A fully differentiable neutrino oscillation engine computes exact gradients through layered-Earth geometry and downstream analysis, validated against external codes and finite differences.
TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github.
fields
hep-ex 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MANGO: An Autodiff Neutrino Oscillation Engine for Differentiable Analysis Pipelines
A fully differentiable neutrino oscillation engine computes exact gradients through layered-Earth geometry and downstream analysis, validated against external codes and finite differences.