分类: 天文学 >> 天文仪器与技术 提交时间: 2024-02-07 合作期刊: 《天文技术与仪器(英文)》
摘要:Artificial Intelligence (AI) 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.
分类: 天文学 >> 天文仪器与技术 提交时间: 2024-02-07 合作期刊: 《天文技术与仪器(英文)》
摘要:Artificial Intelligence (AI) 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.
分类: 物理学 >> 地球物理学、天文学和天体物理学 提交时间: 2024-02-01 合作期刊: 《Research in Astronomy and Astrophysics》
摘要: The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFIs) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square (LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64 m radio telescope's pulsar observations and simulated data. The results confirm the algorithm's effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile.
分类: 物理学 >> 地球物理学、天文学和天体物理学 提交时间: 2024-02-01 合作期刊: 《Research in Astronomy and Astrophysics》
摘要: Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system (Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of cross-matching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.
分类: 物理学 >> 地球物理学、天文学和天体物理学 提交时间: 2024-02-01 合作期刊: 《Research in Astronomy and Astrophysics》
摘要: To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data, we designed and implemented a pulsar baseband data processing algorithm (PSRDP) based on GPU parallel computing technology. PSRDP can perform operations such as baseband data unpacking, channel separation, coherent dedispersion, Stokes detection, phase and folding period prediction, and folding integration in GPU clusters. We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes, and the J0332+5434 pulsar baseband data generated by the self-developed backend of the NanShan Radio Telescope. We obtained the pulse profiles of each baseband data. Through experimental analysis, we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy (DSPSR), which verified the effectiveness of the PSRDP algorithm. Furthermore, using the same baseband data, we compared the processing speed of PSRDP with DSPSR, and the results showed that PSRDP was not slower than DSPSR in terms of speed. The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT (Qi Tai radio Telescope) ultra-wide bandwidth pulsar baseband data.