Speaker
Description
This study explores the application of machine learning, specifically a one-dimensional Convolutional Neural Network (1D CNN), to differentiate the signals from cosmic rays from those of background when using a single plastic scintillation detector. A comprehensive dataset, combining signals from cosmic ray and gamma events, was collected for the machine learning approach. The 1D CNN model, constructed using the Keras library with TensorFlow as the backend, was compiled with precision, utilizing the Stochastic Gradient Descent (SGD) as the optimizer and the sparse_categorical_crossentropy as the loss function. The proposed model achieved promising results, demonstrating its ability to reliably distinguish between the signals of cosmic-ray and gamma events.
| Minioral | Yes |
|---|---|
| IEEE Member | Yes |
| Are you a student? | No |
