Speaker
Description
Muon scattering tomography is a well-established, non-invasive imaging technique using cosmic-ray muons.
Simple algorithms, such as PoCA (Point of Closest Approach), are often utilized to reconstruct the volume of interest from the observed muon tracks.
However, it is preferable to apply more advanced reconstruction algorithms to efficiently use the sparse statistics available.
One approach is to formulate the reconstruction task as a likelihood-based problem, where the material properties of the reconstruction volume are treated as an optimization parameter.
In this contribution, we present a reconstruction method based on directly maximizing the underlying likelihood using automatic differentiation within the PyTorch framework.
We will introduce the general idea of this approach, and evaluate its advantages over conventional reconstruction methods.
Furthermore, first reconstruction results for different scenarios will be presented, and the potential that this approach inherently provides will be discussed.
