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Presentation of new Kraków Group

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1 Presentation of new Kraków Group
Krzysztof Korcyl (Cracow University of Technology, IFJ - PAN)

2 The group 4 (6) persons (lecturers at the Cracow University of Technology Institute of Teleinformatics Faculty of Physics, Mathematics and Informatics) two have phd in informatics two have phd in physics ( + habilitation + 1 prof.) good chance to get students (masters only – for small well defined projects – the CUT does not have phd studies)

3 The expertise Recent interests and expertise of the group members:
Image processing - algorithms (medical images) technologies (GPU with CUDA) Parallel processing – OpenMP, MPI large scale architectures Real time processing – modeling real time systems, architectures Large scale configuration management (co-author of CERN Quattor) Software engineering and project management (Agile/SCRUM) (experience in business (Motorola) and scientific (CERN) projects)

4 Possible areas of collaboration
Polish contribution to the FAIR project (the group can count on some financial support): in direct cooperation with the Jagiellonian University: Forward Tracker – real time track recognition – studies on algorithms with different technologies (FPGA, GPUs) related to activities in AGH (FEE for STT) central tracker - STT - real time track recognition same FEE as for the Forward Tracker fast sampling ADC (in place of TDC with TOT) Not related to the polish FAIR (i.e. no financial suport – hobby): modeling of the real time DAQT system for PANDA

5 Tracks Generation and Reconstruction

6 Track Generator Simple Track Generator for any STT Geometry Features:
(For the moment) based on math equation of circle-arcs and straight-lines Adding noise (radius of drift circles, bogus ST hits) To do: Tracks generation based on physics

7 Track Reconstructor Prototype – proof of concept, architected with:
good parallelisation properties, computational stability in mind, towards RT Reconstruction of circle-arcs and straight-lines

8 Track Reconstructor - Aproach
hybrid graph/histogramming for better accuracy when introducing noise (still good parallelisation and concurency) - for some class of events one method will be OK (room for speedup) STT graph used for noise reduction / calcualtion speedup calculation in legendre space after conformal transformations (for arcs) geometrical pair-wise tangent lines fitting (pairs determination may impact accuracy) graph representation of whole STT (actually we can generate graphs for any STT geometry)

9 Track Reconstruction – To Do
Parallelisation (GPU, MPP CPU), Graph vs Histogram tunning, ST hits pairs determination tunning Evaluation (and adding?) Kalman/Wiener filters Adding physics (circle arcs vs spirals) Accuracy evaluation and tunning other geometrical fitting (analitical solution to tangent non-pair-wise problem?)


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