IRIS-HEP Fellow: Cody Tanner



Fellowship dates: Jul – Sep, 2025

Home Institution: University of Washington


Project: Differentiable Modeling of Systematic Uncertainties in ATLAS Object Corrections

Modern ATLAS analyses depend on object corrections that are currently implemented through non-differentiable procedures like histogram lookups and conditional logic, limiting their integration into gradient-based pipelines. This project proposes a neural network model that replicates ATLAS object corrections, including systematic uncertainties, for small-R jets in a differentiable and computationally efficient form. Starting from an existing baseline trained on the JZ2 dataset, the model will be refined through architectural tuning, loss reweighting, and incorporation of per-object uncertainties to approach sub-percent residuals in jet kinematics. A final case study will use the model to reconstruct Z→jj peaks, evaluating the physics impact of improved corrections and uncertainty modeling. This work provides a foundation for embedding fast, uncertainty-aware corrections directly into end-to-end ATLAS workflows.



More information: My project proposal

Mentors:
  • Gordon Watts (University of Washington)

Presentations and Publications
  • - "", Cody Tanner, Recording:

Current Status
A placeholder for status updates

Contact me: