Exploring the flavor structure of leptons via diffusion models

• URL: http://arxiv.org/abs/2503.21432v1
• Date: Thu, 27 Mar 2025 12:17:00 GMT
• Title: Exploring the flavor structure of leptons via diffusion models
• Authors: Satsuki Nishimura, Hajime Otsuka, Haruki Uchiyama,
• Abstract summary: 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.<n>By utilizing transfer learning, the diffusion model generates 104 solutions consistent with the neutrino mass squared differences and the leptonic mixing angles.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Conditional Synthesis of 3D Molecules with Time Correction Sampler [58.0834973489875]
  Time-Aware Conditional Synthesis (TACS) is a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties.
  arXiv  Detail & Related papers  (2024-11-01T12:59:25Z)
• Chemistry-Inspired Diffusion with Non-Differentiable Guidance [10.573577157257564]
  Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. We propose a novel approach that leverage domain knowledge from quantum chemistry as a non-differentiable oracle to guide an unconditional diffusion model. Instead of relying on neural networks, the oracle provides accurate guidance in the form of estimated gradients, allowing the diffusion process to sample from a conditional distribution specified by quantum chemistry.
  arXiv  Detail & Related papers  (2024-10-09T03:10:21Z)
• Neural Message Passing Induced by Energy-Constrained Diffusion [79.9193447649011]
  We propose an energy-constrained diffusion model as a principled interpretable framework for understanding the mechanism of MPNNs. We show that the new model can yield promising performance for cases where the data structures are observed (as a graph), partially observed or completely unobserved.
  arXiv  Detail & Related papers  (2024-09-13T17:54:41Z)
• New algorithms for sampling and diffusion models [0.0]
  We introduce a novel sampling method for known distributions and a new algorithm for diffusion generative models with unknown distributions. Our approach is inspired by the concept of the reverse diffusion process, widely adopted in diffusion generative models.
  arXiv  Detail & Related papers  (2024-06-14T02:30:04Z)
• An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization [59.63880337156392]
  Diffusion models have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. Despite the significant empirical success, theory of diffusion models is very limited. This paper provides a well-rounded theoretical exposure for stimulating forward-looking theories and methods of diffusion models.
  arXiv  Detail & Related papers  (2024-04-11T14:07:25Z)
• Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models [59.331993845831946]
  Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. This paper provides the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models.
  arXiv  Detail & Related papers  (2024-03-03T23:15:48Z)
• On Error Propagation of Diffusion Models [77.91480554418048]
  We develop a theoretical framework to mathematically formulate error propagation in the architecture of DMs. We apply the cumulative error as a regularization term to reduce error propagation. Our proposed regularization reduces error propagation, significantly improves vanilla DMs, and outperforms previous baselines.
  arXiv  Detail & Related papers  (2023-08-09T15:31:17Z)
• Lipschitz Singularities in Diffusion Models [64.28196620345808]
  Diffusion models often display the infinite Lipschitz property of the network with respect to time variable near the zero point.<n>We propose a novel approach, dubbed E-TSDM, which alleviates the Lipschitz singularities of the diffusion model near the zero point.<n>Our work may advance the understanding of the general diffusion process, and also provide insights for the design of diffusion models.
  arXiv  Detail & Related papers  (2023-06-20T03:05:28Z)
• Information-Theoretic Diffusion [18.356162596599436]
  Denoising diffusion models have spurred significant gains in density modeling and image generation. We introduce a new mathematical foundation for diffusion models inspired by classic results in information theory.
  arXiv  Detail & Related papers  (2023-02-07T23:03:07Z)
• An optimal control perspective on diffusion-based generative modeling [9.806130366152194]
  We establish a connection between optimal control and generative models based on differential equations (SDEs) In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. We develop a novel diffusion-based method for sampling from unnormalized densities.
  arXiv  Detail & Related papers  (2022-11-02T17:59:09Z)
• Diffusion Models in Vision: A Survey [73.10116197883303]
  A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens.
  arXiv  Detail & Related papers  (2022-09-10T22:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.