Title: 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].