The universe is constantly changing. Stars explode as supernovae, asteroids drift across our field of view, and variable stars pulse with mysterious rhythms. But catching these fleeting cosmic events requires sophisticated image processing, precise measurements, and lightning-fast analysis - a challenge that has traditionally demanded custom-built pipelines for every observatory and instrument.
STDPipe transforms this complex workflow into an elegant Python library that handles the entire transient detection pipeline. It seamlessly orchestrates industry-standard tools like SExtractor for object detection, Astrometry.Net for coordinate calibration, and HOTPANTS for image subtraction, while adding intelligent features like automated photometric calibration against Gaia and PanSTARRS catalogs, noise-weighted detection algorithms, and sophisticated template matching. The library operates on familiar Python objects - NumPy arrays for images and Astropy tables for catalogs - making it incredibly approachable for researchers who want to focus on discovery rather than data wrangling.
Whether you’re running a small robotic telescope searching for nearby supernovae or processing archival survey data for statistical studies of stellar variability, STDPipe provides the computational foundation for modern time-domain astronomy. Its modular design means you can easily customize each step of the pipeline while benefiting from battle-tested algorithms that have already proven themselves in real astronomical surveys.
⭐ Stars: 19
💻 Language: Jupyter Notebook
🔗 Repository: karpov-sv/stdpipe