Machine learning in astronomy

被引：6

作者：

Kembhavi, Ajit ^{[1
]}

Pattnaik, Rohan ^{[2
]}

机构：

[1] Interuniv Ctr Astron & Astrophys IUCAA, Pune 411007, Maharashtra, India

[2] Rochester Inst Technol, Sch Phys & Astron, Rochester, NY 14623 USA

来源：

JOURNAL OF ASTROPHYSICS AND ASTRONOMY | 2022年 / 43卷 / 02期

关键词：

Low-mass X-ray binaries; star-galaxy classification; machine learning; classification; CONVOLUTIONAL NEURAL-NETWORKS; X-RAY-BURSTS; CLASSIFICATION;

D O I：

10.1007/s12036-022-09871-2

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

Artificial intelligence techniques like machine learning and deep learning are being increasingly used in astronomy to address the vast quantities of data, which are now widely available. We briefly introduce some of these techniques and then describe their use through the examples of star-galaxy classification and the classification of low-mass X-ray binaries into binaries, which host a neutron star and those which host a black hole. This paper is based on a talk given by one of the authors and reviews previously published work and some new results.

引用

页数：10

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