Tobias Kortus

(Chair for Scientific Computing (SciComp), RPTU University Kaiserslautern-Landau)
hosted by Seminar Series on Scientific Computing

"Exploring End-to-end Differentiable Neural Charged Particle Tracking – A Loss Landscape Perspective"

Charged particle tracking is a core component of event reconstruction in high-energy physics, as well as in related medical and industrial imaging applications. Conventional algorithms typically decompose the problem into separate stages, learning local scores or predictions first and performing the final track building in a subsequent step. This separation, however, risks optimizing intermediate objectives that are only weakly aligned with the ultimate reconstruction performance. These effects can be further amplified in modern component-based reconstruction and analysis pipelines, where non-monotonic error propagation and intricate interdependencies between processing stages give rise to unexpected downstream behavior. In such systems, improvements at the level of individual components do not necessarily translate into gains in overall end-to-end reconstruction performance. In this talk, we will present a systematic study of whether end-to-end differentiable reconstruction techniques provide concrete benefits over standard approaches where scoring and track building are separated. Leveraging graph neural networks coupled with differentiable combinatorial assignment operations, we empirically analyze the loss landscape and training dynamics of end-to-end models, with a focus on applications in proton computed tomography. The talk will highlight both the opportunities and the fundamental challenges of end-to-end optimization and discuss implications for the design of future differentiable tracking and reconstruction pipelines.

Time:	Thursday, 05.02.2026, 10:15
Place:	Hybrid (Room 32-349 and via Zoom)
Video:	https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

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