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Anja Butter - Unfolding unbinned differential cross section measurements

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If you have a question about this talk, please contact Rene Poncelet .

The seminar will take place via Zoom here.

Abstract: Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. In this talk I will discuss classifier and density based methods to obtain such unfolded data and illustrate a proposed scheme for presenting and using unbinned results.

This talk is part of the HEP phenomenology joint Cavendish-DAMTP seminar series.

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