Speaker
Description
For the tracker systems used in experiments like the large LHC experiments, a track based alignment with offline software is performed. The standard approach involves minimising the residuals between the measured and track-predicted hits using the $\chi^2$ method. However, this minimisation process involves solving a complex and computationally expensive linearised matrix equation. A new approach utilising modern Machine Learning frameworks such as TensorFlow and/or PyTorch is being studied. In this study, the problem is addressed by leveraging these frameworks' implemented stochastic gradient descent and backpropagation algorithms to minimise the $\chi^2$ as the cost function. A proof-of-principle example with a generic detector setup is presented.
