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Journal Article

Deep-Learning Continuous Gravitational Waves


Messenger,  Chris
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;


Prix,  Reinhard
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Dreissigacker, C., Sharma, R., Messenger, C., & Prix, R. (2019). Deep-Learning Continuous Gravitational Waves. Physical Review D, 100 (4): 044009. doi:10.1103/PhysRevD.100.044009.

Cite as: https://hdl.handle.net/21.11116/0000-0003-8BB9-0
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 $T=10^6\,\mathrm{s}$.
Detection probability $p_\mathrm{det}$ is measured on a signal population for
which matched filtering achieves $p_\mathrm{det}=90\%$ in Gaussian noise. In
the easiest test case ($T=10^5\,\mathrm{s}$ at $20\,\mathrm{Hz}$) the DNN
achieves $p_\mathrm{det}\sim88\%$, corresponding to a loss in sensitivity depth
of $\sim5\%$ versus coherent matched filtering. However, at higher-frequencies
and longer observation time the DNN detection power decreases, until
$p_\mathrm{det}\sim13\%$ and a loss of $\sim 66\%$ in sensitivity depth in the
hardest case ($T=10^6\,\mathrm{s}$ at $1000\,\mathrm{Hz}$). We study the DNN
generalization ability by testing on signals of different frequencies,
spindowns and signal strengths than they were trained on. We observe excellent
generalization: only five networks, each trained at a different frequency,
would be able to cover the whole frequency range of the search.