國立虎尾科技大學 |

Pulsar Search Using Supervised Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Pulsar Search Using Supervised Machine Learning./
作者:	Ford, John M.
面頁冊數:	1 online resource (228 pages)
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:	click for full text (PQDT)
ISBN:	9781369685558

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

Pulsar Search Using Supervised Machine Learning. - 1 online resource (228 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369685558Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Pulsar Search Using Supervised Machine Learning.
LDR:03137ntm a2200349Ki 4500 001 916766
005 20180928111500.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369685558
035 $a (MiAaPQ)AAI10265451
035 $a (MiAaPQ)scisnova:10464
035 $a AAI10265451
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Ford, John M. $3 1190591
245 1 0 $a Pulsar Search Using Supervised Machine Learning.
264 0 $c 2017
300 $a 1 online resource (228 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Sumitra Mukherjee.
502 $a Thesis (Ph.D.)--Nova Southeastern University, 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Astronomy. $3 593935
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0606
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Nova Southeastern University. $b Computer Information Systems. $3 1190592
773 0 $t Dissertation Abstracts International $g 78-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10265451 $z click for full text (PQDT)