The new era of gravitational wave astronomy truly began on September 14, 2015, with the
detection of GW150914, the sensational first direct observation of gravitational waves from …

Coalescing massive black hole binaries (MBHBs) are one of primary sources for space-
based gravitational wave (GW) observations. The mergers of these binaries are expected to …

After the first detection of a gravitational wave in 2015, the number of successes achieved by
this innovative way of looking through the Universe has not stopped growing. However, the …

We present a convolutional neural network, designed in the autoencoder configuration that
can detect and denoise astrophysical gravitational waves from merging black hole binaries …

Statistically-informed deep learning for gravitational wave parameter estimation - IOPscience
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We evaluate several neural-network architectures, both convolutional and recurrent, for
gravitational-wave time-series feature extraction by performing point parameter estimation …

In recent years, machine learning models have been introduced into the field of gravitational
wave (GW) data processing. In this paper, we apply the convolutional neural network (CNN) …

The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data
analysis is investigated. The AST machine-learning model is a convolution-free classifier …

We test deep-learning (DL) techniques for the analysis of rotational core-collapse
supernovae (CCSN) gravitational-wave (GW) signals by performing classification and …

Hierarchical mergers in a dense environment are one of the primary formation channels of
the intermediate-mass black hole (IMBH) binary system. We expect that the resulting …