“第四十六章 给天文望远镜体检”版本间的差异
来自cslt Wiki
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==开发者资源== | ==开发者资源== | ||
| − | * Source code for checking astronomy data [https://github.com/mesarcik/DL4DI] | + | * Source code for checking astronomy data (Mesarcik et al.)[https://github.com/mesarcik/DL4DI] |
| + | * Source code for DFCN-based RFI detection (Kerrigan et al.) [https://github.com/UPennEoR/ml_rfi] | ||
==高级读者== | ==高级读者== | ||
| 第40行： | 第41行： | ||
* Henry W. Leung1 and Jo Bovy, Deep learning of multi-element abundances from high-resolution spectroscopic data, MNRAS, 2018. [https://arxiv.org/pdf/1808.04428] | * Henry W. Leung1 and Jo Bovy, Deep learning of multi-element abundances from high-resolution spectroscopic data, MNRAS, 2018. [https://arxiv.org/pdf/1808.04428] | ||
* Mesarcik et al, Deep learning assisted data inspection for radio astronomy, MNRAS, 2020. [https://academic.oup.com/mnras/article/496/2/1517/5848205] | * Mesarcik et al, Deep learning assisted data inspection for radio astronomy, MNRAS, 2020. [https://academic.oup.com/mnras/article/496/2/1517/5848205] | ||
| + | * Kerrigan J, Plante P L, Kohn S, et al. Optimizing sparse RFI prediction using deep learning[J]. Monthly Notices of the Royal Astronomical Society, 2019, 488(2): 2605-2615. [https://academic.oup.com/mnras/article/488/2/2605/5529408] | ||
2022年9月14日 (三) 05:25的最后版本
教学资料
扩展阅读
- AI100问：机器学习如何帮助天文学家检测望远镜问题？[2]
- 最强大射电望远镜亮相由66座天线构成 [3]
- 新华社：中国天眼”——500米口径球面射电望远镜（FAST） [4]
- 大国重器“中国天眼” [5]
- 这只“中国天眼”：看透百亿光年 洞悉星辰大海[6]
- 维基百科：中国天眼 [7]
- At 13 Billion Light-Years Away, Galaxy Is Farthest To Be Measured From Earth [8]
视频展示
- CCTV-9 纪录片《天眼》 [9]
- 哈伯望远镜传回的照片 [10]
- 纪录片《哈勃望远镜》 [11]
- Classifying Galaxies with AI [12]
- Big data in astronomy [13]
- AI and space industry [14]
演示链接
开发者资源
- Source code for checking astronomy data (Mesarcik et al.)[15]
- Source code for DFCN-based RFI detection (Kerrigan et al.) [16]
高级读者
- Baron D. Machine learning in astronomy: A practical overview[J]. arXiv preprint arXiv:1904.07248, 2019. [17]
- Henry W. Leung1 and Jo Bovy, Deep learning of multi-element abundances from high-resolution spectroscopic data, MNRAS, 2018. [18]
- Mesarcik et al, Deep learning assisted data inspection for radio astronomy, MNRAS, 2020. [19]
- Kerrigan J, Plante P L, Kohn S, et al. Optimizing sparse RFI prediction using deep learning[J]. Monthly Notices of the Royal Astronomical Society, 2019, 488(2): 2605-2615. [20]
