IRIS-HEP Fellow: Zhe Wang

Fellowship dates: Jun – Sep, 2022

Home Institution: University of Wisconsin-Madison

Project: Implement improved morphing strategy into MadMiner

MadMiner is a Machine Learning-based inference tool that uses simulated events that can be re-weighted to describe distributions with different values for the physics parameters of interest. Currently, the available morphing technique in Madminer requires an inflexible distinct number of default physics parameter values (basis points) needed to use (the number varies depending on the physics process of interest). Thus, we propose to implement a new approach that relaxes the requirement which would allow researchers to pick additional physics parameter values as basis points while still being able to reweight to any other position in parameter space.

More information: My project proposal

  • Kyle Cranmer (University of Wisconsin - Madison)

  • Alexander Held (University of Wisconsin - Madison)

Presentations and Publications
Current Status

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