東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

The Search for Life: Exoplanet Detec...

Scannell, Natasha.

FindBook

Google Book

Amazon

博客來

The Search for Life: Exoplanet Detection with Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Search for Life: Exoplanet Detection with Deep Learning./
作者:	Scannell, Natasha.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	70 p.
附註:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
標題:	Artificial intelligence. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28496437
ISBN:	9798516080111

The Search for Life: Exoplanet Detection with Deep Learning.
Scannell, Natasha.

The Search for Life: Exoplanet Detection with Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 70 p.

Source: Masters Abstracts International, Volume: 83-01.

Thesis (M.Sc.)--The University of Wisconsin - Milwaukee, 2021.

This item must not be sold to any third party vendors.

The discovery of new exoplanets, planets outside of our solar system, is essential for increasing our understanding of the universe. Exoplanets capable of harboring life are particularly of interest. Over 600 GB of data was collected by the Kepler Space Telescope, and about 30 GB is being collected each day by the Transiting Exoplanet Survey Satellite since its launch in 2018. Traditional methods of experts examining this data manually are no longer tractable; automation is necessary to accomplish the task of vetting all of this data to identify planet candidates from astrophysical false positives. Previous state-of-the-art models, Astronet and Exonet, use deep convolutional neural networks (CNNs) with over 8.8 million parameters. In this paper, I experiment with the application of recurrent networks, attentional models, and scaling down Astronet. I have developed a CNN model with 8x fewer trainable parameters than Astronet with the same accuracy and improved precision. I also provide a CNN-LSTM model with 59x fewer parameters just 1\\% behind Astronet in accuracy that, with further tuning, may also be a competitive model for particularly resource-constrained uses.All code for this research is available on GitHub.

ISBN: 9798516080111Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learning

The Search for Life: Exoplanet Detection with Deep Learning.
LDR:02317nmm a2200361 4500 001 2282533
005 20211012150151.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798516080111
035 $a (MiAaPQ)AAI28496437
035 $a AAI28496437
040 $a MiAaPQ $c MiAaPQ
100 1 $a Scannell, Natasha. $3 3561334
245 1 4 $a The Search for Life: Exoplanet Detection with Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 70 p.
500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: McRoy, Susan.
502 $a Thesis (M.Sc.)--The University of Wisconsin - Milwaukee, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The discovery of new exoplanets, planets outside of our solar system, is essential for increasing our understanding of the universe. Exoplanets capable of harboring life are particularly of interest. Over 600 GB of data was collected by the Kepler Space Telescope, and about 30 GB is being collected each day by the Transiting Exoplanet Survey Satellite since its launch in 2018. Traditional methods of experts examining this data manually are no longer tractable; automation is necessary to accomplish the task of vetting all of this data to identify planet candidates from astrophysical false positives. Previous state-of-the-art models, Astronet and Exonet, use deep convolutional neural networks (CNNs) with over 8.8 million parameters. In this paper, I experiment with the application of recurrent networks, attentional models, and scaling down Astronet. I have developed a CNN model with 8x fewer trainable parameters than Astronet with the same accuracy and improved precision. I also provide a CNN-LSTM model with 59x fewer parameters just 1\\% behind Astronet in accuracy that, with further tuning, may also be a competitive model for particularly resource-constrained uses.All code for this research is available on GitHub.
590 $a School code: 0263.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Astronomy. $3 517668
653 $a Deep learning
653 $a Exoplanets
653 $a Kepler
653 $a Machine learning
690 $a 0800
690 $a 0984
690 $a 0606
710 2 $a The University of Wisconsin - Milwaukee. $b Computer Science. $3 3283834
773 0 $t Masters Abstracts International $g 83-01.
790 $a 0263
791 $a M.Sc.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28496437