The MODE introductory article*, published earlier this year proposed an end-to-end differential pipeline for the optimisation of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete endeavour in attempting to realise such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography with respect to both imaging performance and detector budget. This modular and customisable package is capable of simulating detectors which scan unknown volumes by muon radiography, using cosmic ray muons to infer the density of the material. The full simulation and reconstruction chain is made differentiable and an objective function including the goal of the apparatus as well as its cost and other factors can be specified. The derivatives of such a loss function can be back-propagated to each parameter of the detectors, which can be updated via gradient descent until an optimal configuration is reached.

*MODE (2021) Toward Machine Learning Optimization of Experimental Design, Nuclear Physics News, 31:1, 25-28, DOI: 10.1080/10619127.2021.1881364

How to join

The talk is held online via Zoom. You can join with the following link:
https://uni-kl-de.zoom.us/j/61178293734?pwd=Z0lwMXd4TTZZZ1ZSSWhjcHdUWGN4dz09

Referent: Dr. Giles Strong, CERN, University of Padova

Zeit: 12:00 Uhr

Ort: online