Difference between revisions of "Data Management"
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== Particle Trajectory Tracking with ML == | == Particle Trajectory Tracking with ML == | ||
| + | [[CNF:Machine_Learning | Machine Learning for Track Classification and Prediction]] | ||
[[CNF:ML_Data_Denoise | Machine Learning for Track Denoising]] | [[CNF:ML_Data_Denoise | Machine Learning for Track Denoising]] | ||
Revision as of 23:35, 3 November 2020
Contents
- 1 Next-generation imaging filters and mesh-based data representation for phase-space calculations in nuclear femtography
- 2 Telescopic Approach
- 3 Exascale-Era Finite Element Mesh Generation
- 4 Boundary Recovery
- 5 Isotropic Mesh Generation
- 6 Anisotropic Mesh Generation
- 7 Runtime Systems
- 8 Particle Trajectory Tracking with ML
Next-generation imaging filters and mesh-based data representation for phase-space calculations in nuclear femtography
- Tomographic pictures of the nucleon
Tomographic and recently aquired and tessellated pictures of the nucleon as a result of this project. Namely, the plots show a spatial distribution of up quarks as a function of proton's momentum fraction carried by those quarks. Specifically, bX and bY are the spatial coordinates (in 1/GeV = 0.197 fm) defined in a plane perpendicular to the nucleon’s motion, x is the fraction of proton’s momentum and color denotes probability density for finding a quark at given (bX, bY, x).
Plots produced by Dr. Gagik Gavalian and Dr. Pawel Sznajder and tesselated by CRTC's Image-to-Mesh (I2M) conversion software deployed to Jefferson Lab last month.
For more data and information about this project follow this link: CNF
Telescopic Approach
- The telescopic approach of CRTC lab
The project aims at investigating the design and implementation of multi-layered algorithmic and software framework for 3D tetrahedral parallel mesh generation. The framework is referred as the Telescopic Approach
Exascale-Era Finite Element Mesh Generation
Boundary Recovery
Isotropic Mesh Generation
Anisotropic Mesh Generation
Runtime Systems
Particle Trajectory Tracking with ML
Machine Learning for Track Classification and Prediction Machine Learning for Track Denoising
