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Free keywords:
General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Physics, Data Analysis, Statistics and Probability, physics.data-an
Abstract:
The sensitivity of wide-parameter-space searches for continuous gravitational
waves is limited by computational cost. Recently it was shown that Deep Neural
Networks (DNNs) can perform all-sky searches directly on (single-detector)
strain data, potentially providing a low-computing-cost search method that
could lead to a better overall sensitivity. Here we expand on this study in two
respects: (i) using (simulated) strain data from two detectors simultaneously,
and (ii) training for directed (i.e.\ single sky-position) searches in addition
to all-sky searches. For a data timespan of $T = 10^5\, s$, the all-sky
two-detector DNN is about $7\%$ less sensitive (in amplitude $h_0$) at low
frequency ($f=20\,Hz$), and about $51\,\%$ less sensitive at high frequency
($f=1000\,Hz$) compared to fully-coherent matched-filtering (using WEAVE). In
the directed case the sensitivity gap compared to matched-filtering ranges from
about $7-14\%$ at $f=20\,Hz$ to about $37-49\%$ at $f=1500\,Hz$. Furthermore we
assess the DNN's ability to generalize in signal frequency, spindown and
sky-position, and we test its robustness to realistic data conditions, namely
gaps in the data and using real LIGO detector noise. We find that the DNN
performance is not adversely affected by gaps in the test data or by using a
relatively undisturbed band of LIGO detector data instead of Gaussian noise.
However, when using a more disturbed LIGO band for the tests, the DNN's
detection performance is substantially degraded due to the increase in false
alarms, as expected.
