Deep Neural Networks to Enable Real-time Multimessenger Astrophysics (1701.00008v3)

Abstract: Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams. We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks. We showcase the application of this method for the detection and parameter estimation of gravitational waves from binary black hole mergers. Our results indicate that Deep Filtering significantly outperforms conventional machine learning techniques, achieves similar performance compared to matched-filtering while being several orders of magnitude faster thus allowing real-time processing of raw big data with minimal resources. More importantly, Deep Filtering extends the range of gravitational wave signals that can be detected with ground-based gravitational wave detectors. This framework leverages recent advances in artificial intelligence algorithms and emerging hardware architectures, such as deep-learning-optimized GPUs, to facilitate real-time searches of gravitational wave sources and their electromagnetic and astro-particle counterparts.

• The paper introduces a novel Deep Filtering method that replaces computationally intensive matched filtering with paired CNNs for both detection and regression tasks.
• The method achieves 100% sensitivity for signals with SNR above 10 and estimates binary black hole component masses with about 10% error using GPU acceleration.
• The study’s approach enables integration of gravitational wave data with other observatories, significantly advancing the capabilities of real-time multimessenger astrophysics.

Deep Filtering Applied to Real-Time Multimessenger Astrophysics

The paper addresses the formidable challenge of analyzing gravitational wave (GW) data in real time, a task that requires both high sensitivity to weak signals and efficient processing capabilities. Deep Filtering, the approach proposed by the authors, leverages the capabilities of deep neural networks (DNNs) to significantly enhance the detection and parameter estimation of GW signals from binary black hole (BBH) mergers. The notable achievement is the integration of two deep convolutional neural networks (CNNs) designed to operate in a succession that enables both classification and regression tasks.

Key Methodological Developments

Deep Filtering differs fundamentally from existing methods by entirely relying on deep learning techniques, thus bypassing the computationally intensive process of matched-filtering that underlies traditional approaches. The paper demonstrates how two CNNs can be trained: one for detecting the presence of a signal and another for estimating the parameters such as the component masses of BBHs. The networks utilize a novel training strategy with a progressively increasing noise scheme, demonstrating a capacity for transfer learning between the classifier and predictor networks. This approach reveals a keen sensitivity to GW signals with a SNR (Signal-to-Noise Ratio) as low as 7.5, which is notably below the noise level.

The deep learning models trained in this paper offer an alternative to conventional machine learning methods like Random Forest, Support Vector Machines, and others, showcasing superior performance not only in detection accuracy but also in reducing computational demands, achieving real-time processing speeds using modern GPUs.

Strong Numerical Results

The numerical results presented underscore the remarkable efficacy of the Deep Filtering method. The method competes with, and in cases outperforms, matched-filtering in terms of both speed and detection accuracy, with orders of magnitude faster processing times achieved through GPU acceleration. The sensitivity of the classifier reaches 100% for signals with SNR above 10, and the predictor achieves a mean relative error of approximately 10% for the estimation of component masses at similar SNR levels. These findings point towards a profound computational efficiency in Deep Filtering, rendering it a practical tool for real-time applications.

Implications for Multimessenger Astrophysics

The implications of this research are significant for the multimessenger astrophysics domain. Not only does this method expand the range of detectable GW signals, but it also offers a pathway to integrate GW data with electromagnetic and astro-particle observatories, thereby enhancing the capability for real-time multimessenger detections. This capability could drastically improve the coordination and response time of ground-based and space-borne telescopes in concert with GW observatories.

Future Directions and Theoretical Implications

The transition from traditional signal processing to deep learning frameworks as illustrated in this paper suggests profound changes could emerge in the way GW data is handled. Future research may explore a broader array of signal types, incorporate more complex parameters like spin and eccentricity, and move beyond binary BBH systems to other astrophysical phenomena. On a theoretical level, these advancements could offer new insights into the physics underpinning GW sources, potentially pushing the boundaries of current astrophysical models.

In conclusion, this paper exemplifies how the novel application of DNNs can revolutionize the analysis of time-series data in GW astrophysics, laying the groundwork for an era where real-time multimessenger astronomy could become routine. With further development and integration, these models hold the promise of unifying different observational modalities into a coherent, dynamic view of the universe.