Speaker
Description
Over the last years, a general purpose track finding algorithm based on the combinatorial Kalman filter (CKF) has been developed for the Acts toolkit - a community-driven project that provides experiment-independent tracking algorithms written in modern C++. It has been validated and optimized with the OpenDataDetector (ODD), and the ATLAS Phase-2 Inner Tracker (ITk). The CKF shows good performance for muons and pions but is inefficienct for electrons due to bremstrahlung. Acts also provides a matured implementation of a Gaussian Sum Filter (GSF) to cope with the non-gaussian energy loss during track fitting.
In this contribution we present efforts to tackle the specific challenges of electron reconstruction in Acts. For track finding, we present an algorithm that uses the CKF mechanism to discover new measurements, but leverages components of the GSF to adapt to the brehmsstrahlung energy loss. For the electron re-fitting, we present the exploration of an ML-based regression as a potential replacement of the computationally expensive multi-component fit by the GSF.
The new algorithms will be compared to the currently available, matured algorithms in ACTS and validated with the ODD using reference physics samples.