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ENSEMBLE LEARNING FOR PROBABILISTIC CLASSIFICATION OF ASTRONOMICAL OBJECTS IN DEEP FIELDS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING METHOD

S. Brindha, Ram Prasad M

Abstract


Understanding cosmic structure and evolution requires the classification of stars, galaxies, and quasars; however, supervised learning techniques are still limited by the lack of labeled spectroscopic data, and the large amount of data from contemporary surveys makes manual classification unfeasible. Massive amounts of image data encompassing dim and densely populated celestial objects are produced by deep astronomy surveys. It is difficult to classify these artifacts accurately, especially in deep-field areas without authorized catalog labeling. This paper presents an Artificial Intelligence (AI) and Machine Learning (ML)–driven framework for probabilistic classification of astronomical objects using coordinate-based catalog labeling combined with ensemble learning techniques. Objects detected in cataloged sky regions are cross-matched using celestial coordinates to obtain reliable ground-truth labels from established astronomical databases [6]. These labeled samples are used to train a One-vs-Rest ensemble of Machine Learning classifiers, including Random Forest, Gradient Boosting, Support Vector Machine, in addition to the Logistic Regression models.

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