Deep-Learning continuous gravitational waves

Understand

We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars.

• The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals.
• We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline.
• As test benchmarks we consider two types of all-sky searches over the frequency range from $20\,\mathrm{Hz}$ to $1000\,\mathrm{Hz}$: an `easy' search using $T=10^5\,\mathrm{s}$ of data, and a `harder' search using $T=10^6\,\mathrm{s}$.