Home > Research > Publications & Outputs > A first search for argon-bound neutron-antineut...

Associated organisational unit

Text available via DOI:

View graph of relations

A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC

Research output: Working paperPreprint

Published

Standard

A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC. / MicroBooNE Collaboration.
2022.

Research output: Working paperPreprint

Harvard

APA

Vancouver

MicroBooNE Collaboration. A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC. 2022 May 12. doi: 10.2172/2406083

Author

Bibtex

@techreport{b5dd815ead82448a969aef1821dfd982,
title = "A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC",
abstract = "The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n − {\=n}) oscillation. The n − {\=n} oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n − {\=n} oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n − {\=n} oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).",
author = "{MicroBooNE Collaboration} and Jaroslaw Nowak",
year = "2022",
month = may,
day = "12",
doi = "10.2172/2406083",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2022/5/12

Y1 - 2022/5/12

N2 - The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n − n̄) oscillation. The n − n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n − n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n − n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

AB - The use of Liquid Argon Time Projection Chambers (LArTPCs) as a detector technology in neutrino experiments has grown considerably over the past two decades. The excellent spatial and calorimetric resolution offered by LArTPCs enable precise neutrino oscillation measurements as well as beyond-Standard Model searches. One such search, which is the focus of this note, is the search for nucleus-bound neutron-antineutron (n − n̄) oscillation. The n − n̄ oscillation process is a baryon number violating process that produces a unique, star-like topology as a result of multiple final state pions. This unique signature is a key feature that may be used to search for this signal process. This note describes a machine learning-based analysis of MicroBooNE data, making use of a sparse convolutional neural network to search for n − n̄ oscillation-like signals in MicroBooNE. While the future DUNE LArTPC can search for this signature with high sensitivity, existing MicroBooNE data can be used to demonstrate and validate methodologies that can be used as part of the DUNE search. This document presents the first-ever search for n − n̄ oscillation in a LArTPC, using MicroBooNE off-beam data (data collected when the neutrino beam was not running).

U2 - 10.2172/2406083

DO - 10.2172/2406083

M3 - Preprint

BT - A first search for argon-bound neutron-antineutron oscillation using the MicroBooNE LArTPC

ER -