Multimodal Astrophysical Object Classification Dataset (SDSS, NASA Exoplanet Archive, Pan-STARRS, Gaia)

This dataset contains a multimodal collection of astrophysical objects compiled from several major astronomical surveys and archives, including: Sloan Digital Sky Survey (SDSS) NASA Exoplanet Archive Pan-STARRS (Panoramic Survey Telescope and Rapid Response System) Gaia DR3 The dataset includes 6000 entries labeled across various object types such as GALAXY, STAR, QSO (quasi-stellar objects), EXOPLANET, and ASTEROID. Each object is described by photometric and positional features like right ascension, declination, magnitudes in multiple bands (u, g, r, i, z), redshift (if available), and survey-specific parameters. The goal of this dataset is to support research and benchmarking in machine learning-based classification of astrophysical objects. The data is preprocessed and split into: features.csv: feature matrix labels.csv: corresponding class labels merged_dataset.csv: full combined dataset All scripts used to retrieve and merge the data (veri_cek.py, veri_birlestir.py) are included for reproducibility. This dataset is suitable for training and evaluating supervised classification algorithms, as well as exploring feature distributions across diverse astronomical object classes.