Development of the trigger algorithm for the MONOLITH experiment Article Swipe
The MONOLITH project is proposed to prove atmospheric neutrino oscillations and to improve the corresponding measurements of Super-Kamiokande. The MONOLITH detector consists of a massive (34 kt) magnetized iron tracking calorimeter and is optimized for muon neutrino detection. This diploma thesis presents the development of the trigger algorithm for the MONOLITH experiment and related test measurements. Chapter two gives an introduction to the mechanism of neutrino oscillations. The two flavor approximation and the three flavor mechanism are described and influences of matter on neutrino oscillations are discussed. The principles of neutrino oscillation experiments are discussed and the results of Super-Kamiokande, a neutrino oscillation experiment, are presented. Super-Kamiokande gave the strongest indications for atmospheric neutrino oscillations so far. The third chapter introduces the MONOLITH project in the context of atmospheric neutrino oscillations. The MONOLITH detector is described and the main active component, the glass spark chamber, is presented. Chapter four presents the practical part of this thesis. A test setup of a glass spark chamber is built up including a cosmics trigger and a data acquisition system. Cosmic ray muons are used for the investigation of the chamber. During a long term test, the stability of the efficiency and the noise rate of the chamber are investigated. A status report of the results is given. The results are taken as input for the trigger development. In chapter five, the development of the trigger algorithm is presented. In the beginning, the structural design of the trigger algorithm is described. The efficiency and the rate of the trigger algorithm are investigated using two event sources, a Monte Carlo neutrino event sample and a generated noise sample. For the analysis, the data sources are processed by several processing stages which are visualized by corresponding event displays. In the course of the data processing, dead zones of the detector are implemented. The optimization of several trigger parameter combination results in the standard trigger algorithm. This algorithm is investigated in detail and the influences of possible changes in detector parameters are analyzed. In the end, the performance of the standard trigger algorithm is presented. The agreement with the requirements is confirmed. (orig.)
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://bib-pubdb1.desy.de/record/300383
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3186853720
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3186853720Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3204/pubdb-2016-02292Digital Object Identifier
- Title
-
Development of the trigger algorithm for the MONOLITH experimentWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-09Full publication date if available
- Authors
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O. GutscheList of authors in order
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https://bib-pubdb1.desy.de/record/300383Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://bib-pubdb1.desy.de/record/300383Direct OA link when available
- Concepts
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Monolith, Computer science, Algorithm, Chemistry, Biochemistry, CatalysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.structural | 239 |
| abstract_inverted_index.visualized | 287 |
| abstract_inverted_index.acquisition | 174 |
| abstract_inverted_index.atmospheric | 7, 112, 128 |
| abstract_inverted_index.calorimeter | 30 |
| abstract_inverted_index.combination | 312 |
| abstract_inverted_index.development | 43, 228 |
| abstract_inverted_index.experiment, | 103 |
| abstract_inverted_index.experiments | 92 |
| abstract_inverted_index.indications | 110 |
| abstract_inverted_index.oscillation | 91, 102 |
| abstract_inverted_index.performance | 340 |
| abstract_inverted_index.processing, | 298 |
| abstract_inverted_index.development. | 223 |
| abstract_inverted_index.implemented. | 305 |
| abstract_inverted_index.introduction | 60 |
| abstract_inverted_index.investigated | 257, 322 |
| abstract_inverted_index.measurements | 15 |
| abstract_inverted_index.optimization | 307 |
| abstract_inverted_index.oscillations | 9, 84, 114 |
| abstract_inverted_index.requirements | 352 |
| abstract_inverted_index.approximation | 70 |
| abstract_inverted_index.corresponding | 14, 289 |
| abstract_inverted_index.investigated. | 205 |
| abstract_inverted_index.investigation | 183 |
| abstract_inverted_index.measurements. | 55 |
| abstract_inverted_index.oscillations. | 66, 130 |
| abstract_inverted_index.Super-Kamiokande | 106 |
| abstract_inverted_index.Super-Kamiokande, | 99 |
| abstract_inverted_index.Super-Kamiokande. | 17 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101728612 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.00290947 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |