6 papers
Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
Haruto Kitagawa, Satsuki Nishimura, Hajime Otsuka
We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence…
Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
Haruto Kitagawa, Coh Miyao, Satsuki Nishimura +1
We revisit one-zero and two-zero textures of the neutrino mass matrix under current experimental and cosmological constraints. We identify the phenomenologically viable texture str…
Exploring the flavor structure of leptons via diffusion models
Satsuki Nishimura, Hajime Otsuka, Haruki Uchiyama
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 s…
Reinforcement learning-based statistical search strategy for an axion model from flavor
Satsuki Nishimura, Coh Miyao, Hajime Otsuka
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…
Coupling Selection Rules in Heterotic Calabi-Yau Compactifications
Jun Dong, Tatsuo Kobayashi, Ryusei Nishida +2
We study coupling selection rules of chiral matter fields in heterotic string theory with standard embedding. These selection rules are determined by topological properties of Cala…
Diffusion-model approach to flavor models: A case study for modular flavor model
Satsuki Nishimura, Hajime Otsuka, Haruki Uchiyama
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 ge…