This is all sharad's work, I have just used his repo. and parts of it to improve my own implemenations. Current Progress:
- Initial data exploration
- Clustering
- Neural Network - FC: 86%
- Random Forest: 93%
- Gradient Boosted Classifiers: 96%
- XGBoost Classifier, 500 trees and (max_depth = 25), Trained on 1 event: 98.1%
- Exploration of different Neural Network architectures
Particle Physics and Quantum Mechanics:
- Chapter 1 Griffiths
- Chapter 2 Griffiths
- Introductory Quantum Mechanics
Current Approach:
- Classification of 2 hits as promising or not
- Classification of a third promising hit
- Reconstruction of the trajectory based on the three hits classified as promising
- The current models are trained 1st step(i.e., classification of 2 hits as promising or not), since the same model can be extended in the second step
- In the final step, the hits that are closest to the reconstructed trajectory will be selected