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Neutrino Physics and Machine Learning 2026

15–19 Jun 2026
UC Irvine
America/New_York timezone

Session

Methods: Generative & Advanced Reconstruction Methods

16 Jun 2026, 13:50
The Interdisciplinary Science and Engineering Building (UC Irvine)

The Interdisciplinary Science and Engineering Building

UC Irvine

419 Physical Sciences Quad, Irvine, CA 92697

Presentation materials

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  1. 9. Noise-Aware Representation Learning for Signal Reconstruction in Rare-Event Detectors
    Dowling Wong (Karlsruhe Institute of Technology)
    16/06/2026, 13:50
    AI/ML R&D
    AI/ML R&D

    Cryogenic detectors aiming to push detection thresholds to ever lower energies must operate close to the noise limit, where reducing the threshold further risks increasing false triggers. The DELight experiment will use a superfluid helium detector instrumented with large area cryogenic microcalorimeters to probe sub-GeV dark matter via faint quasiparticle and photon signals.

    We formulate...

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  2. 45. Data-Driven Generation and Inference of LArTPC Images Using Latent Diffusion Models
    Zeviel Imani (Tufts University)
    16/06/2026, 14:15
    Sharable AI Tools
    Shareable AI Tools

    We present a data-driven approach for the generation and inference of 2D LArTPC events. Using a conditional latent diffusion model trained on LArTPC images, we have demonstrated the generation of physically realistic protons. Combining this conditional model with Earth Mover’s Distance (EMD) enables us to perform stochastic gradient descent to efficiently infer the 3D momentum of an input...

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  3. 53. NuGraph3: Hierarchical GNN for Neutrino Physics Event Reconstruction
    Dr V Hewes (University of Cincinnati)
    16/06/2026, 15:00
    AI/ML R&D
    AI/ML R&D

    NuGraph3 is a hierarchical Graph Neural Network (GNN) architecture for event reconstruction in neutrino physics experiments, utilized across a range of Liquid Argon Time Projection Chamber (LArTPC) experiments including MicroBooNE, ICARUS, SBND and DUNE. This third-generation architecture leverages a range of different decoders to predict multiple outputs simultaneously on a heterogeneous...

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  4. 7. Machine Learning Track Inference in the Dead Regions of DUNE's Near Detector Prototype: The Liquid Argon TPC Dead Region Inference Project
    Hilary Utaegbulam (University of Rochester)
    16/06/2026, 15:25
    AI/ML R&D
    AI/ML R&D

    The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment's Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors with pixelated charge readouts and inactive regions wherein there is no sensitivity to energy depositions in the liquid argon. These inactive regions are located between the active...

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  5. 12. Exploring Generative Adversarial Networks for the simulation of neutrino scattering off nuclei and their adaptability to new data using transfer learning.
    Dr Jose Luis Bonilla Ramirez (University of Wroclaw)
    16/06/2026, 16:05
    AI/ML R&D
    AI/ML R&D

    As we enter the era of precision in neutrino physics, it is essential to better understand and limit systematic uncertainties in experimental setups. With the improvement of statistics and measurement quality in detectors, the uncertainties arising from Monte Carlo (MC) generators, used throughout the data analysis process, become more relevant. These uncertainties stem from limitations in the...

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