1: Algorithms and theoretical analysis
Mathematical evaluation of pattern recognition problems, fitting tracks beyond classical Kalman filters, effect of noise, etc.
2: Real-time pattern recognition and fast tracking
Software and firmware implementation for parallel and discrete pattern recognition, e.g. Hough transform approaches, look-up tables, associative memory, etc. Timing performance for fast tracking or on-line trigger system.
3: Machine learning approaches
Novel tracking concepts, software and firmware implementations, exploration of neuromorphic hardware
4: Performance evaluation
Examples of implemented pattern recognition problems and solutions with emphasis on new challenges and limits of scaling existing approaches
5: Advanced usage of tracks
Advanced algorithms to build high level information from tracks, e.g. conversion, vertexing, jets, tau and flavor tagging
6: Beyond the conventional tracking
4D tracking using precision timing information, software techniques for novel detector concepts and interdiciplinary developments in the field of data science, e.g. neuro inspired computing, Brain activity, connectivity