Seedless clustering in all-sky searches for gravitational-wave transients

The problem of searching for unmodeled gravitational-wave bursts can be thought of as a pattern recognition problem: how to find statistically significant clusters in spectrograms of strain power when the precise signal morphology is unknown. In a previous publication, we showed how "seedless clustering" can be used to dramatically improve the sensitivity of searches for long-lived gravitational-wave transients. In order to manage the computational costs, this initial analysis focused on externally triggered searches where the source location and emission time are both known to some degree of precision. In this paper, we show how the principle of seedless clustering can be extended to facilitate computationally-feasible, all-sky searches where the direction and emission time of the source are entirely unknown. We further demonstrate that it is possible to achieve a considerable reduction in computation time by using graphical processor units (GPUs), thereby facilitating more sensitive searches.Comment: 9 pages, 2 figure

Detecting compact binary coalescences with seedless clustering

Compact binary coalescences are a promising source of gravitational waves for second-generation interferometric gravitational-wave detectors. Although matched filtering is the optimal search method for well-modeled systems, alternative detection strategies can be used to guard against theoretical errors (e.g., involving new physics and/or assumptions about spin/eccentricity) while providing a measure of redundancy. In previous work, we showed how "seedless clustering" can be used to detect long-lived gravitational-wave transients in both targeted and all-sky searches. In this paper, we apply seedless clustering to the problem of low-mass ($M_\text{total}\leq10M_\odot$) compact binary coalescences for both spinning and eccentric systems. We show that seedless clustering provides a robust and computationally efficient method for detecting low-mass compact binaries