Particle identification for the NNBAR experiment at ESS
(2024) FYSK04 20241Department of Physics
Particle and nuclear physics
- Abstract
- The violation of baryon number, B, is a vital component of a process promoting the excess of matter over antimatter, as it is necessary to explain the baryon imbalance observed in the universe. Since experimental evidence for such an occurrence has yet to be discovered, the HIBEAM-NNBAR experiment, at the European Spallation Source facility, is a proposed two-stage program aimed at expanding the current sensitivity limits in the study of baryon number violation, particularly by considering the possibility of neutron oscillations into antineutrons and sterile states.
This thesis aims to improve and further develop the existing particle identification framework for the NNBAR detector, with a specific focus on the misidentification of... (More) - The violation of baryon number, B, is a vital component of a process promoting the excess of matter over antimatter, as it is necessary to explain the baryon imbalance observed in the universe. Since experimental evidence for such an occurrence has yet to be discovered, the HIBEAM-NNBAR experiment, at the European Spallation Source facility, is a proposed two-stage program aimed at expanding the current sensitivity limits in the study of baryon number violation, particularly by considering the possibility of neutron oscillations into antineutrons and sterile states.
This thesis aims to improve and further develop the existing particle identification framework for the NNBAR detector, with a specific focus on the misidentification of electrons and positrons as charged pions.
The thesis will thus investigate an optimised cut-based analysis selection to distinguish electrons and pions based on reconstructed particle-level variable distributions. Moreover, a machine learning model approach is also explored to further improve the efficiency of the identification.
The cut-based analysis has resulted in a reduction of misidentified electrons and positrons by 56%. In turn, the machine learning approach demonstrated a discrimination capability exceeding 90% for both classes. (Less) - Popular Abstract
- The universe is a complex balance of matter and antimatter. But there's a problem, we observe much more matter than antimatter. One important factor in this discussion is something called the violation of baryon number, which essentially means that matter particles, such as the familiar protons and neutrons, could switch to their antimatter counterparts, and vice versa. The HIBEAM-NNBAR experiment at the European Spallation Source is based on this idea.
To discover this mysterious process, the HIBEAM-NNBAR experiment needs to detect what happens when an antineutron meets a regular neutron. Generally, when antimatter and matter collide they annihilate and produce new particles. It is very important to know which particles are produced.... (More) - The universe is a complex balance of matter and antimatter. But there's a problem, we observe much more matter than antimatter. One important factor in this discussion is something called the violation of baryon number, which essentially means that matter particles, such as the familiar protons and neutrons, could switch to their antimatter counterparts, and vice versa. The HIBEAM-NNBAR experiment at the European Spallation Source is based on this idea.
To discover this mysterious process, the HIBEAM-NNBAR experiment needs to detect what happens when an antineutron meets a regular neutron. Generally, when antimatter and matter collide they annihilate and produce new particles. It is very important to know which particles are produced. What the HIBEAM-NNBAR experiment is particularly interested in are particles called "pions".
Pions are part of the group of particles called "hadrons" and they are made up of smaller particles called quarks, just like neutrons and protons.
Sometimes, in detectors, pions are confused with electrons. This is a big problem for the HIBEAM-NNBAR experiment, as recognising the pions is very important to discover if neutrons are converting to antimatter.
This thesis will be focused on trying to separate pions from electrons in the HIBEAM-NNBAR experiment. This will firstly be done by introducing a set of rules, to distinguish between pions and electrons, and later by using machine learning. The first method decreases the percentage of misidentified electrons by 56%, while the second correctly distinguishes electrons and pions 90% of the time. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9163136
- author
- Åstrand, Lucas LU
- supervisor
- organization
- course
- FYSK04 20241
- year
- 2024
- type
- M2 - Bachelor Degree
- subject
- keywords
- Particle Physics, NNBAR experiment, Baryon number violation, Neutron oscillations, Particle identification, European Spallation Source (ESS), Cut-based analysis, Machine learning
- language
- English
- id
- 9163136
- date added to LUP
- 2024-06-24 12:39:17
- date last changed
- 2024-06-24 12:39:17
@misc{9163136,
abstract = {{The violation of baryon number, B, is a vital component of a process promoting the excess of matter over antimatter, as it is necessary to explain the baryon imbalance observed in the universe. Since experimental evidence for such an occurrence has yet to be discovered, the HIBEAM-NNBAR experiment, at the European Spallation Source facility, is a proposed two-stage program aimed at expanding the current sensitivity limits in the study of baryon number violation, particularly by considering the possibility of neutron oscillations into antineutrons and sterile states.
This thesis aims to improve and further develop the existing particle identification framework for the NNBAR detector, with a specific focus on the misidentification of electrons and positrons as charged pions.
The thesis will thus investigate an optimised cut-based analysis selection to distinguish electrons and pions based on reconstructed particle-level variable distributions. Moreover, a machine learning model approach is also explored to further improve the efficiency of the identification.
The cut-based analysis has resulted in a reduction of misidentified electrons and positrons by 56%. In turn, the machine learning approach demonstrated a discrimination capability exceeding 90% for both classes.}},
author = {{Åstrand, Lucas}},
language = {{eng}},
note = {{Student Paper}},
title = {{Particle identification for the NNBAR experiment at ESS}},
year = {{2024}},
}