“第四十六章 天文学家的助手”版本间的差异
来自cslt Wiki
（以“==教学资料== * 教学参考 * [http://aigraph.cslt.org/courses/46/course-46.pptx 课件] * 小清爱提问：机器学习如何检查天文...”为内容创建页面） |
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==开发者资源== | ==开发者资源== | ||
| − | + | * AstroCV: an oper source toolbox for astronomy [https://github.com/astroCV/astroCV] | |
* Source code for checking astronomy data (Mesarcik et al.)[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] | * Source code for DFCN-based RFI detection (Kerrigan et al.) [https://github.com/UPennEoR/ml_rfi] | ||
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* 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] | * 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] | ||
| + | * González R E, Munoz R P, Hernández C A. Galaxy detection and identification using deep learning and data augmentation[J]. Astronomy and computing, 2018, 25: 103-109. [https://www.sciencedirect.com/science/article/am/pii/S2213133718300325] | ||
2022年9月14日 (三) 06:33的版本
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
- AstroCV: an oper source toolbox for astronomy [15]
- Source code for checking astronomy data (Mesarcik et al.)[16]
- Source code for DFCN-based RFI detection (Kerrigan et al.) [17]
高级读者
- Baron D. Machine learning in astronomy: A practical overview[J]. arXiv preprint arXiv:1904.07248, 2019. [18]
- Henry W. Leung1 and Jo Bovy, Deep learning of multi-element abundances from high-resolution spectroscopic data, MNRAS, 2018. [19]
- Mesarcik et al, Deep learning assisted data inspection for radio astronomy, MNRAS, 2020. [20]
- 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. [21]
- González R E, Munoz R P, Hernández C A. Galaxy detection and identification using deep learning and data augmentation[J]. Astronomy and computing, 2018, 25: 103-109. [22]
