scholarly journals Weakly supervised classification in high energy physics

2017 ◽  
Vol 2017 (5) ◽  
Author(s):  
Lucio Mwinmaarong Dery ◽  
Benjamin Nachman ◽  
Francesco Rubbo ◽  
Ariel Schwartzman
Keyword(s):  
Supervised Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Weakly Supervised ◽  
Weakly Supervised Classification ◽  
Energy Physics

scholarly journals Weakly Supervised Classification For High Energy Physics

2018 ◽  
Vol 1085 ◽  
pp. 042006 ◽  
Author(s):  
Lucio Mwinmaarong Dery ◽  
Benjamin Nachman ◽  
Francesco Rubbo ◽  
Ariel Schwartzman
Keyword(s):  
Supervised Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Weakly Supervised ◽  
Weakly Supervised Classification ◽  
Energy Physics

Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

2020 ◽  
Vol 35 (23) ◽  
pp. 2050131
Author(s):  
Mohd Adli Md Ali ◽  
Nu’man Badrud’din ◽  
Hafidzul Abdullah ◽  
Faiz Kemi
Keyword(s):  
Anomaly Detection ◽  
Supervised Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Classification Model ◽  
Training Dataset ◽  
Weakly Supervised Learning ◽  
Physics Community ◽  
Weakly Supervised ◽  
Energy 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.

Development of Silicon Sensor Characterization System for Future High Energy Physics Experiments

2015 ◽  
Vol 3 (1) ◽  
pp. 41-45
Author(s):  
Preeti Kumari ◽  
◽  
Kavita Lalwani ◽  
Ranjit Dalal ◽  
Ashutosh Bhardwaj ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Physics Experiments ◽  
Silicon Sensor ◽  
Energy Physics
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