An Educational Approach to Higgs Boson Hunting Using Machine Learning Classification Algorithms on ATLAS Open Data Article Swipe
In this study, the performance of several classification algorithms that are used to separate the H → ττ signal from background is investigated. The data set came from the publicly available ATLAS data, which was utilized for the Machine Learning (ML) competition. The data was obtained from a full ATLAS simulation and originated from proton-proton collisions. There are 250 thousand events in the data set, and 70% of them were used to train the algorithms. The primary objective of this research is to identify the signal events from the background events by using various ML methods in the context of high-energy physics. In order to discover a solution to the binary classification problem that was discussed earlier, six distinct classification algorithms were utilized. This article also compares the performance of these classification algorithms, including Linear Support Vector Machines (SVM), Radical SVM, Logistic Regression, K-Nearest Neighbours, XGBoost Classifier, and the AdaBoost Classifier. The best results were obtained using the XGBoost Classification method, which had an AUC of 0.84 ± 1.9 x 10-3 followed by the AdaBoost Classifier with an AUC of 0.82 ± 2.5 x 10-3.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.28979/jarnas.1242840
- https://dergipark.org.tr/en/download/article-file/2919199
- OA Status
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- References
- 31
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4385815749Canonical identifier for this work in OpenAlex
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https://doi.org/10.28979/jarnas.1242840Digital Object Identifier
- Title
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An Educational Approach to Higgs Boson Hunting Using Machine Learning Classification Algorithms on ATLAS Open DataWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-08-14Full publication date if available
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A. BatyList of authors in order
- Landing page
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https://doi.org/10.28979/jarnas.1242840Publisher landing page
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https://dergipark.org.tr/en/download/article-file/2919199Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://dergipark.org.tr/en/download/article-file/2919199Direct OA link when available
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.utilized | 35 |
| abstract_inverted_index.K-Nearest | 143 |
| abstract_inverted_index.available | 30 |
| abstract_inverted_index.discussed | 115 |
| abstract_inverted_index.including | 133 |
| abstract_inverted_index.objective | 77 |
| abstract_inverted_index.utilized. | 122 |
| abstract_inverted_index.Classifier | 175 |
| abstract_inverted_index.algorithms | 8, 120 |
| abstract_inverted_index.background | 20, 89 |
| abstract_inverted_index.originated | 52 |
| abstract_inverted_index.simulation | 50 |
| abstract_inverted_index.Classifier, | 146 |
| abstract_inverted_index.Classifier. | 150 |
| abstract_inverted_index.Neighbours, | 144 |
| abstract_inverted_index.Regression, | 142 |
| abstract_inverted_index.algorithms, | 132 |
| abstract_inverted_index.algorithms. | 74 |
| abstract_inverted_index.collisions. | 55 |
| abstract_inverted_index.high-energy | 100 |
| abstract_inverted_index.performance | 4, 128 |
| abstract_inverted_index.competition. | 41 |
| abstract_inverted_index.investigated. | 22 |
| abstract_inverted_index.proton-proton | 54 |
| abstract_inverted_index.Classification | 159 |
| abstract_inverted_index.classification | 7, 111, 119, 131 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5108015276 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I2799303410 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.05400143 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |