2015/10/31 by Miroslav Shaltev, M. Shaltev · 1 citation
Earth and Planetary Sciences · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Data set #Detector #Geophysics and Gravity Measurements #Greedy algorithm #Ideal (ethics) #Mathematical optimization #Mathematics #Noise (video) #Numerical methods in inverse problems #Optimization problem #Pulsars and Gravitational Waves Research #Search algorithm #Selection (genetic algorithm) #Sensitivity (control systems) #Set (abstract data type) #astro-ph.IM #gr-qc
paper · pdf · doi:10.1103/physrevd.93.044058
published as Phys. Rev. D 93, 044058 (2016) · 14 pages, 6 figures
openalex publication_date 2016/02/23 · arxiv created 2016/04/03 · arxiv updated 2016/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The search for continuous gravitational waves in a wide parameter space at a 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 and Shaltev [Phys. Rev. D 85, 084010 (2012)] have developed a semianalytic method for finding optimal StackSlide parameters at a fixed computing cost under ideal data conditions, i.e., gapless data and a constant noise floor. In this work, we consider more realistic conditions by allowing for gaps in the data and changes in the 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 an 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 data set. 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.