activity
20222026
most citedExploring the flavor structure of quarks and leptons with reinforcement learning

10 citations · 21 across the 5 of their papers we have counts for

collaborators

5 papers

hep-ph2026

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…

hep-ph2024★ 4 cited

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…

hep-ph2024★ 1 cited

New Constraints on Gauged U(1) Models via Mixing

Kento Asai, Coh Miyao, Shohei Okawa +1

It is known that the model based on U(1) gauge symmetry can explain not only the discrepancy between the measured value of muon and the theoretical prediction, bu…

hep-ph2023★ 10 cited

Exploring the flavor structure of quarks and leptons with reinforcement learning

Satsuki Nishimura, Coh Miyao, Hajime Otsuka

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…

hep-ph2022★ 6 cited

Scalar Dark Matter with a Flavored Mediator

Kento Asai, Coh Miyao, Shohei Okawa +1

We study a renormalizable scalar singlet dark matter model based on lepton flavor symmetry. A -philic scalar doublet is introduced to explain the discrepancy between the…