In order to face the new PU challengs for Run II, two new algorithms has been implemented: deterministic annealing filter for tracking reconstruction and cellular automata for tracking seeding. Preliminary results are shown below.
The track parameters estimation by error minimization is made through a fit method. The Deterministic Annealing Filter is a soft assignment fit method, where several competing hits can contribute to the same track with different weights.
The implemented algorithm is basically an iterated Kalman Filter, where the wrong hit assignment to the track is avoided introducing an annealing factor which changes iteration by iteration.
Plot  Description 
Final distribution of weights for competing hits selected by the DAF algorithm. The distribution is shown for different samples: single muon (red), ttbar(green) and QCD jets(blue). Structures are visible around the values 0.3 and 0.5 due to the annealing scheduled chosen (30.0, 18.0, 14.0, 11.0, 6.0, 4.0, 2.0, 1.0).  
Final distribution of weights for competing hits selected by the DAF algorithm. The distribution is shown for different samples: single muon (red), ttbar(green) and QCD jets(blue). The properly tuned annealing schedule (15.0, 10.0, 6.0, 4.0, 2.0, 1.0, 1.0, 0.5, 0.1) shows a smooth distribution.  
The DAF algorithm uses the competing hits by including them in a single virtual hit, called MultiRecHit (MRH). This plot shows the distribution of the number of competing hits inside a MultiRecHit for different samples: single muon (red), ttbar(green) and QCD jets (blue). 
In a Cellular Automaton (CA), a network of cells evolves in discrete time steps from an initial state according to predefined rules, depending only on the values of the cells in the local neighborhood.
In the implementation of this algorithm in the tracking seeding context, the following consepts have been defined:
* cell: as a segment linking three hits in different layers (different configuration are chosen for the three pixel layer and the two innermost ones in the strip detector).
* neighborhood: two cells are considered neighborhood if they have in common pair of hits and similar eta.
* evolution rule: at each time step a cell increases its state if on its left it has a neighbor with the same state.
At the end of the network evolution, the neighbor fit triples are joint in a longer seed.
Plot  Description 
Full tracking efficiency as a function of the pseudorapidity for simulated ttbar + 

CPU time per event for the first iteration using the standard seeding (blue) and the Cellular Automaton one (red) for a simulated ttbar + 
 EricaBrondolin  20150203
I  Attachment  History  Action  Size  Date  Who  Comment 

png  CA_effPlotAppr.png  r1  manage  151.4 K  20150204  16:19  EricaBrondolin  
png  CA_timePlotAppr.png  r1  manage  35.5 K  20150204  16:16  EricaBrondolin  
png  nHitsinMRH_NewAnnCycle.png  r1  manage  9.4 K  20150203  10:58  EricaBrondolin  
png  weigtsOfMultiMRH_NewAnnCycle.png  r1  manage  12.6 K  20150203  10:58  EricaBrondolin  
png  weigtsOfMultiMRH_OldAnnCycle.png  r1  manage  12.0 K  20150203  10:58  EricaBrondolin 