About this Research Topic
The groundbreaking detection of gravitational wave event GW150914 has ushered in a transformative era for multi-messenger astronomy. As the sensitivity of ground-based observatories like LIGO-Virgo-KAGRA is enhanced, fainter gravitational wave signals become detectable. Concurrently, the imminent launch of space-based detectors, including LISA, Taiji, and Tianqin, will facilitate the exploration of a more diverse range of astrophysical phenomena. With the burgeoning volume and complexity of gravitational wave data, deep learning methods are poised to address the challenges of signal detection and parameter estimation where traditional techniques, such as matched-filtering, grapple with computational efficiency and scalability. Harnessing the capabilities of deep learning will allow scientists to surmount the limitations of classical approaches and unlock a treasure trove of astrophysical discoveries within the expanding realm of gravitational wave data.
The rapid advancements in Artificial Intelligence (AI) hold great potential for revolutionizing gravitational wave data analysis by adopting data-driven approaches. Harnessing AI to process massive gravitational wave data and extract valuable information is an exciting prospect. To date, deep learning has made significant strides in various aspects of gravitational wave data analysis, such as signal detection, parameter estimation, glitch classification, noise reduction, and signal extraction.
Though still in its infancy, AI techniques have demonstrated considerable promise for detecting and analyzing gravitational wave (GW) signals. This Research Topic aims to compile current AI applications in GW astronomy and showcase the latest advancements in the field. Additionally, it offers a platform for scientists to present their work in areas such as:
(i) Data quality control and gap imputation;
(ii) Detector noise reduction;
(iii) GW signals detection, extraction;
(iv) Multi-source classification and separation;
(v) Physical parameter estimation;
(vi) Other topics in gravitational wave sciences.
The rapid advancements in Artificial Intelligence (AI) hold great potential for revolutionizing gravitational wave data analysis by adopting data-driven approaches. Harnessing AI to process massive gravitational wave data and extract valuable information is an exciting prospect. To date, deep learning has made significant strides in various aspects of gravitational wave data analysis, such as signal detection, parameter estimation, glitch classification, noise reduction, and signal extraction.
Though still in its infancy, AI techniques have demonstrated considerable promise for detecting and analyzing gravitational wave (GW) signals. This Research Topic aims to compile current AI applications in GW astronomy and showcase the latest advancements in the field. Additionally, it offers a platform for scientists to present their work in areas such as:
(i) Data quality control and gap imputation;
(ii) Detector noise reduction;
(iii) GW signals detection, extraction;
(iv) Multi-source classification and separation;
(v) Physical parameter estimation;
(vi) Other topics in gravitational wave sciences.
Keywords: Gravitational Wave, Artificial Intelligence, Deep Learning, Data Analysis, Multi-messenger astronomy
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