English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Among astrophysical sources in the Advanced LIGO and Advanced Virgodetectors' frequency band are rotating non-axisymmetric neutron stars emittinglong-lasting, almost-monochromatic gravitational waves. Searches for thesecontinuous gravitational-wave signals are usually performed in long stretchesof data in a matched-filter framework e.g., the F-statistic method. In anall-sky search for a priori unknown sources, large number of templates ismatched against the data using a pre-defined grid of variables (thegravitational-wave frequency and its derivatives, sky coordinates),subsequently producing a collection of candidate signals, corresponding to thegrid points at which the signal reaches a pre-defined signal-to-noisethreshold. An astrophysical signature of the signal is encoded in themulti-dimensional vector distribution of the candidate signals. In the firstwork of this kind, we apply a deep learning approach to classify thedistributions. We consider three basic classes: Gaussian noise, astrophysicalgravitational-wave signal, and a constant-frequency detector artifact("stationary line"), the two latter injected into the Gaussian noise. 1D and 2Dversions of a convolutional neural network classifier are implemented, trainedand tested on a broad range of signal frequencies. We demonstrate that theseimplementations correctly classify the instances of data at varioussignal-to-noise ratios and signal frequencies, while also showing conceptgeneralization i.e., satisfactory performance at previously unseen frequencies.In addition we discuss the deficiencies, computational requirements andpossible applications of these implementations.

Convolutional neural network classifier for the output of the time-domain F-statistic all-sky search for continuous gravitational waves