This repository contains the machine learning methods for our multi-algorithm approach of flare identification in light curve data. Models We trained three models using three different algorithms: Deep Neural Network (DNN) Random Forest (RF) XGBoost These models are designed to identify flares in TESS short-cadence light curve data but can theoretically be applied to light curve data observed at different cadences. The models are user-friendly and can run on standard machines. You can find them in the "ML_models" directory of this repository: DNNClassifier_flare_classification-by-Lin.keras RandomForestClassifier_flare_classification-by-Lin.pkl XGBoostClassifier_flare_classification-by-Lin.json Tutorial A comprehensive tutorial on how to effectively use these models is provided in "Tutorial.ipynb". This tutorial will guide you step-by-step on: Collecting flare candidates: How to gather flare candidates from the TESS light curve data. Feature extraction: How to determine the features of these flare candidates. Identifying true flares: How to use our machine learning models to identify "True Flares" among these candidates. Installation and Dependencies To run the models and tutorial, ensure you have all standard/wide-used Python packages (i.e., numpy, scipy, matplotlib, etc.) and the following dependencies installed: Python 3.x TensorFlow or PyTorch (for DNN) Scikit-learn (for Random Forest) XGBoost You can install these dependencies using the following command: pip install tensorflow scikit-learn xgboost Usage To learn how to use the models, follow these steps: Clone the repository: git clone https://github.com/CLL-Lin/MLsFlares.git cd MLsFlares Follow the instructions in "Tutorial.ipynb" to start. Citation If you find our models useful, please cite the article "Scalable, Advanced Machine Learning-based Approaches for Stellar Flare Identification: Application to TESS short-cadence Data and Analysis of a New Flare Catalogue". Note: This article is still under review. We will update the DOI and the announcement when the paper is published in Atronomical Journal.