Conveners
parallel session B: ML
- Mihoko Nojiri
-
Haruto Kitagawa18/02/2026, 14:50
In this talk, we present a model-independent analysis based on a latent diffusion model to address the flavor structure of leptons. The latent diffusion model combines a diffusion model with a variational autoencoder as a generative AI framework. By generating a wide variety of parameter sets consistent with experimental observations, we find non-trivial features characterizing lepton flavor...
Go to contribution page -
Rafal Maselek18/02/2026, 15:10
Searches for BSM Physics have produced many theoretical ideas, but only a few can be directly tested at the LHC. Reinterpreting existing results is therefore essential for constraining a wider range of models. Independent of the specific reinterpretation method, the final step always involves statistical analysis and hypothesis testing. Accurate tests require detailed information on...
Go to contribution page -
Satsuki Nishimura18/02/2026, 15:30
Recent studies actively apply machine learning to the exploration of flavor physics. In analyzing the flavor structure of the lepton sector, we apply reinforcement learning to a $U(1)$ flavor model with $L_{e}-L_{mu}-L_{tau}$ charges. By testing multiple architectures to explore charge combinations, we develop a strategy to efficiently achieve high-precision solutions. It turns out that the...
Go to contribution page