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Identification and Classification of Radio Pulsar Signals Using Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identification and Classification of Radio Pulsar Signals Using Machine Learning./
作者:	Pang, Di.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	180 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Automatic classification. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29058033
ISBN:	9798426868380

Identification and Classification of Radio Pulsar Signals Using Machine Learning.
Pang, Di.

Identification and Classification of Radio Pulsar Signals Using Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 180 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--West Virginia University, 2021.

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

Automated single-pulse search approaches are necessary as ever-increasing amount of observed data makes the manual inspection impractical. Detecting radio pulsars using single-pulse searches, however, is a challenging problem for machine learning because pulsar signals often vary significantly in brightness, width, and shape and are only detected in a small fraction of observed data.The research work presented in this dissertation is focused on development of machine learning algorithms and approaches for single-pulse searches in the time domain. Specifically, (1) We developed a two-stage single-pulse search approach, named SinglePulse Event Group IDentification (SPEGID), which automatically identifies and classifies pulsars in radio pulsar search data. SPEGID first identifies pulse candidates as trial single-pulse event groups and then extracts features from the candidates and trains classifiers using supervised machine learning. SPEGID also addressed the challenges introduced by the current data processing techniques and successfully identified bright and dim candidates as well as other types of challenging pulsar candidates. (2) To address the lack of training data in the early stages of pulsar surveys, we explored the cross-surveys prediction. Our results showed that using instance-based and parameter-based transfer learning methods improved the performance of pulsar classification across surveys. (3) We developed a hybrid recommender system aimed to detect rare pulsar signals that are often missed by supervised learning. The proposed recommender system uses a target rare case to state users' requirements and ranks the candidates using a similarity function which is calculated as a weighted sum of individual feature similarities. Our hybrid recommender system successfully detects both low signal-to-noise ratio (S/N) pulsars and Fast Radio Bursts (FRBs).The approaches proposed in this dissertation were used to analyze data from the Green Bank Telescope 350 MHz drift (GBTDrift) pulsar survey and the Arecibo 327 MHz (AO327) drift pulsar survey and discovered eight pulsars that were overlooked in previous analysis done with existing methods.

ISBN: 9798426868380Subjects--Topical Terms:

1569653
Automatic classification.

Identification and Classification of Radio Pulsar Signals Using Machine Learning.
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