Deep Learning techniques to make Gravitational Wave Detections from Weak Time-Series Data. (arXiv:2007.05889v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Chauhan_Y/0/1/0/all/0/1">Yash Chauhan</a>
Gravitational waves are ripples in the space time fabric when high energy
events such as black hole mergers or neutron star collisions take place. The
first Gravitational Wave (GW) detection (GW150914) was made by the Laser
Interferometer Gravitational-wave Observatory (LIGO) on September 14, 2015.
Furthermore, the proof of the existence of GWs had countless implications from
Stellar Evolution to General Relativity. Gravitational waves detection requires
multiple filters and the filtered data has to be studied intensively to come to
conclusions on whether the data is a just a glitch or an actual gravitational
wave detection. However, with the use of Deep Learning the process is
simplified heavily, as it reduces the level of filtering greatly, and the
output is definitive and binary. This technique, Deep Learning, uses a
one-dimensional convolutional neural network (CNN). The model is trained by a
composite of real LIGO noise, and injections of GW waveform templates. The CNN
effectively uses classification to differentiate weak GW time series from
non-gaussian noise glitches in the LIGO datastream. The model’s sensitivity is
higher than all prior studies in this field, when making real-time detections
of GWs at an extremely low SNR, while still being less computationally
expensive. This sensitivity is achieved through the use of deep signal
manifolds, from both the Hanford and Livingston detectors, which enable the
model to be responsive to false positives.
Gravitational waves are ripples in the space time fabric when high energy
events such as black hole mergers or neutron star collisions take place. The
first Gravitational Wave (GW) detection (GW150914) was made by the Laser
Interferometer Gravitational-wave Observatory (LIGO) on September 14, 2015.
Furthermore, the proof of the existence of GWs had countless implications from
Stellar Evolution to General Relativity. Gravitational waves detection requires
multiple filters and the filtered data has to be studied intensively to come to
conclusions on whether the data is a just a glitch or an actual gravitational
wave detection. However, with the use of Deep Learning the process is
simplified heavily, as it reduces the level of filtering greatly, and the
output is definitive and binary. This technique, Deep Learning, uses a
one-dimensional convolutional neural network (CNN). The model is trained by a
composite of real LIGO noise, and injections of GW waveform templates. The CNN
effectively uses classification to differentiate weak GW time series from
non-gaussian noise glitches in the LIGO datastream. The model’s sensitivity is
higher than all prior studies in this field, when making real-time detections
of GWs at an extremely low SNR, while still being less computationally
expensive. This sensitivity is achieved through the use of deep signal
manifolds, from both the Hanford and Livingston detectors, which enable the
model to be responsive to false positives.
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