Speakers
Description
We present GRAEP (Gradient-based End-to-End Physics Analysis), a JAX-based framework for building modular, end-to-end differentiable analysis pipelines in high-energy physics. The framework integrates tooling from the Scikit-HEP ecosystem and enables gradient-based optimisation across HEP analysis workflows. We demonstrate an end-to-end differentiable analysis applied to CMS Open Data, covering event selection, observable construction, differentiable histogramming, and likelihood-based inference in a signal extraction setup. The example reflects a realistic CMS-like analysis setup, using structured analysis code and binned statistical models rather than simplified toy problems. We discuss the treatment of non-differentiable operations commonly encountered in HEP analyses, comparing different strategies for making discrete operations differentiable, including continuous relaxations and probabilistic formulations, along with their trade-offs in terms of stability, faithfulness, and computational cost. We also consider the treatment of systematic uncertainties in the workflow, including both their incorporation in the statistical model and uncertainties arising from the gradient-based optimisation procedure. This work provides a concrete reference for end-to-end differentiable analyses in HEP and illustrates how gradient-based methods can complement traditional analysis workflows.