1. Methods for fast reconstruction of events Ivan Kisel Kirchhoff-Institut für Physik, Uni-Heidelberg FutureDAQ Workshop, München March 25-26, 2004 KIP

2. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg2 CBM Trigger

3. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg3Algorithms Simple Local Parallel Fast HT - Hough Transform CA - Cellular Automaton EN - Elastic Net KF - Kalman Filter

4. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg4 CA – Game “Life” Each cell has 8 neighboring cells, 4 adjacent orthogonally, 4 adjacent diagonally. The rules are: Survivals. Every counter with 2 or 3 neighboring counters survives for the next generation. Deaths. Each counter with 4 or more neighbors dies from overpopulation. Every counter with 1 neighbor or none dies from isolation. Births. Each empty cell adjacent to exactly 3 neighbors is a birth cell. It is important to understand that all births and deaths occur simultaneously. Each cell has 8 neighboring cells, 4 adjacent orthogonally, 4 adjacent diagonally. The rules are: Survivals. Every counter with 2 or 3 neighboring counters survives for the next generation. Deaths. Each counter with 4 or more neighbors dies from overpopulation. Every counter with 1 neighbor or none dies from isolation. Births. Each empty cell adjacent to exactly 3 neighbors is a birth cell. It is important to understand that all births and deaths occur simultaneously. Sci. Amer., 223 (1970) 120 TRACKING ! NOISE ! TRACK ! no convergence ! RECO TRACK RECO TRACK RECO TRACK RECO TRACK RECO TRACK RECO TRACK GHOST TRACK ? GHOST TRACK ? moves moves

5. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg5 CA – Segment Tracking NIM A387 (1997) 433; NIM A489 (2002) 389; NIM A490 (2002) 546

6. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg6 BTeV L1 Vertex Trigger segment trackers (~500 FPGAs) Merge Trigger decision to Global Level 1 Switch: sort by crossing number track/vertex farm (~2500 DSPs) 30 station pixel detector Find beginning and ending segments of tracks from hit clusters in 3 adjacent stations (triplets):   beginning segments: required to originate from beam region   ending segments: required to project out of pixel detector volume FPGA Segment Finder (Pattern Recognition) Match beginning and ending segments found by FPGA segment finder to form complete tracks Reconstruct primary interaction vertices using complete tracks Find tracks that are “detached” from reconstructed primaries DSP Tracking and Vertexing >50% of total L1 processing time is taken up by the segment matching However Triplet FPGA Beauty-2002; BTeV Coll. Meeting, March 2003

7. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg7 LHCb L1 Vertex Trigger Z vtx histogram X,Y vtx 2d tracks in a 45 o sector: Triplet  time (ms)  Events 17 ms CPU (CA) 5 ms 15  s 130  s  Events  time (  s) FPGA (CA) LHCb Trigger TDR, CERN-LHCC-2003-031; LHCb Note 2003-064 Find VELO 2D tracks (~70) and reconstruct 3D primary vertex Reconstruct high-impact parameter tracks (~10%) in 3D Extrapolate to TT through small magnetic field -> PT Match tracks to L0 muon objects -> PT and PID Select B-events using impact parameter and PT information Use T1-3 data to improve further selection (5-10% of events)

8. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg8 CBM Trigger Algorithm M1 M2 S1 S2 S3 S4 S5 D J/  RICH TRD, ECAL

9. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg9 EN – Traveling Salesman Problem Continuous EN Discrete EN File Cities Extra path Time, sec(*) (*) Pentium II/100 MHz Nature, 326 (1987) 689; J. Comp. Meth. Sci. Eng., 2 (2002) 111

10. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg10 EN – Ring Search CHEP’01, Beijing (2001) 162

11. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg11 Kalman Filter –> Kalman Smoother Kalman Filter One Processing Unit Consecutively hit by hit Kalman Smoother Many Processing Units All hits in parallel NIM A489 (2002) 389; NIM A490 (2002) 546

12. 25-26 March 2004, MünchenIvan Kisel, KIP, Uni-Heidelberg12ConclusionSimpleLocalParallelFast Hough Transform Cellular Automaton Elastic Net Kalman Filter