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Diffusion model extracts particle momentum distributions from raw data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel conditional diffusion model to extract transverse momentum dependent parton distribution functions (TMD PDFs) from raw scattering event data. This method bypasses traditional parameterized functional forms, offering greater flexibility and simplifying uncertainty quantification. Tested on simulated data from the Electron-Ion Collider's CLAS12 experiment, the model accurately recovers underlying TMDs and provides reliable estimates even with limited event statistics, making it relevant for ongoing and future experiments. AI

IMPACT This method could accelerate the analysis of complex experimental data in high-energy physics, enabling faster discoveries.

RANK_REASON The item is an arXiv preprint detailing a new machine learning method for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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Diffusion model extracts particle momentum distributions from raw data

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The item is an arXiv preprint detailing a new machine learning method for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  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…

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