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| Paper: |
A Machine Learning Approach for Dark-Matter Particle
Identification Under Extreme Class Imbalance |
| Volume: |
523, Astronomical Data Analysis Software and Systems XXVIII |
| Page: |
115 |
| Authors: |
Sutrisno, R.; Vilalta, R.; Renshaw, A. |
| Abstract: |
The Darkside-50 collaboration is an international experiment conducted at the Laboratori Nazionali del Gran Sasso in Italy, where low-radioactivity liquid argon is used within a dual-phase time projection chamber to detect weakly interacting massive particles (WIMPS), one of the leading candidates for dark matter. The Darkside-50 experiment faces two main data-analysis challenges: extreme class imbalance and large datasets. In this paper we show how machine learning techniques can be employed, even under the presence of samples exhibiting extreme class-imbalance (i.e., extreme signal-to-noise ratio). In our data-analysis study, the ratio of negative or background events to positive or signal events is highly imbalanced by a factor of 107. This poses a serious challenge when the objective is to identify a signal that can be easily misclassified as background. We compare several techniques in machine learning that deal with the class imbalance problem: ROUS, SMOTE, and MSMOTE. Experimental results on real data obtained from the Darkside-50 experiment show very high recall values (∼0.985), with reasonable performance in terms of precision (∼0.80) and F1-score (∼0.875). |
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