Artificial intelligence

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), can significantly contribute to this field in several ways:

• Pattern Recognition and Event Reconstruction: Neutrinos are elusive particles that interact very weakly with matter, making their detection challenging. When neutrinos interact with the detector medium, they produce a cascade of secondary particles. Deep learning algorithms, such as Convolutional Neural Networks (CNNs), are highly effective in analyzing the spatial patterns of these interactions. They can reconstruct the tracks and showers produced by particles in a neutrino detector, distinguishing neutrino events from background noise.
• Data Analysis Enhancement: The volume of data generated in neutrino experiments, like those in the Large Hadron Collider or neutrino observatories, is enormous. Traditional data analysis methods can be time-consuming and may not fully exploit the complexity of the data. AI algorithms can process and analyze this data more efficiently, identifying correlations and features that might not be evident to human analysts.
• Simulation and Modeling: AI can assist in the simulation of neutrino interactions, which is crucial for understanding the expected signatures in detectors. Generative models, such as Generative Adversarial Networks (GANs), can be used to create realistic simulation data, helping to improve the understanding of detector responses and systematics.

Without the group, we are actively working on these various aspects in the context of neutrino physics.