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

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

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      - Describe all configuration options for each portion

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      - 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

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