Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911956367048704 |
|---|---|
| author | Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Prasad, Hemant Sobczyk, Jan T. |
| author_facet | Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Prasad, Hemant Sobczyk, Jan T. |
| contents | Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the bootstrap approach and the Monte Carlo dropout approach. In our analysis, the $χ^2$ defines the loss function, including point-to-point and normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7$%. To test these models, we compare their predictions to test datasets excluded from the training process and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and theoretical calculations. We also perform a comparison to a dataset lying beyond the covered kinematic region, and find that the bootstrap approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17298 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods Kowal, Beata E. Graczyk, Krzysztof M. Ankowski, Artur M. Banerjee, Rwik Dharmapal Prasad, Hemant Sobczyk, Jan T. High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the bootstrap approach and the Monte Carlo dropout approach. In our analysis, the $χ^2$ defines the loss function, including point-to-point and normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7$%. To test these models, we compare their predictions to test datasets excluded from the training process and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and theoretical calculations. We also perform a comparison to a dataset lying beyond the covered kinematic region, and find that the bootstrap approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm. |
| title | Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2312.17298 |