Alternate methods for anomaly detection in high-energy physics via semi-supervised learning
Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing Co Pte Ltd
2020
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| Online Access: | http://irep.iium.edu.my/82384/ http://irep.iium.edu.my/82384/1/Final-WS-IJMPA%20Sepohon.pdf |
| _version_ | 1848789289441165312 |
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| author | Md. Ali, Mohd. Adli Badrud’din, Nu’man Abdullah, Hafidzul Kemi, Faiz |
| author_facet | Md. Ali, Mohd. Adli Badrud’din, Nu’man Abdullah, Hafidzul Kemi, Faiz |
| author_sort | Md. Ali, Mohd. Adli |
| building | IIUM Repository |
| collection | Online Access |
| description | Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles. |
| first_indexed | 2025-11-14T17:54:21Z |
| format | Article |
| id | iium-82384 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T17:54:21Z |
| publishDate | 2020 |
| publisher | World Scientific Publishing Co Pte Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-823842022-07-15T03:24:19Z http://irep.iium.edu.my/82384/ Alternate methods for anomaly detection in high-energy physics via semi-supervised learning Md. Ali, Mohd. Adli Badrud’din, Nu’man Abdullah, Hafidzul Kemi, Faiz QA76 Computer software QC Physics Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles. World Scientific Publishing Co Pte Ltd 2020-07-13 Article PeerReviewed application/pdf en http://irep.iium.edu.my/82384/1/Final-WS-IJMPA%20Sepohon.pdf Md. Ali, Mohd. Adli and Badrud’din, Nu’man and Abdullah, Hafidzul and Kemi, Faiz (2020) Alternate methods for anomaly detection in high-energy physics via semi-supervised learning. International Journal of Modern Physics A. ISSN 0217-751X E-ISSN 1793-656X https://www.worldscientific.com/worldscinet/ijmpa |
| spellingShingle | QA76 Computer software QC Physics Md. Ali, Mohd. Adli Badrud’din, Nu’man Abdullah, Hafidzul Kemi, Faiz Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title | Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title_full | Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title_fullStr | Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title_full_unstemmed | Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title_short | Alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| title_sort | alternate methods for anomaly detection in high-energy physics via semi-supervised learning |
| topic | QA76 Computer software QC Physics |
| url | http://irep.iium.edu.my/82384/ http://irep.iium.edu.my/82384/ http://irep.iium.edu.my/82384/1/Final-WS-IJMPA%20Sepohon.pdf |