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Pulsar Search Using Supervised Machi...

Ford, John M.

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Pulsar Search Using Supervised Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Pulsar Search Using Supervised Machine Learning./
作者:	Ford, John M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	228 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10265451
ISBN:	9781369685558

Pulsar Search Using Supervised Machine Learning.
Ford, John M.

Pulsar Search Using Supervised Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 228 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--Nova Southeastern University, 2017.

Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require a large ensemble of pulsars to provide enough statistical information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100\% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1\% and a recall of near 100\% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1\% was not reached, recall of over 99\% was achieved with a false positive rate of less than 2\%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

ISBN: 9781369685558Subjects--Topical Terms:

523869
Computer science.

Pulsar Search Using Supervised Machine Learning.
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