Predicting Plasma Temperature From Line Intensities Using ML Models
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915270687195136 |
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| author | Malviya, Ashwini |
| author_facet | Malviya, Ashwini |
| contents | In this work, ML models were used to predict the plasma temperature using the dataset obtained by implementing the CR-model for Na-like Krypton. The models included in the study are: Linear Regression, Lasso Regression, Support Vector Regression, Decision Trees, Random Forest, XGBoost, Multi-layer Perceptron and Convolutional Neural Network. For evaluating the models we used Mean Absolute Error, Mean Squared Error and R^2 Score as metrics, In our study Random Forest performed best as compared to other model considered, the study conclude that complex relation between the line-intensities and Plasma temperature can be capture by ML models and they can be used to predict the temperature with high accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Plasma Temperature From Line Intensities Using ML Models Malviya, Ashwini Plasma Physics In this work, ML models were used to predict the plasma temperature using the dataset obtained by implementing the CR-model for Na-like Krypton. The models included in the study are: Linear Regression, Lasso Regression, Support Vector Regression, Decision Trees, Random Forest, XGBoost, Multi-layer Perceptron and Convolutional Neural Network. For evaluating the models we used Mean Absolute Error, Mean Squared Error and R^2 Score as metrics, In our study Random Forest performed best as compared to other model considered, the study conclude that complex relation between the line-intensities and Plasma temperature can be capture by ML models and they can be used to predict the temperature with high accuracy. |
| title | Predicting Plasma Temperature From Line Intensities Using ML Models |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2505.01414 |