2022
DOI: 10.1103/physrevc.105.065501
|View full text |Cite|
|
Sign up to set email alerts
|

New model of intranuclear neutron-antineutron transformations in O816

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 52 publications
0
1
0
Order By: Relevance
“…For 40 Ar nuclei, R is expected to take on a value of 5.6 × 10 22 s −1 with an uncertainty of 20% [19]. The most stringent limit on the free neutron transition time is provided by ILL in Grenoble [8] at 0.86 × 10 8 s at the 90% confidence level (CL), while the Super-Kamiokande experiment, using oxygen-bound neutrons and an associated suppression factor of 5.17 × 10 22 s −1 [20,21], corresponds to τ n−n > 4.7 × 10 8 s at the 90% CL [4]. This work presents a deep learning (DL)-based analysis of MicroBooNE data, making use of a sparse convolutional neural network (CNN) [22,23], to search for n → n like signals using primarily their topological signature.…”
Section: Introductionmentioning
confidence: 99%
“…For 40 Ar nuclei, R is expected to take on a value of 5.6 × 10 22 s −1 with an uncertainty of 20% [19]. The most stringent limit on the free neutron transition time is provided by ILL in Grenoble [8] at 0.86 × 10 8 s at the 90% confidence level (CL), while the Super-Kamiokande experiment, using oxygen-bound neutrons and an associated suppression factor of 5.17 × 10 22 s −1 [20,21], corresponds to τ n−n > 4.7 × 10 8 s at the 90% CL [4]. This work presents a deep learning (DL)-based analysis of MicroBooNE data, making use of a sparse convolutional neural network (CNN) [22,23], to search for n → n like signals using primarily their topological signature.…”
Section: Introductionmentioning
confidence: 99%