| Autor: |
Margalef-Bentabol, B., Wang, L., La Marca, A., Blanco-Prieto, C., Chudy, D., Domínguez-Sánchez, H., Goulding, A. D., Guzmán-Ortega, A., Huertas-Company, M., Martin, G., Pearson, W. J., Rodriguez-Gomez, V., Walmsley, M., Bickley, R. W., Bottrell, C., Conselice, C., O'Ryan, D. |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
A&A 687, A24 (2024) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1051/0004-6361/202348239 |
| Popis: |
Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1
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| Databáze: |
arXiv |
| Externí odkaz: |
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