Iterative HOMER with uncertainties
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908867608182784 |
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| author | Butter, Anja Ore, Ayodele Schweitzer, Sofia Palacios Plehn, Tilman Assi, Benoît Bierlich, Christian Ilten, Philip Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure |
| author_facet | Butter, Anja Ore, Ayodele Schweitzer, Sofia Palacios Plehn, Tilman Assi, Benoît Bierlich, Christian Ilten, Philip Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative HOMER with uncertainties Butter, Anja Ore, Ayodele Schweitzer, Sofia Palacios Plehn, Tilman Assi, Benoît Bierlich, Christian Ilten, Philip Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure High Energy Physics - Phenomenology High Energy Physics - Experiment 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. |
| title | Iterative HOMER with uncertainties |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.03592 |