LSSTdriven

LSSTdriven is a software framework designed to facilitate the analysis of data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). It provides a collection of tools and libraries that enable astronomers and data scientists to process, analyze, and visualize the vast datasets generated by LSST. The framework aims to streamline the workflow for scientific investigations, making it easier to derive meaningful scientific results from the observational data.

The LSSTdriven project is developed as an open-source initiative, encouraging community contributions and ensuring its accessibility to researchers worldwide. It is built upon established astronomical software packages and aims to integrate with the broader LSST Science Pipelines. This integration allows for seamless transition between the raw data processing and higher-level scientific analysis tasks.

Key functionalities of LSSTdriven include tools for object catalog generation, time-series analysis, and the implementation of machine learning algorithms for tasks such as object classification and anomaly detection. The framework is designed to be modular and extensible, allowing users to adapt it to specific scientific use cases. LSSTdriven plays a crucial role in enabling the scientific community to leverage the full potential of the LSST data for a wide range of astronomical research, from studying the Milky Way to probing the nature of dark energy.