Welcome to Hm.JetscapeMl! This repository contains code and resources related to utilizing machine learning techniques for analyzing Jetscape simulation data.
Hm.JetscapeMl is designed to extract valuable insights and patterns from Jetscape simulation data using modern machine learning techniques. The dataset and accompanying scripts provide a comprehensive framework for conducting machine learning experiments on this data.
ML-JET, a dataset for parameter classification in heavy ion collisions using jet images. The dataset is hosted on Kaggle: ML-Jet Dataset (https://www.kaggle.com/datasets/haydarmehryar/ml-jet).
The JET-ML dataset is designed as a comprehensive benchmark for machine learning applications in the field of relativistic heavy ion collisions. This dataset facilitates the study and prediction of energy loss mechanisms in high-energy particle physics, specifically focusing on parameters like initial parton virtuality and strong coupling constant, denoted as
Purpose and Scope
The primary aim of the JET-ML dataset is to support the development and evaluation of machine learning models for high energy physics that can classify and predict jet event parameters under different physical conditions in a quark-gluon plasma (QGP). It provides a rich collection of simulated jet images, which are pivotal in understanding the dynamics of parton energy loss in such environments. The dataset emphasizes the connection between energy loss and quantum chromodynamics (QCD) parameters,
Data Generation and Features
The dataset was generated using the JETSCAPE framework (https://jetscape.org/), a sophisticated tool for simulating jet events in high-energy collisions.
Dataset Composition and Labeling
The JET-ML dataset comprises 10.8 million images, each with a resolution of 32 x 32pixels, representing Pb-Pb collision events. The jet observables used in our dataset building process are: (a)
- The
$x$ axis represents$\eta$ , which is the pseudorapidity, and is in range of$[-\pi,\pi]$ , - The
$y$ axis represents$\phi$ , which is the azimuthal angle, and is in range of$[-\pi,\pi]$ , - The
$z$ axis represents$\Sigma p_t$ , which is the summation of$p_t$ values in each specific mesh cell.
Each image is labeled with its corresponding energy loss module (MATTER or MATTER-LBT), the strong coupling constant
Point cloud representation of a sample event is demostrated in image below:
-
Configurations (01 to 09): Nine distinct configurations corresponding to different combinations of physical parameters.
-
Strong Coupling Constant (
$\alpha_s$ ): The simulations include$\alpha_s$ values of 0.2, 0.3, and 0.4. -
Virtuality Separation Scale (
$Q_0$ ): The dataset includes$Q_0$ values of 1, 1.5, 2.0, and 2.5. -
Energy Loss Modules:
- MATTER: Handles the initial parton showering and energy loss.
- MATTER-LBT: Incorporates medium-induced scattering and gluon radiation at lower virtualities.
-
Dataset Size: varies indeffrent files. They contain 10.8 million, 1 million, 100k, 10k, 1k images of 32x32 pixel resolution.
-
Dataset Format:
DataColumn(name="dataset_x", description="32x32 pixel jet images.", data_type="image", shape=(32, 32)),
DataColumn(name="dataset_y", description="Associated labels including energy loss module, alpha_s, and Q_0.", data_type="numeric", shape=(3,)),
Intended Use and Applications
This dataset is intended for researchers and practitioners in both machine learning and high-energy physics. It provides a robust platform for developing models that can classify or predict event parameters in particle collisions, aiding in the deeper understanding of QGP properties and behavior. Possible applications include:
- Training deep learning models for medium parameter classification.
- Evaluating the impact of different
$Q_0$ and$\alpha_s$ values on parton energy loss. - Benchmarking novel machine learning algorithms in the context of high-energy physics.
Compliance with FAIR Standards
The JET-ML dataset adheres to the principles of FAIR (Findable, Accessible, Interoperable, Reusable) data. It is publicly available through platforms like Kaggle (https://www.kaggle.com/datasets/haydarmehryar/ml-jet) and GitHub (https://github.com/hmehryar/Hm.JetscapeMl), with comprehensive documentation and metadata provided to facilitate its use and integration into various research workflows.
To get started with Hm.JetscapeMl, follow these steps:
- Clone this repository to your local machine:
git clone https://github.com/hmehryar/Hm.JetscapeMl.git- Navigate to the repository's directory:
cd Hm.JetscapeMl- Reading the Dataset: To read and utilize the dataset, users can employ various tools and libraries compatible with the pickle format. Here’s how the dataset can be accessed using Python with the
picklelibrary:
import pickle
dataset_file_name = f"ml_jet_dataset.pkl"
try:
with open(file_name, 'rb') as dataset_file:
loaded_data = pickle.load(dataset_file, encoding='latin1')
(dataset_x, dataset_y) = loaded_data
print("dataset_x:",type(dataset_x), dataset_x.size, dataset_x.shape)
print("dataset_y:",type(dataset_y), dataset_y.size,dataset_y.shape)
except pickle.UnpicklingError as e:
print("Error while loading the pickle file:", e)All the step-by-step process/related codes for buidling the ML-JET Dataset can be found in jet_ml_dataset_builder Directory.
MNIST Net ~\cite{lecun1998gradient}, more commonly known as LeNet. It was initially devised for handwritten digit recognition, leverages insights into 2D shape invariances through local connection patterns and weight constraints. It uses an image input. With 4 layers, including convolutional and fully connected layers, MNIST Net boasts 96,445 trainable parameters. The model implementation can be found at MNIST Net Direcetory.
VGG16Net ~\cite{simonyan2014very}, renowned for its remarkable performance in image recognition tasks. It uses an image input and comprises 16 layers, with 4 convolutional and fully connected blocks, totaling 15,676,673 trainable parameters. The model implementation can be found at VGG16 Net Direcetory.
PointNet~\cite{qi2017pointnet} introduces a novel approach to processing point cloud data, making it uniquely suited for our jet event image classification task. Unlike conventional CNNs that operate on structured grid-like data, PointNet directly consumes unordered point sets. The model implementation can be found at Point Net Direcetory.
All following methods implemetation can be found at ML models directory.
- The logistic regression model is trained specifically for binary classification on the first column.
- Predictions and evaluation are performed based on the binary labels.
This code uses DecisionTreeClassifier instead of LogisticRegression. The structure is similar: extract the first column for binary classification, split the dataset, flatten the images, initialize the model, train the model, make predictions, and evaluate the accuracy.
This code uses LinearSVC instead of LogisticRegression or DecisionTreeClassifier. The structure remains similar: extract the first column for binary classification, split the dataset, flatten the images, initialize the model, train the model, make predictions, and evaluate the accuracy.
Adjust the k_neighbors parameter based on your requirements. The structure is similar to the previous examples: extract the first column for binary classification, split the dataset, flatten the images, initialize the model, train the model, make predictions, and evaluate the accuracy.
This code uses RandomForestClassifier from scikit-learn. The structure is similar to the previous examples: extract the first column for binary classification, split the dataset, flatten the images, initialize the model, train the model, make predictions, and evaluate the accuracy.
jet_ml_dataset_builder_by_size For building a new dataset from the original dataset with different
jet_ml_diffusion_model Difussion model implemeteation for generating events from parameters
jet_ml_mnist_net MNIST Net Implemetation of binary classifier for Eloss for each 9 different configuration
jet_ml_models consists of python classes for each implmented models (For now just pointnet has its own implementation)
jet_ml_models_notebooks it consists the binary classification implemetation of Decision Tree, KNN, Random Forest, SVM,and Logistic Regression for $eloss$ , more detail explanation of code implemented in this directory is in Traditional machine learning method section.
jet_ml_pointnet several implmentation of PointNet for binary classification of $eloss$ or a single notebook that can train different type of classifier based on user desire, and it includes a sample tensorflow GPU implmentation
jet_ml_pointnet_alpha_s includes a PointNet classifier specifically for $\alpha_s$
jet_ml_pointnet_eloss includes a PointNet binary classifier specifically for $eloss$
jet_ml_sample_events includes sample events from each dataset configuration in 2-D and point cloud demostration.
jet_ml_synthesis_model_vgg16 consists of VGG16 Net implemetation, user can choose the desired parameter from $eloss$ , $\alpha_s$ , or $Q_0$ to train the classifier for it
jet_ml_validation_calculator consists of implmentation for loading a trained model and calculate the confusion matrix and accuracy for it based on the loaded dataset
jet_ml_vgg16_model_cnn VGG16 Net Implemetation of binary classifier for Eloss for each 9 different configuration
Once you have the repository set up and the dependencies installed, you can start utilizing the project:
- Data Preprocessing: Use the provided scripts to preprocess and prepare Jetscape simulation data for analysis.
- Machine Learning Models: Explore the models directory to find pre-implemented machine learning models tailored for analyzing Jetscape data.
- Example Notebooks: Check out the notebooks directory for example Jupyter notebooks that demonstrate how to use the machine learning models with Jetscape data.
- Customization: Feel free to customize and extend the code to suit your specific needs and experiments.
Contributions are welcome and encouraged! If you'd like to contribute to Hm.JetscapeMl, follow these steps:
- Fork the repository to your GitHub account.
- Create a new branch from the main branch for your changes.
- Make your changes and commit them with descriptive commit messages.
- Push your changes to your forked repository.
- Open a pull request (PR) to the original repository, describing the changes you've made.
Please ensure your contributions adhere to the project's coding standards and follow best practices.
This project is licensed under the MIT License. Feel free to customize this template according to your project's specifics and additional information you want to provide.
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