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SUMMARY:Anja Butter - Unfolding unbinned differential cross section measur
 ements - Anja Butter (Heidelberg)
DTSTART:20220603T150000Z
DTEND:20220603T160000Z
UID:TALK173930@talks.cam.ac.uk
CONTACT:Rene Poncelet
DESCRIPTION:*The seminar will take place via Zoom "here":https://maths-cam
 -ac-uk.zoom.us/j/95215212460?pwd=TWN5cjJ3azErSUYremw5UWRKL0NKUT09.*\n\nAbs
 tract: Machine learning tools have empowered a qualitatively new way to pe
 rform differential cross section measurements whereby the data are unbinne
 d\, possibly in many dimensions. Unbinned measurements can enable\, improv
 e\, or at least simplify comparisons between experiments and with theoreti
 cal predictions. Furthermore\, many-dimensional measurements can be used t
 o define observables after the measurement instead of before. There is cur
 rently no community standard for publishing unbinned data. While there are
  also essentially no measurements of this type public\, unbinned measureme
 nts 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 us
 ing unbinned results.
LOCATION:Potter Room (B1.19)
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