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New ML algorithm Profile OmniFold enhances particle physics data correction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new machine learning algorithm called Profile OmniFold to improve the accuracy of unfolding, a process used in particle physics to correct measured data for detector effects. This new method extends the existing OmniFold algorithm by incorporating nuisance parameters, which account for uncertainties in the detector's forward model. The effectiveness of Profile OmniFold was demonstrated through a Gaussian example and a case study using simulated data from the CMS Experiment at the Large Hadron Collider. AI

IMPACT Enhances scientific data analysis capabilities by improving the accuracy of detector effect corrections in particle physics.

RANK_REASON The cluster contains an academic paper detailing a new machine learning algorithm for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ML algorithm Profile OmniFold enhances particle physics data correction

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The cluster contains an academic paper detailing a new machine learning algorithm for scientific data analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Huanbiao Zhu, Krish Desai, Mikael Kuusela, Vinicius Mikuni, Benjamin Nachman, Larry Wasserman ·

    Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters

    arXiv:2512.07074v3 Announce Type: replace-cross Abstract: Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding. In cases with complex instruments, the d…

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