Erlangen 2026 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 34: Data, AI, Computing, Electronics IV
T 34.4: Vortrag
Dienstag, 17. März 2026, 17:00–17:15, KH 02.014
Differentiable Setup for a Top-Higgs Analysis — •Felix Zinn1, Peter Fackeldey2, Benjamin Fischer1, Nina Herfort1, and Martin Erdmann1 — 1RWTH Aachen University — 2Princeton University
In high energy physics (HEP), the measurement of physical quantities often involves intricate data analysis workflows that include the application of kinematic cuts, event categorization, machine learning techniques, and data binning, followed by the setup of a statistical model. Each step in this process requires careful selection of parameters to optimize the outcome for statistical interpretation.
This presentation introduces a differentiable approach to the data analysis workflow utilizing the python package evermore for statistical model building. Built on top of JAX, the models created in evermore benefit from automatic differentiation. By leveraging this feature alongside neural networks, we can apply optimization across all stages of the analysis. This method allows for a more systematic selection of parameter values while also ensuring that the optimization process accounts for systematic uncertainties included in the analysis.
We apply this approach to a CMS analysis targeting the production of a Higgs boson in association with one or two top quarks and demonstrate how each individual step can be implemented in a differentiable manner. A setup for a differentiable analysis workflow is presented.
Keywords: jax; evermore; statistics; higgs