Speaker
Malte Algren
(UNIGE)
Description
A calibration of the classifier using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the classification probabilities from tagging algorithms in simulation. After application of the derived calibration maps, closure between simulation and observation is achieved. A continuous calibration opens up new possibilities for the future use of jet flavor information in LHC analyses and furthermore serves as a guide for deriving high-dimensional corrections to simulation via transportation maps, an important development for a broad range of inference tasks.
| AI keywords | Optimal Transport; input-convex-neural-networks; foundation model |
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