Neutrino mass textures analyzed with ML and cosmological data

This research paper revisits neutrino mass textures, specifically one-zero and two-zero configurations, by analyzing current experimental and cosmological data. The study identifies viable texture structures using neutrino oscillation parameters, cosmic microwave background (CMB) data, and bounds on neutrino masses. It highlights that while some two-zero textures are allowed with only CMB constraints, the inclusion of Baryon Acoustic Oscillation (BAO) data restricts viable options to A-series textures. The paper also employs machine learning techniques, such as flow matching, to analyze one-zero textures and their predictions for various neutrino mass properties. AI

IMPACT This research utilizes machine learning techniques, specifically flow matching, to analyze complex physics data, potentially advancing the application of AI in scientific discovery.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical physics research. [lever_c_demoted from research: ic=2 ai=0.4]

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• arXiv
• A series
• Bao
• cosmic microwave background
• Dirac CP phase
• Flow Matching for Generative Modeling
• High Energy Physics - Phenomenology
• JIS B series
• machine learning
• neutrinoless double beta decay
• One Zero

• paper
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