Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise

Understand

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).