Deep Learning Techniques to make Gravitational Wave Detections from Weak Time-Series Data. (arXiv:2007.05889v4 [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) and Virgo Collaboration 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 more definitive, even though the model
produces a probabilistic result. Our technique, Deep Learning, utilizes a
different implementation of 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 from glitches in the
LIGO data stream. In addition, we are the first study to utilize fine-tuning as
a means to train the model with a second pass of data, while maintaining all
the learned features from the initial training iteration. This enables our
model to have a sensitivity of 100%, higher than all prior studies in this
field, when making real-time detections of GWs at an extremely low
Signal-to-noise ratios (SNR), while still being less computationally expensive.
This sensitivity, in part, is also achieved through the use of deep signal
manifolds from both the Hanford and Livingston detectors, which enable the
neural network 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) and Virgo Collaboration 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 more definitive, even though the model
produces a probabilistic result. Our technique, Deep Learning, utilizes a
different implementation of 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 from glitches in the
LIGO data stream. In addition, we are the first study to utilize fine-tuning as
a means to train the model with a second pass of data, while maintaining all
the learned features from the initial training iteration. This enables our
model to have a sensitivity of 100%, higher than all prior studies in this
field, when making real-time detections of GWs at an extremely low
Signal-to-noise ratios (SNR), while still being less computationally expensive.
This sensitivity, in part, is also achieved through the use of deep signal
manifolds from both the Hanford and Livingston detectors, which enable the
neural network to be responsive to false positives.
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