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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,...

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Main Authors: Butter, Anja (Author) , Ore, Ayodele (Author) , Palacios Schweitzer, Sofia (Author) , Plehn, Tilman (Author) , Assi, Benoît (Author) , Bierlich, Christian (Author) , Ilten, Philip (Author) , Menzo, Tony (Author) , Mrenna, Stephen (Author) , Szewc, Manuel (Author) , Wilkinson, Michael K. (Author) , Youssef, Ahmed (Author) , Zupan, Jure (Author)
Format: Article (Journal)
Language:English
Published: 2026-02-12
In: SciPost physics
Year: 2026, Volume: 20, Issue: 2, Pages: 1-36
ISSN:2542-4653
DOI:10.21468/SciPostPhys.20.2.042
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.20.2.042
Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.20.2.042
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Author Notes:Anja Butter, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn, Benoît Assi, Christian Bierlich, Philip Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef and Jure Zupan
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Summary: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.
Item Description:Veröffentlicht: 12. Februar 2026
Gesehen am 13.04.2026
Physical Description:Online Resource
ISSN:2542-4653
DOI:10.21468/SciPostPhys.20.2.042

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