“第四十六章 给天文望远镜体检”版本间的差异

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(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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* 最强大射电望远镜亮相由66座天线构成 [https://epaper.qlwb.com.cn/qlwb/content/20130314/ArticelA30002FM.htm]
 
* 最强大射电望远镜亮相由66座天线构成 [https://epaper.qlwb.com.cn/qlwb/content/20130314/ArticelA30002FM.htm]
* 新华社:中国天眼”——500米口径球面射电望远镜(FAST) https://www.cas.cn/zt/kjzt/fastcg/tp/202201/t20220106_4820923.shtml
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* 新华社:中国天眼”——500米口径球面射电望远镜(FAST) [https://www.cas.cn/zt/kjzt/fastcg/tp/202201/t20220106_4820923.shtml]
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* 大国重器“中国天眼” [https://finance.sina.com.cn/tech/2021-08-18/doc-ikqciyzm2108272.shtml]
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* 这只“中国天眼”:看透百亿光年 洞悉星辰大海[https://www.toutiao.com/article/7104433080105959977/]
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* 维基百科:中国天眼 [http://aigraph.cslt.org/courses/46/500米口径球面射电望远镜.pdf]
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* At 13 Billion Light-Years Away, Galaxy Is Farthest To Be Measured From Earth [https://www.npr.org/sections/thetwo-way/2015/05/08/405280915/at-13-billion-light-years-away-galaxy-is-farthest-to-be-measured-from-earth]
  
  
 
==视频展示==
 
==视频展示==
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* CCTV-9 纪录片《天眼》 [https://www.bilibili.com/video/BV1oy4y1r71Z?spm_id_from=333.337.search-card.all.click]
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* 哈伯望远镜传回的照片 [https://www.bilibili.com/video/BV18b411u7re?spm_id_from=333.337.search-card.all.click]
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* 纪录片《哈勃望远镜》 [https://www.bilibili.com/video/BV16541197gP?spm_id_from=333.337.search-card.all.click]
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* Classifying Galaxies with AI [http://aigraph.cslt.org/courses/46/Classifying_Galaxies.mp4]
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* Big data in astronomy [http://aigraph.cslt.org/courses/46/BigDatainAstronomy.mp4]
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* AI and space industry [http://aigraph.cslt.org/courses/46/AI-space.mp4]
  
  
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==开发者资源==
 
==开发者资源==
  
 
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* Source code for checking astronomy data (Mesarcik et al.)[https://github.com/mesarcik/DL4DI]
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* Source code for DFCN-based RFI detection (Kerrigan et al.) [https://github.com/UPennEoR/ml_rfi]
  
 
==高级读者==
 
==高级读者==
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* 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]
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* 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]