# 引力波数据探索:编程与分析实战训练营 # Gravitational Wave Data Exploration: A Practical Training in Programming and Analysis > Under construction... Welcome to the GitHub repository for the Gravitational Wave Data Exploration Bootcamp Series! This course is meticulously designed to provide a solid foundation in programming, operational knowledge, and data-driven modeling skills centered around gravitational wave data analysis and research. ## Training Objectives - Equip participants with robust programming and operational skills, and foundational training in data-driven modeling, focusing on gravitational wave data analysis and related research areas. - **Note: The course is conducted entirely in Mandarin Chinese to cater to a wide range of Chinese-speaking students and researchers.** - Discuss the common research methodologies combining gravitational wave data processing with AI technologies, with hands-on examples and projects for practical understanding and mastery. - Analyze cutting-edge deep learning models and apply them to real-world gravitational wave data analysis problems through specific case studies. ## Target Audience - Undergraduate and graduate students interested in data analysis and algorithm development, especially those focusing on gravitational wave data processing and related research. - The course also welcomes undergraduates with a basic programming background, looking to enhance their data analysis skills or with an interest in gravitational wave data processing. - Future professionals aspiring to work in space-based gravitational wave detection projects and related research fields. ## Course Design Philosophy - Drawing from past teaching experiences and identified knowledge gaps in student research projects, the course introduces relevant concepts and common methods to ensure comprehensive understanding and application in research. - The course is scheduled weekly or bi-weekly, each session lasting about 3 hours, combining online and offline methods (腾讯会议) to ensure interactivity and practicality. - The curriculum is expected to be offered once per semester or annually, with continual updates and enrichment based on student feedback and research demands. ## Course Outline / Schedule - **Part Zero**: Motivational Introduction (2023/11/08) - **Part One**: Programming Development Environment and Workflow (2023/11/12) - Linux Commands and Shell Scripting - Git Version Control (GitHub / GitLab) - SSH Remote Server Access (Shell / VSCode) - Containerization with Docker - Hands-On: Setting up Python / Jupyter Development Environment - Hands-On: Compiling LALsuite / LISAcode Source Code - **Part Two**: Python-Based Data Analysis Fundamentals - Introduction to Python Programming - Algorithms with Numpy / Pandas / Scipy - Hands-On: Exploratory Data Analysis of GW Event Catalog / Glitch Data - Hands-On: Matched Filtering for GW150914 Data - Data Visualization in Python: Theory and Practice - Hands-On: Reproducing Figures from GWTC Papers - **Part Three**: Basics of Machine Learning - Overview of Artificial Intelligence - Definitions, Objectives, and Types of Machine Learning - Machine Learning Project Development and Preparation - Hands-On: Clustering Analysis of LIGO's Glitch Data - **Part Four**: Introduction to Deep Learning - Overview of Deep Learning Technologies - Fundamentals of Artificial Neural Networks (ANN) - Convolutional Neural Networks (CNN) - Hands-On: Identifying Gravitational Waves from Binary Black Hole Systems using CNN - Frontiers of Gravitational Wave Data Analysis and AI ## Collaborating Institutions - University of Chinese Academy of Sciences (UCAS) - International Centre for Theoretical Physics Asia-Pacific (ICTP-AP) - Taiji Laboratory for Gravitational Wave Universe ## Getting Started To participate in this course: 1. Clone the repository: ```bash git clone https://github.com/iphysresearch/GWData-Bootcamp.git ``` (Under construction) ## Staff This class is co-taught by [He Wang](https://iphysresearch.github.io/blog/) and several esteemed colleagues, including guest lecturers and industry experts, whose names will be announced as they join. ## Questions For any inquiries regarding the course, please email us at [📧 taiji@ucas.ac.cn](mailto:taiji@ucas.ac.cn). We look forward to your participation and contribution to this exciting field of study! ## Contributing We welcome contributions to enhance course materials. Please fork the repository, make your changes, and submit a pull request. --- ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## Acknowledgments - Contributions from the open-source gravitational wave community. - Educational resources and datasets from renowned institutions and projects in the field.