Browsing by Author "Saavedra Bastidas, Jorge Eduardo"

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    Classification of major galaxy mergers using machine learning algorithms trained with N-body simulations.
    (Universidad de Concepción, 2024) Saavedra Bastidas, Jorge Eduardo; Schleicher, Dominik
    Galaxy mergers are significant events in astronomy, driving the morphological transformation from spiral to elliptical galaxies and disrupting internal gas mechanics, increasing star formation, enhancing nuclear activity, and contributing to the formation and evolution of supermassive black holes. Traditional detection methods for galaxy mergers lack the effectiveness and efficiency required to handle large datasets. In this study, we perform a systematic comparison of different machine learning models as classifiers for major galaxy mergers and their merger stages, relying solely on morphological information. We test ensemble-based classifiers like Random Forest (RF) and Extreme Gradient Boosting (XGboost) and deep learning architectures like Convolutional Neural Networks (CNNs). We propose the implementation of images extracted from N-body simulations designed to replicate the morphological features of galaxy-galaxy interactions as training data for the classification algorithms. We evaluate the performance of these models across three levels of observational realism: highly idealized galaxies extracted from our simulations, galaxies convoluted with a Gaussian point spread function (PSF), and galaxies convoluted with the Gaussian PSF and complemented with real background noise. We found that models with the best performance on the highest observational realism synthetic test set are those trained on data from the same distribution. CNNs achieved an average area under the receiver operating characteristic curve of 95.2%, while XGBoost and RF obtained 93.5% and 93.0%, respectively. Despite being in second place, XGBoost shows greater stability than CNNs when predicting mergers from galaxies provided by different data distributions. We test XGBoost on a sample of massive, low-redshift (z ≤ 0.15) galaxies from the Dark Energy Camera Legacy Survey - Galaxy Zoo Data Release 5, showing the ability to differentiate galaxy pairs effectively. We conclude that morphological features are a solid base for training a machine learning classifier for galaxy mergers, however, the differences between isolated galaxies and recent post-mergers require more detailed physics to completely characterize both stages.
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