Speaker
Description
We present PyLongslit, a simple and transparent Python pipeline for processing astronomical long-slit spectroscopy data obtained with CCD detectors. The software is designed to prioritize manual execution, robustness, and pedagogical clarity, providing an accessible alternative to highly automated “black-box” reduction pipelines. The pipeline emphasizes visualization and quality assessment at each processing step, making it particularly well suited for teaching environments and for challenging datasets where automated methods may fail. Validation against established semi-automated reductions demonstrates good agreement in extracted spectra and noise estimates across multiple instruments. In this presentation, we will highlight several difficult and edge-case reductions where manual control and transparency provide clear advantages, and provide an overview of the scientific research in which the pipeline has already been applied.