2022
DOI: 10.48550/arxiv.2201.05659
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

A machine learning-based methodology for pulse classification in dual-phase xenon time projection chambers

P. Brás,
F. Neves,
A. Lindote
et al.

Abstract: Machine learning techniques are now well established in experimental particle physics, allowing detector data to be analysed in new and unique ways. The identification of signals in particle observatories is an essential data processing task that can potentially be improved using such methods. This paper aims at exploring the benefits that a dedicated machine learning approach might provide to the classification of signals in dual-phase noble gas time projection chambers. A full methodology is presented, from … Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 42 publications
(58 reference statements)
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?