Speaker
Description
The design choices made for particle physics detectors can have long-lasting impacts on the scientific outcome of a given experiment. Optimizing detector layouts is therefore essential in the planning stages of an experiment. However, conventional brute-force Monte Carlo–based studies often become computationally prohibitive when exploring large design parameter spaces. Differentiable programming offers a solution by enabling calculation of gradients of scientific performance metrics with respect to detector design parameters, allowing efficient gradient-based optimization.
In this contribution, we present a fully differentiable end-to-end optimization pipeline for in-ice radio neutrino detectors targeting ultra-high-energy neutrino observations. The framework combines differentiable PyTorch implementations of radio signal generation, propagation, detection, and reconstruction using machine-learning-based surrogate models and uncertainty estimation through the Fisher information. This enables direct optimization of detector performance metrics with respect to antenna positions and orientations. We show proof-of-concept studies demonstrating end-to-end detector optimizations aimed at improving reconstruction precision for the IceCube-Gen2 radio array.