Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data (1711.03121v1)

Abstract: The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.

• The paper demonstrates a novel deep learning approach, Deep Filtering, for real-time detection and parameter estimation of gravitational waves.
• It leverages compact CNN architectures to process non-Gaussian noise, achieving performance comparable to traditional matched-filtering methods with greater efficiency.
• The approach generalizes across diverse noise environments and shows promising potential for integration into multimessenger astronomy pipelines.

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation

The paper presents a novel application of deep learning techniques, specifically convolutional neural networks (CNNs), to the detection and parameter estimation of gravitational waves (GWs) using data from the Advanced LIGO detectors. This research marks a significant advance in the application of machine learning to astrophysical data analysis, particularly in the domain of real-time gravitational wave detection.

The authors introduce "Deep Filtering," a framework leveraging CNNs to process time-series data directly. The work extends previous methods demonstrated with simulated LIGO data by applying them to real data collected by LIGO, showcasing the capability to detect and estimate parameters of GWs from binary black hole (BBH) mergers in non-stationary and non-Gaussian noise environments. Notably, Deep Filtering achieves sensitivities and parameter estimation errors comparable to traditional matched-filtering techniques, while boasting significant computational efficiency gains and robustness to data artifacts and glitches.

Key Results and Implications

Deep Filtering employs a two-step approach: a classifier CNN identifies the presence of a GW signal, and a predictor CNN estimates the parameters of the detected signal. This architecture notably compresses the information from substantial template banks and noise datasets into two compact networks, each 23 MB in size. The framework handles continuous data streams effectively, capable of real-time analysis with minimal hardware resources—just a single CPU or GPU. The CNNs demonstrated the ability to interpolate between known templates, identifying signals not explicitly part of the training set with impressive accuracy.

Critically, Deep Filtering was tested on real LIGO noise data that included transient glitches. The networks not only maintained robust detection capabilities in these challenging conditions but also showed potential for glitch classification, pointing towards a unified approach for signal detection and noise characterization. Additionally, the system successfully generalized across different noise distributions, indicating readiness for application in future LIGO data without retraining.

Practical and Theoretical Advances

From a practical perspective, the shift from traditional computational methods to deep learning significantly reduces the resource and time burden associated with analyzing GW data. This innovation opens the door to more extensive parameter space coverage in GW searches, even encompassing complex systems with multiple parameters such as spins or eccentric orbits.

Theoretically, the intrinsic scalability of deep learning presents opportunities to address the "curse of dimensionality" faced in GW analyses, enabling coverage of the full parameter space and detection capabilities beyond existing methods. Additionally, the ability of Deep Filtering to function effectively on non-stationary, non-Gaussian noise and in the presence of glitches offers insights into the non-linear feature extraction properties of CNNs, paving the way for novel approaches in signal processing.

Future Directions

The paper encourages further exploration into expanding Deep Filtering to handle more complex GW source models, including spin and precessing-binary systems. Integrating these methods into existing multimessenger astronomy pipelines could enhance prompt identification and characterization of GW events, vital for electromagnetic follow-up observations.

Moreover, this research illuminates potential cross-disciplinary applications of deep learning for noise reduction and signal extraction in various scientific fields beyond astrophysics. The scalability and adaptability of deep learning models position them as useful tools in diverse domains where weak signal detection amidst noisy backgrounds is critical.

In conclusion, the application of CNNs through Deep Filtering offers a promising and efficient route for the advancement of real-time GW detection and parameter estimation, with profound implications for both the computational and astrophysical sciences. The robustness and adaptability demonstrated in this work underscore the potential of machine learning to extend beyond traditional paradigms, facilitating novel scientific discoveries in the dynamic field of gravitational wave astronomy.