sergey-serebryakov / particle-track-reconstruction

Using Machine Learning to reconstruct trajectories of particles detected as hits by ATLAS experiment at LHC, Cern.

Code for project on Particle Track Reconstruction - trackml dataset

The repository has code for project done under Dr. Kinjal Banerjee

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:

1. Classification of 2 hits as promising or not
2. Classification of a third promising hit
3. 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

Cite

@misc{chitlangia2021tracking,
  author = {Chitlangia, Sharad},
  title = {Particle Track Reconstruction using Machine Learning},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {Available at \url{https://github.com/Sharad24/Particle-Track-Reconstruction/}},
}