Machine Learning methods for recognition and classification of Young Stellar Objects – Ilknur Gezer

We are living in the big data age and the analysis of huge amounts of data is a new challenge for researchers. This is especially true for astronomers. The number of infrared facilities and the amount of collected data increased several order of magnitudes in the last decade. Increasing data leading us to new discoveries through data mining and knowledge discovery in databases using modern statistical methods, supervised and unsupervised machine learning. Provided data by on-going surveys allow us to catch phenomena that we have never seen before. I present the project NEMESIS (Novel Evolutionary Model for the Early stages of Stars with Intelligent Systems) that aims to build the largest panchromatic dataset of Young Stellar Objects (YSOs) and our methods
that are efficiently used in YSO discoveries in large catalogues.