0000-0001-9046-D
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.