APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGIES TO ASTROPHYSICAL DATA

Аннотация

Artificial intelligence is an interdisciplinary research field with widespread applications. It aims at developing theoretical, methodological, technological, and applied systems that simulate, enhance, and assist human intelligence. Recently, notable accomplishments of artificial intelligence technology have been achieved in astronomical data processing, establishing this technology as central to numerous astronomical research areas such as radio astronomy, stellar and galactic (Milky Way) studies, exoplanets surveys, cosmology, and solar physics. This article systematically reviews representative applications of artificial intelligence technology to astronomical data processing, with comprehensive description of specific cases: pulsar candidate identification, fast radio burst detection, gravitational wave detection, spectral classification, and radio frequency interference mitigation. Furthermore, it discusses possible future applications to provide perspectives for astronomical research in the artificial intelligence era. 1.INTRODUCTION Astronomy is an observation-based science, for the development of which large volumes of data are essential. Recently, advances in manufacturing and information technology have considerably enhanced the data-collection capabilities of astronomical observation instruments, introducing the "big data" methodology into astronomy. With the development of an increasing number of large telescopes, large-scale digital sky surveys have become prominent in astronomy. In recent years, China has established several large astronomical ground-based observation facilities, such as the Five-hundred-meter Aperture Spherical Telescope (FAST) [1] , the Shanghai 65 m Radio Telescope (Tianma) [2] , and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) [3] . Other large telescopes, for example, the Qitai 110 m Radio Telescope (QTT) [4] , are also under construction. Moreover, China has launched the Dark Matter Particle Explorer (DAMPE or Wukong) [5] and Advanced Space-based Observatory- Solar (ASO-S) [6] satellites, with plans to launch and operate other space telescopes in succession. Future large astronomical instruments will generate daily considerable vol-umes of observation data. For example, the current data volume generated by a single sky survey represents tens of terabytes and survey dataset size is gradually increasing [7] . This considerable data volume increase requires new processing methods, such as the big data methodology, creating new, specific challenges. Astronomy has become a data-driven and data-intensive science [8] . Realtime processing, transmission, storage, and analysis of large data volumes are serious problems that must be addressed. AI is a branch of computer science that aims to study and develop theories, methods, technologies, and applied systems capable of simulating, supporting, and extending human intelligence. It establishes principles to implement computer systems that mimic human intelligence, enabling it to perform higher-level applications. As an emerging technology, AI has found widespread applications in various fields [9]. Following the "third industrial revolution" represented by the emergence of the internet and mobile communication technology, the 102 ResearchBib IF - 11.01, ISSN: 3030-3753, Volume 2 Issue 6 emergence of AI, jointly with big data, is seen as initiating the "fourth industrial revolution". The AI framework encompasses three standard paradigms for data structure extraction: (1) Supervised learning [10] : data consist of pairs of input items and labels. Supervised learning is considered a particular powerful form of nonlinear regression that extracts patterns from existing item/label pairs to predict labels for new items. (2) Unsupervised learning [11]: input data lack labels; therefore, the objective is to identify underlying statistical structures. Unsupervised learning is an extension of traditional statistical techniques such as clustering and principal component analysis (PCA). Modern AI systems also often rely on self-supervised learning, which achieves results similar to unsupervised learning by automatically labeling data, for example, by applying the same label to artificially generated variations of an object. (3) Reinforcement learning [12]: its purpose is to establish methods or strategies to achieve specific objectives, using feedback from previous actions. Astronomy includes several interconnected fields, such as astrophysics and astrochemistry. Its purpose is to identify and formulate the physical laws that govern the properties and evolution of the universe by observing astronomical phenomena, such as celestial body motion or cosmic redshift [13] . Astronomical research relies on extensive data and computational power. For specialized tasks, AI technology often outperforms traditional methods that rely on human intervention or rule-based programming; therefore, it often achieves comparable or bette