Adaptive clustering procedure for continuous gravitational wave searches

Abstract

In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].

11 pages, 9 figures, 2 tables; v1: initial submission; v2: journal review, copyedited version; v3: fixed typo in Fig1

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• Search methods for continuous gravitational-wave signals from unknown sources in the advanced-detector era
• Deep learning for clustering of continuous gravitational wave candidates
• Einstein@Home all-sky search for continuous gravitational waves in LIGO O2 public data
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• Search for Continuous Gravitational Waves from the Central Compact Objects in Supernova Remnants Cassiopeia A, Vela Jr. and G347.3-0.5
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• Application of a hierarchical MCMC follow-up to Advanced LIGO continuous gravitational-wave candidates
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• Gravitational-wave searches in the era of Advanced LIGO and Virgo
• Results from an Einstein@Home search for continuous gravitational waves from Cassiopeia A and Vela Jr. using LIGO O2 data
• One-stop strategy to search for long-duration gravitational-wave signals
• Efficient Search for Detection Candidates Using a Peak Finder Strategy for All-Sky-All-Frequency Gravitational Wave Radiometer