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Diffusion model advances particle physics data extraction

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new conditional diffusion model to extract transverse momentum dependent parton distribution functions (TMD PDFs) from raw scattering event data. This approach bypasses traditional methods that rely on parameterized functional forms, offering greater flexibility and simplifying uncertainty quantification. Tested on simulated data from the CLAS12 experiment, the model demonstrated its ability to accurately recover TMD PDFs and provide reliable estimates even with limited event statistics, making it relevant for current and future experiments at facilities like Jefferson Lab and the Electron-Ion Collider. AI

IMPACT This diffusion model approach could accelerate scientific discovery by improving data analysis efficiency and accuracy in particle physics experiments.

RANK_REASON The cluster describes a research paper detailing a new machine learning model for scientific data analysis.

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Diffusion model advances particle physics data extraction

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jitao Xu, Christopher Cocuzza, Kevin Braga, Daniel Lersch, Nobuo Sato, Yaohang Li ·

    Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

    arXiv:2608.27077v1 Announce Type: cross Abstract: Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon structure program at Jefferson Lab and the future Electr…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion

    Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon structure program at Jefferson Lab and the future Electron-Ion Collider. Traditional extraction methods re…

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