Data Science for Science

Hubble Deep Field. Courtesy: Encyclopedia Britannica Space is vast. The number of objects in the Universe is literally astronomical. The first step toward exploring these objects is to identify them aka classify them. Objects could be stars, galaxies, quasars, supernovae etc. Due to the sheer number and complexity of objects the task is very challenging to do manually. The further away a source of light is, the harder it is to distinguish its features and thus classify it. Astronomers have now sought the aid of AI in this task. A machine learning pipeline called SHEEP has been developed. SHEEP first extracts the photometric redshift of celestial objects and then uses this as one of the data features fed into an ML model for training. SHEEP combines several ML algorithms: XGBoost , LightGBM , and Catboost to obtain better classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class and (ii) one versus all with correction by a meta-learner.