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Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog

Student: Stanislav Dobryakov

Supervisor: Denis Derkach

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

In recent years the number of observations obtained from powerful telescopes has been growing significantly. To process such amount of information, laboratories need new automated ways to distinguish the most interesting objects from others. In this article we suggest an efficient way of data-driven binary classification of Supernovae objects into two classes: Ia vs Not-Ia. Our method is based on a Random Forest model with a specific way of data processing and feature generation. For a training process of a model we use only real data obtained from telescopes presented in the Open Supernova Catalog. As a result, we achieved a good quality of a classification model with a target metric 0.89 ROC AUC.

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