This project contains an experiment-independent set of track reconstruction tools. The main philosophy is to provide high-level track reconstruction modules that can be used for any tracking detector. The description of the tracking detector’s geometry is optimized for efficient navigation and quick extrapolation of tracks. Converters for several common geometry description languages exist. Having a highly performant, yet largely customizable implementation of track reconstruction algorithms was a primary objective for the design of this toolset. Additionally, the applicability to real-life HEP experiments plays major role in the development process. Apart from algorithmic code, this project also provides an event data model for the description of track parameters and measurements.
Key features of this project include: tracking geometry description which can be constructed from TGeo, DD4Hep, or GDML input, simple and efficient event data model, performant and highly flexible algorithms for track propagation and fitting, basic seed finding algorithms.
See ACTS webpage for further details.
We held a tracking workshop for HEP in Berkeley in January 2019.
- ACTS Status (Xiaocong Ai, 08 Aug 2019) at USATLAS Summer Workshop
- ACTS: a common track reconstruction software (Xiaocong Ai, 31 Jul 2019) at DPF 2019
- Ambiguity Resolution: Using Machine Learning (Nicholas Cinko, 31 Jul 2019) at ACTS Developers Meetings
- IRIS-HEP Innovative Algorithms (Heather Gray, 06 Feb 2019) at IRIS-HEP Steering Board Meeting
- Ambiguity Resolution Studies with ACTS (Nicholas Cinko, 13 Jan 2019) at Berkeley Tracking Workshop
- Workshop introduction (Heather Gray, 13 Jan 2019) at Berkeley Tracking Workshop
- Ambiguity Resolution Studies with ACTS (Heather Gray, 13 Jan 2019) at Berkeley Tracking Workshop