Operating System for OR Machine
Questions to be answered:
- Trigger for simulated events
- J/Psi peak reconstruction
- Run a test with J/Psi, Drell-Yan, and random, try to reconstruct the peak.
- Parallelization of QTracker process
- How much is GPU based
- How much is CPU based
- 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