[2603.22342] Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers

High Energy Physics - Phenomenology arXiv:2603.22342 (hep-ph) [Submitted on 21 Mar 2026] Title:Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers Authors:Giorgio Morales, Gregory Lehaut, Antonin Vacheret, Frederic Jurie, Jalal Fadili View a PDF of the paper titled Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers, by Giorgio Morales and 4 other authors View PDF HTML (experimental) Abstract:Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these parameters from oscillation probability maps is, however, computationally challenging due to the maps' high dimensionality and nonlinear dependence on the underlying physics. Traditional inference methods, such as likelihood-based or Monte Carlo sampling approaches, require extensive simulations to explore the parameter space, creating major bottlenecks for large-scale analyses. In this work, we introduce a data-driven framework that reformulates atmospheric neutrino oscillation parameter inference as a supervised regression task over structured oscillation maps. We propose a hierarchical transformer architecture that explicitly models the two-dimensional structure of these maps, capturing angular dependencies at fixed energies and global correlations across the energy spectrum. To improve physical consistency, the model is trained using a sur...