Tracker Instrumentation for the Mu2e Experiment and Track Reconstruction with Deep Neural Networks

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Tracker Instrumentation for the Mu2e Experiment and Track Reconstruction with Deep Neural Networks

Published Date

2023

Publisher

Type

Thesis or Dissertation

Abstract

Tracker Instrumentation for the Mu2e Experimentand Track Reconstruction with Deep Neural Networks

Keywords

Description

University of Minnesota Ph.D. dissertation. ....2023. Major: Physics. Advisor: Ken Heller. 1 computer file (PDF); i, 301 pages.

Related to

Replaces

License

Collections

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Ciampa, Kate. (2023). Tracker Instrumentation for the Mu2e Experiment and Track Reconstruction with Deep Neural Networks. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/257015.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.