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Tracking Photon Conversions

Tracking Photon Conversions. 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?.

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Tracking Photon Conversions

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  1. Tracking Photon Conversions

  2. Existing Track Seeding • From pixels • Widely used, but not useful here • From stereo silicon layers • Uses layers 5 and 8 (barrel), not enough acceptance

  3. 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

  4. 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)

  5. 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

  6. Outside-In SeedingCurrent Results

  7. 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

  8. 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

  9. Inside-Out Results

  10. 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?

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