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:
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Gordon Watts (University of Washington)
- - "", Cody Tanner, Recording:
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