Neutrino Physics Research

Derniers papiers

• Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders

  Haruto Kitagawa, Satsuki Nishimura, Hajime Otsuka · 18 août 2026

  We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence (generative AI) method, we generate a broad set of solutions reproducing the experimentally measured values of the neutr…

• Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data

  Haruto Kitagawa, Coh Miyao, Satsuki Nishimura, Hajime Otsuka · 10 juillet 2026

  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 structures using the latest results on neutrino oscillation parameters, the cosmological bound on the sum of neutrino masse…

• Predicting the Neutrino Mass Ordering Using Neural Networks

  T. J. C. Bezerra, L. Asquith, E. Bannister, W. Shorrock · 18 juin 2026

  Determining the neutrino mass ordering remains a central open problem in particle physics. While next-generation long-baseline experiments are expected to resolve this question, current data provide limited sensitivity because the spectral differences between normal and inverted ordering are subtle …

• Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment

  Katharina Lachner, Sa\'ul Alonso-Monsalve, Benjamin Richards, Davide Sgalaberna · 1 juin 2026

  Modern machine learning techniques have become increasingly important in particle physics because of their powerful pattern-recognition capabilities, including in real-time data acquisition where stringent runtime constraints apply. This paper details the performance of deep-learning-based trigger a…

• From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD

  A. Matseiko (Institute for Nuclear Research of the Russian Academy of Sciences), G. Plotnikov (Institute for Nuclear Research of the Russian Academy of Sciences), I. Kharuk (Institute for Nuclear Research of the Russian Academy of Sciences) · 13 mai 2026

  We present a neural-network-based data processing pipeline for Baikal-GVD, designed to improve event reconstruction quality and accelerate neutrino candidates selection. The pipeline comprises three stages: fast suppression of extensive air shower events, suppression of noise optical modules activat…

• Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring

  Nicholas Tan Jerome, Nadia Aouadi, Christoph Koehler, Suren Chilingaryan, Andreas Kopmann · 12 mai 2026

  The Karlsruhe Tritium Neutrino Experiment (KATRIN) aims to measure the absolute neutrino mass with unprecedented sensitivity, requiring precise monitoring of the windowless gaseous tritium source, where tritium beta decay occurs. To track variations of the source activity, beta-induced X-ray spectro…

• Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics

  Dikshant Sagar, Kaiwen Yu, Alejandro Yankelevich, Jianming Bian, Pierre Baldi · 11 mai 2026

  Recent advances in Large Language Models (LLMs) have demonstrated their remarkable capacity to process and reason over structured and unstructured data modalities beyond natural language. In this work, we explore the applications of Vision Language Models (VLMs), specifically a fine-tuned variant of…

• Towards AI-assisted Neutrino Flavor Theory Design

  Jason Benjamin Baretz, Max Fieg, Vijay Ganesh, Aishik Ghosh, V. Knapp-Perez, Jake Rudolph, Daniel Whiteson · 17 avril 2026

  Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign fiel…

• Exploring the flavor structure of leptons via diffusion models

  Satsuki Nishimura, Hajime Otsuka, Haruki Uchiyama · 17 avril 2026

  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 neutri…

• Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning

  Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li, Wuming Luo, Jiahui Wei, Wenxing Fang, Hehe Fan · 31 mars 2026

  Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of $^{14}$C in LS, the photons induced by the $\beta$ decay of the $^{14}$C isotope inevitably contaminate the signal, degrading the energy r…

• Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers

  Giorgio Morales, Gregory Lehaut, Antonin Vacheret, Frederic Jurie, Jalal Fadili · 25 mars 2026

  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 dimensionali…

• Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs

  Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk · 20 mars 2026

  Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrino-argon and antineutrino-carbon interactions. We investigate how much of the unde…

• First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference

  Karla Tame-Narvaez, Steven Gardiner, Aleksandra \'Ciprijanovi\'c, Giuseppe Cerati · 11 mars 2026

  To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these s…

• Machine Learning Neutrino-Nucleus Cross Sections

  Daniel C. Hackett, Joshua Isaacson, Shirley Weishi Li, Karla Tame-Narvaez, Michael L. Wagman · 9 décembre 2025

  Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate…

• Panda: Self-distillation of Reusable Sensor-level Representations for High Energy Physics

  Samuel Young, Kazuhiro Terao · 2 décembre 2025

  Liquid argon time projection chambers (LArTPCs) provide dense, high-fidelity 3D measurements of particle interactions and underpin current and future neutrino and rare-event experiments. Physics reconstruction typically relies on complex detector-specific pipelines that use tens of hand-engineered p…

• Improving Neutrino Oscillation Measurements through Event Classification

  Sebastian A. R. Ellis, Daniel C. Hackett, Shirley Weishi Li, Pedro A. N. Machado, Karla Tame-Narvaez · 18 novembre 2025

  Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematicall…