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Optimizing Stereo Hit Finding and Straw Clustering in MU2E: Development Insights

This report details the current advancements in the stereo hit finding and straw clustering code implemented by Hans Wenzel. Stereo hits are identified as initial seeds for trajectory fitting, enhancing the efficiency of detecting these hits is critical. The method clusters neighboring straws based on signal thresholds, aiming to improve the rate of detected hits. Future plans include factoring in noise hits to evaluate false positive rates. Despite current low efficiency, these developments mark a significant step towards refining stereo hit identification as well as clustering methods.

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Optimizing Stereo Hit Finding and Straw Clustering in MU2E: Development Insights

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  1. Testing the T-tracker Straw Clustering Code and Stereo Hit Finding Clustering Code Recently implemented by Hans Wenzel (MakeStrawCluster_pluggin.cc) StrawClusters are now a MU2E class Stereo Hits are NOT a MU2E class. Code currently in development (see ReadStrawCluster_pluggin.cc) . Why is this important ? Our approach: Stereo Hits are starting seeds to send to a helical trajectory fitter. We need to know the efficiency for finding stereo hits, among other things. Stereo Hits are formed by clusters (more later). In the future, we will add (correlated or random) noise straw hits to access the rate of false stereo hits. Hans Wenzel, Hogan Nguyen, Jan 19th, 2011

  2. Straw Clustering Code Implemented by Hans Wenzel. Clustering finds clumps of neighboring straws (above threshold) within a Panel

  3. Signal Electrons Generated Cos (Polar Angle) = 0.5 NO NOISE HITS Number of Straws (above threshold) per Cluster The number of isolated straw hits (n=1) = 25%

  4. 3 mm distance between wires For normal trajectories, rate of singlets = 1 mm/ 3 mm = 33% We find 25% rate of singlets (independent of hit threshold). Trajectory generated is 30 degreesfrom normal. Higher efficiency probably due to non-normal track angles. 0.5 mm 0.5 mm

  5. Later should generalize Stereo Hit finding to within ONE STATION (= 2 devices) Currently, finding Stereo Hits ONLY WITHIN ONE Device Device Station = 2 devices back-to-back

  6. Number of Clusters Found PerDevice (0-35) at least 2 are required to form a “Stereo Hit” Rate is too low

  7. Number of Stereo Hits Found PerDevice(0-35) Rate is too low

  8. Number of STEREO Hits Found Per Event at least 3 needed to fit to a circle (48% efficiency)

  9. Clustering and Stereo Hit finding seems to work • This strategy yields too low efficiency for finding Stereo Hits. But it • is a good start. • Stereo hit finding should extend to a station (rather than a device) • Can access later if stereo hit finding should include three-cluster • intersections (currently only 2 are used)

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