The AutoEncoder compress the four-momentum of a sample of simulated particles from 4 to 3 variables.
Here, you can find the codes for implementation of autoencoder-decoder network to compress the data from the ATLAS experiments from 4 to 3 variables, and reconstruct the same.
The ATLAS experiments being conducted at the Large Hadron Collider at CERN produce millions of proton-proton collision events. Here, I analyse the variables from the 4-momentum from the leading jets.
- pt: transverse momentum pT
- eta: pseudorapidity η
- phi: azimuthal angle φ
- eta: energy E
Python 3.5
- numpy
- pandas
- matplotlib
- sys
- torch
- fastai == 1.0.61
- scipy
- seaborn
- corner
The Colab Notebook consist of the following modules:
- Loading the dataset.
The analysis has been done by preprocessing the data in two ways and comparing the results by auto-ecoding the two sets of preprocessed data. Preprocessing of the data:
- Normalisation of data: Subtract the mean of the training set and divide by standard deviation of the training set.
- Singlular Value Decomposition(SVD): Mathematical technique used for dimensionality reduction. We will add the singular values to original dataframe and then pass it through the deep autoencoder network.
- Utils.py : This file contains basic script used for preprocessing of data like removing delimiters, normalization and svd
- Histogram to visulaise the data distribution.
- Train the Model. (NOTE: You can just use the pretrained model by loading it) -Model
- Reconstruction of the data using the auto encoder-decoder model.
The plots for both the reconstructed normalised as well as custom standardised data are shown. - Reconstruction Loss (Residual).
Residual = (Predicted Data - Original Data) / Original Data
Download the datasets and store them in a folder processed_data:
The various plots can be visualised in the Jupyter Notebook