Computational challenges for multimodal astrophysics

Cuoco, E.; Patricelli, B.; Iess, A.; Morawski, F.

doi:10.1038/s43588-022-00288-z

Cited by 4 publications

(1 citation statement)

References 69 publications

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“…Recent advances in artificial intelligence (AI) offer a fantastic avenue for enabling or boosting study of GW into many previously inaccessible and computationally expensive issues (see Refs. [18][19][20] for review). The majority of state-of-the-art machine learning algorithms, however, have trouble in dealing with real-world noise that tainted by non-stationary and non-Gaussian noise artifacts [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent noise suppression for gravitational wave observational data

Wang

Ren²,

Zhou

et al. 2023

Preprint

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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 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 1 order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, we achieve state-of-the-art accuracy on reported binary black hole events of existing LIGO observing runs and substantial 1386 years inverse false alarm rate improvement on average. Our work highlights the potential of large neural networks and can be extended to the data processing analyses of upcoming observing runs.

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Section: Introductionmentioning

confidence: 99%

Intelligent noise suppression for gravitational wave observational data

Wang

Ren²,

Zhou

et al. 2023

Preprint

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WaveFormer: transformer-based denoising method for gravitational-wave data

Wang,

Zhou,

Cao

et al. 2024

Mach. Learn.: Sci. Technol.

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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 (BBH) events of LIGO we obtain a significant improvement in inverse false alarm rate. Our work highlights the potential of large neural networks in gravitational wave data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network (IGWN) in future observational runs.

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Identification of multicomponent LOFAR sources with multimodal deep learning

Alegre,

Best,

Sabater

et al. 2024

Monthly Notices of the Royal Astronomical Society

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Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently, there are no reliable automatic methods to determine which radio components are single radio sources or part of multi-component sources. We propose a deep learning classifier to identify those sources that are part of a multi-component system and require component association on data from the LOFAR Two-Metre Sky Survey (LoTSS). We combine different types of input data using multi-modal deep learning to extract spatial and local information about the radio source components: a convolutional neural network component that processes radio images is combined with a neural network component that uses parameters measured from the radio sources and their nearest neighbours. Our model retrieves 94 per cent of the sources with multiple components on a balanced test set with 2,683 sources and achieves almost 97 per cent accuracy in the real imbalanced data (323,103 sources). The approach holds potential for integration into pipelines for automatic radio component association and cross-identification. Our work demonstrates how deep learning can be used to integrate different types of data and create an effective solution for managing modern radio surveys.

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Computational challenges for multimodal astrophysics

Cited by 4 publications

References 69 publications

Intelligent noise suppression for gravitational wave observational data

Intelligent noise suppression for gravitational wave observational data

WaveFormer: transformer-based denoising method for gravitational-wave data

Identification of multicomponent LOFAR sources with multimodal deep learning

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