Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

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Publication Details

Journal
SciPost Physics
Published
2026-02-12
DOI
https://doi.org/10.21468/scipostphys.20.2.042
Primary Topic
Stability and Control of Uncertain Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Iterative HOMER with uncertainties

P. Ilten, Jure Zupan, Tilman Plehn, Christian Bierlich et al.
SciPost Physics
Stability and Control of Uncertain Systems
article

Iterative HOMER with uncertainties

P. Ilten, Jure Zupan, Tilman Plehn, Christian Bierlich, Manuel Szewc, Anja Butter, Ayodele Ore, M. Wilkinson, Tony Menzo, Sofia Palacios Schweitzer, S. Mrenna, Benoît Assi, Ahmed Youssef
article en

Abstract

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

SciPost PhysicsVol. 20(2)
Centre National de la Recherche Scientifique (FR), Fermi National Accelerator Laboratory (US), Lund University (SE), Université Paris Cité (FR), Heidelberg University (DE), National University of General San Martín (AR), Sorbonne Université (FR), Laboratoire de Physique Nucléaire et de Hautes Énergies (FR), International Center for Advanced Studies (AR), University of Cincinnati (US)
National Science Foundation, U.S. Department of Energy, Aspen Center for Physics, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Knut och Alice Wallenbergs Stiftelse, Centre National de la Recherche Scientifique, Sorbonne Université, Université de Paris, Office of Science, Institut National de Physique Nucléaire et de Physique des Particules, High Energy Physics
Openalex Percentile: Top 100%
Stability and Control of Uncertain Systems
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