This IRIS-HEP Blueprint workshop brings together the HEP community to review the current use of automatic differentiation (AD) in analysis workflows, identify concrete use cases, and discuss future directions for its development and adoption. The scope extends beyond machine learning models to include full analysis pipelines, statistical inference, systematic uncertainty treatment, and experiment-scale software frameworks.
The agenda combines short overview talks with focused discussion sessions to examine the structure of differentiable analysis workflows, highlight conceptual and technical challenges, and identify areas where common abstractions, interfaces, and standards would be beneficial across experiments.
The outcome of the workshop will be a blueprint document summarizing the discussions and outlining near-term priorities and longer-term directions for automatic differentiation in HEP, with the aim of improving coordination between existing efforts and supporting realistic use in LHC analyses.
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This event is being organised by the Institute for Research and Innovation in Software (IRIS-HEP) with support from National Science Foundation Cooperative Agreement OAC-2323298. |
Conference information
