Year: 2026
Type: article
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.
Source: SciPost Physics
Institutions: Centre National de la Recherche Scientifique, Université Paris Cité, Heidelberg University, Sorbonne Université, Laboratoire de Physique Nucléaire et de Hautes Énergies, University of Cincinnati, Lund University, Fermi National Accelerator Laboratory, National University of General San Martín, International Center for Advanced Studies
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
Funders: 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
Awards: G1620761618, G1741598991, G196419191, TRR 257: Phänomenologische Elementarteilchenphysik nach der HiggsEntdeckung, G2304628665, G2595738051, G2645866196, G2805343336, Collaborative Research: CyberTraining: Pilot: Monte Carlo General Education Network (MCGEN), G3523651922, Elements: Automated parametric transformations for rapid particle physics simulations, G3883943580, G4404904460, Elements: Machine Learning Quark Hadronization, G498139845, G5616713406, G585908603, G5861921277, G6024799018, Discovering True Muonium, G638484911, G6526081583, G6649312515, EXC 2181: STRUKTUREN: Emergenz in Natur, Mathematik und komplexen Daten, G716412294, G7291426837, Programs on Critical Problems in Physics, Astrophysics and Biophysics at the Aspen Center for Physics, G7974404656, G8233100948, G86560745, G969889393
OpenAlex ID: W4414566707