Abstract

The ALPHA experiment at CERN is designed to produce and trap antihydrogen to the purpose of making a precise comparison with hydrogen. The basic technique consists of driving an antihydrogen resonance which will cause the antiatom to leave the trap and annihilate. The main background to antihydrogen detection is due to cosmic rays. When an experimental cycle extends for several minutes, while the number of trapped antihydrogen remains fixed, background rejection can become challenging. Machine learning methods have been employed in ALPHA for several years, leading to a dramatic reduction of the background contamination. This allowed ALPHA to perform the first laser spectroscopy experiment on antihydrogen.

Details

Title
Machine learning for antihydrogen detection at ALPHA
Author
Capra, A 1 

 TRIUMF, 4004 Wesbrook Mall, Vancouver BC V6T 2A3, Canada 
Publication year
2018
Publication date
Sep 2018
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2572548717
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.