Speaker
Description
The use of first and higher order differentiation is essential for many parts of track reconstruction: either as part of the transport of track parameters through the detector, in several linearization applications, and for establishing the detector alignment. While in general those derivations are well known, they can be complex to derive and even more difficult to be validated. The latter is often done with numerical cross checking using a Ridder's algorithm or similar approaches. The vast development of machine learning application in the last years has also renewed interest in algorithmic differentiation techniques, that uses compiler or runtime techniques to compute exact derivates from function expressions, surpassing the precision achievable via standard numerical differerntiation based on finite differerences.
ACTS is a common track reconstruction toolkit that aims to preserve the tack reconstruction software from the LHC era and at the same time prepares a R&D testbed for further algorithm and technology research. We present the successful inclusion of the auto-diff library into the ACTS propagation and track based alignment modules that serves as a complimentary way to calculate transport jacobians and alignment derivatives: the implementation within the ACTS software is shown, and the validation and CPU time comparison with respect to the implemented analytical or numerically determined expressions are given.
