Speaker
Description
We shall review the results of our recent work on the developments of the NuWro Monte Carlo generator of events. We are working on applying deep learning techniques to optimize the NuWro generator. In the first step, we work on the neural network model that generates the lepton-nucleus cross-sections. We obtained a deep neural network model that predicts the electron-carbon cross-section over a broad kinematic region, extending from the quasielastic peak through resonance excitation to the onset of deep-inelastic scattering. We considered two methods of obtaining the model-independent parametrizations and the corresponding uncertainties based on the ensemble method - the bootstrap aggregation and the Monte Carlo dropout. The loss is defined by $\chi^2$ that includes point-to-point uncertainties. Additionally, we include the systematic normalization uncertainties for each independent set of measurements. We compared the predictions to a test data set, excluded from the training process, theoretical predictions obtained within the spectral function approach, and additional measurements not included in the analysis.
| Type of contribution | Talk: 15 minutes. |
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