addressing-hadron-collider-challenges-with-ML

This project consists for the following stages:

1- Detecting Z Boson

The objective of this stage is to detect the appearance of the particle Z boson, determine its mass and reconstruct the parameters of its decay.

This stages includes:

1. Problem describtion
2. Computing the invariant mass
3. Detecting the existance of Z boson
4. Using Bayesian optimization to determine the parameters of Z boson decay

2- Identifying the Particles Types Created in LHC

The aim of this stage is to identify the type of particles created by the collisions of the accelorated protons in LHC. We use the responses of various detector system to classify the particles into six particle types (Electron, Proton, Muon, Pion, Kaon, Ghost or noise)

This stages includes:

1. Problem description
2. Exploratory data analysis
3. Data processing
4. Training a classifier
5. Training a classifier-adversary
6. Evaluation

3- Searching for the Dark Matter in LHC

The aim of this stage is to assist the search of the Dark Matter in LHC. This can be done by building a classifier that can discriminate the basetracks beloning to the electromagnetic showers from the background basetracks.

This stage includes:

1. Problem description
2. EDA
3. Data preprocessing
4. Feature engineering
5. Modeling
6. Model Evaluation

4- Bayesian Optimization of LHC detector

We aim in this project to use machine learning methods to optimize the LHC detector structure in a way that maximizes its ability to detect the charactristics of the particles that fly through it.

This stage includes:

1. Problem description
2. Detector simulation
3. Random optimization of the detector
4. Bayesian optimization of the detector
5. Comparing results