Speaker
Description
Deep generative models have entered the mainstream with transformer-based chatbots and diffusion-based image generation models. We showcase the utility of such models with several studies focused on different aspects of neutrino physics.
Firstly, we present a method based on flow matching suitable for cross-section measurements in the precision era of neutrino physics. Using flow matching, we train a continuous normalizing flow and we show that it captures the underlying density described by variations of systematic parameters faithfully.
Secondly, we present an autoregressive transformed based method for paired generative tasks in neutrino physics. Unlike traditional transformers we frame the problem from a continuous perspective by predicting each dimension using a transformed mixture of Gaussians, allowing us to impose physically motivated constraints.
Finally, we show initial results on a diffusion-based 3D model for the generation of LArTPC events. We show ways of enhancing the scalability of the proposed method by utilizing hierarchical generation.
| Type of contribution | Talk: 30 minutes. |
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