Exploring the flavor structure of leptons via diffusion models
We propose a method to explore the flavor structure of leptons using diffusion models, which are known as one of generative artificial intelligence (generative AI). We consider a simple extension of the Standard Model with the type I seesaw mechanism and train a neural network to generate the neutrino mass matrix. By utilizing transfer learning, the diffusion model generates 104 solutions that are consistent with the neutrino mass squared differences and the leptonic mixing angles. The distributions of the CP phases and the sums of neutrino masses, which are not included in the conditional labels but are calculated from the solutions, exhibit non-trivial tendencies. In addition, the effective mass in neutrinoless double beta decay is concentrated near the boundaries of the existing confidence intervals, allowing us to verify the obtained solutions through future experiments. An inverse approach using the diffusion model is expected to facilitate the experimental verification of flavor models from a perspective distinct from conventional analytical methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Transfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Exploring the flavor structure of quarks and leptons with reinforcement learning
We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with $U(1)$ flavor symmetry. By training neu…
reinforcement-learningReinforcement LearningReinforcement learning-based statistical search strategy for an axion model from flavor
We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model param…
reinforcement-learningReinforcement LearningFlavorDiffusion: Predicting Food Pairings and Chemical Interactions Using Diffusion Models
The study of food pairing has evolved beyond subjective expertise with the advent of machine learning. This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interacti…
Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model
We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (genera…
modelFood Pairing Unveiled: Exploring Recipe Creation Dynamics through Recommender Systems
In the early 2000s, renowned chef Heston Blumenthal formulated his "food pairing" hypothesis, positing that if foods share many flavor compounds, then they tend to taste good when eaten together. In 2011, Ahn et al. cond…
Collaborative FilteringRecommendation Systems