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Explore the Inside-Out and Outside-In tracking approaches for particle conversions, focusing on optimizing trajectory building methods with Calo clusters. Implementing a combination of methods for efficient track seeding in particle conversion analysis. Investigate ways to improve efficiency and track reconstruction in early and later conversions, utilizing stereo silicon layers and advanced algorithms. Evaluate strategies for rejecting p0s, energy matching, and vertex constraints in multi-track scenarios.
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Existing Track Seeding • From pixels • Widely used, but not useful here • From stereo silicon layers • Uses layers 5 and 8 (barrel), not enough acceptance
First Question:Inside-Out or Outside-In? Supercluster loses particles: more likely for early conversions, asymmetric, bremming. Both go into same supercluster Might be cleaner to work inside-out in a narrow f-window Need to work outside-in Maybe look for 2nd track inside-out
Outside-In:Finding the starting trajectory state • Do separately for each basic cluster • Or should I only do once for each supercluster? • Assume the conversion happened 1/3rd of the way in the tracker • Hand-wavingly Bayesian, since we can’t track the outer conversions • Assume the track had the full energy of the cluster • Calculate where in f to start the track • From the formula for the intersection of two circles • Propagate this trajectory to outer layers and look for consistent hits (Df < 0.015, Dz consistent with IP spread)
Outside-InCompleting the Seed • Next, create a trajectory state from the calo cluster position, the hit position, andthe cluster energy. • Propagate inwards and look for second hits in the seed (still optimizing f and z windows) • If a second point is found, create a new trajectory from a helix of the two tracker points and the calo cluster point. • Use the Kalman Filter Updator to add the points to the trajectory, to get combined errors correct. • Send seeds off to Kalman track reconstructor. • Demand four-hit tracks, hit c2<5, one lost hit
Inside-Out Seeding • Look for first hits within a narrow f window (0.006) along the supercluster centroid • Once a first hit is found, look for 2nd hits on the next two layers, assuming the track starts at the first point, and has a curvature of half the supercluster energy • If a second point is found, make a seed using the curvature that was found, assuming the track was going radially at the first hit. • Try to find tracks • If only one track is found, try looser cuts to find a second seed with the same vertex
Inside-Out Seeding:Two-Way Trajectory Building? Brem showers degrade the measurement Of the calo cluster f What if the cluster f is mismeasured, so I miss the first few points? Tried to make a TrajectoryBuilder that works In both directions, but isn’t quite working yet
Next Steps • Not nearly enough efficiency from either algorithm. Keep refining/debugging the algorithms • Find a final combined algorithm: • Different algorithm depending on calo cluster shape? • Implement an outside-in for the first track, then inside-out for the second • Use stereo silicon seeding for early conversions, inside-out for later ones • How to reject p0s? Track h-matching? Can I get two tracks and get an energy match constraint, or a vertex constraint?
