• Introduction
    • -Overview and why Q-tracker is needed
    • -Goals
    • -How we will achieve these goals
    • -outline of what's to come (order of how things are broken down)

  • Training Data production
    • - MC production (details on quality/matching), event simulation quality/matching, trigger dependence, detector resolution dependence
    • - Quantifying variational equivalence in hit matrix (hit matrix covariance) and error propagation
    • - Detector efficiency map as a function of time/run (bad DC or hodo regions)
    • - Detector Covariance
    • - Energy loss and momentum corrections (difference needed for data to MC comparison)
    • - Change in momentum accuracy and precision as a function of change in covariance
  • Limitations in the MC (reducing knowns and noting unknowns)
    • - quantifying the difference between ELOSS from MC and ELOSS from Experiment
    • - detailed field map and alignment in MC used
    • - J/psi peak position and width as a function of field configuration, resolution, eloss, momentum corrections
  • Experimental data matching
    • - biases in positive and negative tracks and/or geometry
    • - Proof that deviations do not lead to bias or larger systematic errors
    • - Quantified robustness of accuracy and precision of 4-momenta, vertex position, and J/psi and psi' peak
    • - Assumptions about distributions of processes
    • - True occupancy limits
    • - Ensuring correct drift time implementation and impact
  • Q-tracker Algorithm

    • - Overview with choice of DNN type for each part and why

    • - Describe each portion's architecture, and what it does

    • - Describe all configuration options for each portion

    • - Describe the integration of all parts together

  • Training
    • - Training process and philosophy for best results
    • - Training evolution based on trial and error (lessons learned)
    • - Folding in degrees of noise in phases of training and testing at each level
    • - Validation studies with 906 data in training and in predictions
  • Performance and Quality checks
    • - Best px,py,pz all being better or equal to Ktracker
    • - Best vx, vy, vz, and studies on improved iterative reconstruction
    • - Fiducial cuts
    • - Fiducial settings (requirements for optimal performance)
    • - Integrating timing cuts
    • - basic analysis using J/psi+psi/ width measurements after corrections to the spectrum imposed
    • - Probability cut variation
    • - Systematic variation of all Qtracker configuration settings
    • - Detector effects not taken into account
    • - Reliability of incomplete tracks and threshold conditions for cut-off
    • - Combinatoric analysis after final quality and vertex cuts
    • - Analysis of remaining dump and non-target produced tracks
    • - Analysis of acceptance and occupancy dependence on beam intensity (rate effects)
    • - Final comprehensive quantitative comparison to Ktracker
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