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  • Open Access

Rolling down the leptonic BSM landscape using machine learning techniques

Alfredo Aranda1,2,3,*, Raymundo Ramos4,†, and Alexander J. Stuart1,2,‡

  • *Contact author: fefo@ucol.mx
  • †Contact author: raramos@kias.re.kr
  • ‡Contact author: astuart@ucol.mx
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Phys. Rev. D 114, 075037 – Published 28 October, 2026

DOI: https://doi.org/10.1103/g5l2-bzs4

Abstract

In this work, we adapt and apply techniques from machine learning to the exploration of physics beyond the Standard Model in the leptonic sector. Namely, we employ initialization and optimization, as they are applied in machine learning, to minimize a loss function that describes textures or conditions which we want in the neutrino mass matrix. The model free parameters are explored during the optimization, and after training for a number of optimization steps, we obtain matrices that approximately follow the desired forms, as well as their corresponding optimized parameters. We also discuss extensions and additional applications of the ideas presented here in conjunction with other methods based on artificial intelligence.

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Phys. Rev. D 114, 075037– Published 28 October, 2026
  • Received 10 June 2026
  • Accepted 5 October 2026
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