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arXiv 0811.0520astro-ph
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光学巡天中类星体天体物理建模和光度红移估计的机器学习技术

Machine learning techniques for astrophysical modelling and photometric redshift estimation of quasars in optical sky surveys

N. Daniel Kumar

摘要:

如今,机器学习技术被用于天体物理学研究的多个领域。本论文讨论了ML技术在天体物理学中的两类问题的应用,即随着时间的推移和自动化,同时分析数千个对象在大型光学巡天的个别天文现象的分析。具体研究的是(1)技术,以近似的精确轨道的卫星木星和土星的地球上的观测,以及(2)技术,以快速估计的距离,在斯隆数字巡天观测类星体。考虑的学习方法包括遗传算法,粒子群优化,人工神经网络和径向基函数网络。本文的第一部分证明了遗传算法和粒子群算法都可以有效地用于建模的函数是高度非线性的几个维度。随后在第二部分中证明,人工神经网络和RBFN可以被用来作为有效的预测光谱红移给定准确的测光,特别是在结合其他学习为基础的方法在文献中描述。仔细应用这些和其他ML技术来解决天文学和天体物理学中的问题,将有助于更好地理解恒星演化、双星系统、宇宙学和宇宙的大尺度结构。

英文摘要:

Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual astronomical phenomena over time and the automated, simultaneous analysis of thousands of objects in large optical sky surveys. Specifically investigated are (1) techniques to approximate the precise orbits of the satellites of Jupiter and Saturn given Earth-based observations as well as (2) techniques to quickly estimate the distances of quasars observed in the Sloan Digital Sky Survey. Learning methods considered include genetic algorithms, particle swarm optimisation, artificial neural networks, and radial basis function networks. The first part of this dissertation demonstrates that GAs and PSO can both be efficiently used to model functions that are highly non-linear in several dimensions. It is subsequently demonstrated in the second part that ANNs and RBFNs can be used as effective predictors of spectroscopic redshift given accurate photometry, especially in combination with other learning-based approaches described in the literature. Careful application of these and other ML techniques to problems in astronomy and astrophysics will contribute to a better understanding of stellar evolution, binary star systems, cosmology, and the large-scale structure of the universe.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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