國立虎尾科技大學 |

Deep Learning for Experimental Physics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep Learning for Experimental Physics./
作者:	Sadowski, Peter.
面頁冊數:	1 online resource (83 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369228878

Deep Learning for Experimental Physics.
Sadowski, Peter.

Deep Learning for Experimental Physics. - 1 online resource (83 pages)

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2016.

Includes bibliographical references

Experimental physicists explore the fundamental nature of the universe by probing the properties of subatomic particles using specialized detectors. These detectors generate vast quantities of data that must undergo multiple stages of processing, such as dimensionality-reduction and feature extraction, before statistical analysis and interpretation. The processing steps are typically designed by physicists, using expert knowledge and intuition. However, this approach is human-limited and costly --- useful information is inevitably lost. Machine learning offers an alternative approach in which processing steps are instead learned from data. In particular, deep learning is the approach of learning many processing steps simultaneously. In this dissertation, we apply deep learning to a number of data analysis pipelines in experimental physics. We demonstrate that human-engineered processing steps can instead be learned from data, and that the deep learning approach can retain useful information from low-level data that is otherwise lost. Ultimately, this has the potential to both streamline data analysis pipelines and increase the statistical power of experiments, speeding up scientific discoveries.

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

Mode of access: World Wide Web

ISBN: 9781369228878Subjects--Topical Terms:

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

554714
Electronic books.

Deep Learning for Experimental Physics.
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500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
500 $a Adviser: Pierre Baldi.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2016.
504 $a Includes bibliographical references
520 $a Experimental physicists explore the fundamental nature of the universe by probing the properties of subatomic particles using specialized detectors. These detectors generate vast quantities of data that must undergo multiple stages of processing, such as dimensionality-reduction and feature extraction, before statistical analysis and interpretation. The processing steps are typically designed by physicists, using expert knowledge and intuition. However, this approach is human-limited and costly --- useful information is inevitably lost. Machine learning offers an alternative approach in which processing steps are instead learned from data. In particular, deep learning is the approach of learning many processing steps simultaneously. In this dissertation, we apply deep learning to a number of data analysis pipelines in experimental physics. We demonstrate that human-engineered processing steps can instead be learned from data, and that the deep learning approach can retain useful information from low-level data that is otherwise lost. Ultimately, this has the potential to both streamline data analysis pipelines and increase the statistical power of experiments, speeding up scientific discoveries.
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 High energy physics. $3 1180812
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a University of California, Irvine. $b Computer Science. $3 1179555
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