Speaker
Description
The morphological analysis of galaxies provides important insights into the physical processes that shape their formation and evolution. Modern sky surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory, enable the study of galaxy morphology across large samples, further increasing the need for efficient automated analysis methods.
This presentation discusses ongoing work toward the large-scale morphological analysis of spiral galaxies in survey imaging data. The project aims to develop a scalable Python-based pipeline that integrates multiple techniques for characterizing spiral structure while maintaining robustness across heterogeneous imaging datasets and compatibility with high-volume survey workflows.
Beyond their astrophysical relevance, the extracted morphological features can add value to large galaxy catalogs, supporting efforts to extend compilations (such as GLADE+ and the newer UpGLADE catalog developed in LIGO) toward significantly larger samples.
Preliminary work focuses on implementing core components of the analysis pipeline and testing them on archival galaxy images. The talk outlines the methodological framework, summarizes initial results, and discusses directions for further development in preparation for future large survey datasets.