- 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
- Describe the integration of all parts together
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- 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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