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Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs -- Revolutionizing Event Generation for Sparse Data
Neutrino physics stands at the forefront of fundamental research, probing the universe's most elusive particles. Understanding how neutrinos interact with matter is crucial for interpreting experimental results from cutting-edge detectors like Hyper-Kamiokande and DUNE. This paper, "Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs," introduces a groundbreaking application of artificial intelligence to enhance the simulation of these complex interactions, especially in scenarios where experimental data is scarce.
Abstract Summary
This research explores the power of transfer learning (TL) to extend the capabilities of a Generative Adversarial Network (GAN) initially trained on synthetic charged-current (CC) muon neutrino-carbon scattering data. The core idea is to "adapt" this pre-trained model to generate events for different interactions, specifically neutrino-argon and antineutrino-carbon scattering, and even for modified neutrino-carbon models. The study rigorously compares transfer learning against training models from scratch, using both small (10,000 events) and larger (100,000 events) datasets. The findings convincingly demonstrate that transfer learning significantly outperforms de novo training, particularly when data is limited. This approach offers a highly promising solution for creating event generators in experiments where collecting extensive data is challenging.
Research Context and Motivation
Neutrino-nucleus interactions are a cornerstone of modern particle physics experiments. These interactions provide insights into neutrino properties, nuclear structure, and fundamental forces. Large-scale experiments, such as Hyper-Kamiokande and DUNE, heavily rely on accurate simulations of these interactions to reconstruct neutrino properties and understand detector responses.
The current state of the field shows several key challenges. Existing Monte Carlo (MC) event generators, while sophisticated, often rely on theoretical models that incorporate complex approximations, especially concerning nuclear effects. These models require extensive tuning against experimental data. However, this tuning process cannot fully compensate for inherent model limitations or account for all real-world complexities. Furthermore, obtaining...