The deep learning method has developed very fast as a tool for data analysis in recent years. Moreover, as a technique, it is quite promising as a way to analyze gravitational-wave detection data. Multiple works in the literature have already used deep learning to process simulated gravitational-wave data. In this paper, we apply deep learning to LIGO data. In order to improve the weak signal recognition, we design a new structure of the convolutional neural network (CNN). The key feature of our new CNN structure is the sensing layer. This layer mimics matched filtering but is different from the usual matched-filtering technique. Usually, the matched-filtering technique uses a full template bank to match the data. However, our sensing layer only uses tens of waveforms. Our new convolutional neural network admits comparable accuracy and efficiency of signal recognition compared to other deep learning works published in the literature. Based on our new CNN, we can clearly recognize the 11 confirmed gravitational-wave events included in O1 and O2. In addition, we find about 2000 gravitational-wave triggers in O1 data.

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\ge 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.

With the rapid development of deep learning technology, more and more researchers apply it to gravitational wave (GW) data analysis. Previous studies focused on a single deep learning model. In this paper we design an ensemble algorithm combining a set of convolutional neural networks for GW signal recognition. The whole ensemble model consists of two subensemble models. Each subensemble model is also an ensemble model of deep learning. The two subensemble models treat data of Hanford and Livinston detectors, respectively. Proper voting scheme is adopted to combine the two subensemble models to form the whole ensemble model. We apply this ensemble model to all reported GW events in the first observation and second observation runs (O1/O2) by LIGO-VIRGO Scientific Collaboration. We find that the ensemble algorithm can clearly identify all binary black hole merger events except GW170818. We also apply the ensemble model to one month (August 2017) data of O2. No false trigger happens, although only O1 data are used for training. Our test results indicate that the ensemble learning algorithms can be used in real-time GW data analysis.

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in GW data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network in future observational runs.