# Item

ITEM ACTIONSEXPORT

Released

Journal Article

#### Deep-Learning Continuous Gravitational Waves

##### External Resource

No external resources are shared

##### Fulltext (restricted access)

There are currently no full texts shared for your IP range.

##### Fulltext (public)

1904.13291.pdf

(Preprint), 4MB

PhysRevD.100.044009.pdf

(Publisher version), 919KB

##### Supplementary Material (public)

There is no public supplementary material available

##### Citation

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

##### Abstract

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.

(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.