Characteristic sky-background features around galaxy mergers

• Autorzy: Suelves Luis E , Pearson William J. , Pollo Agnieszka

Identyfikatory

DOI

10.7494/csci.2025.26.SI.7072

• Języki publikacji: EN

Abstrakty

EN

In the context of finding galaxy mergers in large-scale surveys, we applied machine-learning algorithms that made use of flux measurements instead of using images (as is the current standard). By training multiple NNs using the Sloan Digital Sky Survey class-balanced data set of mergers and non-mergers, we found that sky-background error parameters could provide a validation accuracy of 92.64±0.15% and a training accuracy of 92.36±0.21%. Moreover, analyzing the NN identifications led us to find that a simple decision diagram using the sky error for two flux filters was enough to gain a 91.59% accuracy. By understanding how the galaxies vary along the diagram and trying to parametrize the methodology in the deeper images of the Hyper Suprime-Cam, we are currently trying to define and generalize this sky error-based methodology.

Słowa kluczowe

EN

galaxies; evolution; interactions; photometry; data analysis; numerical methods

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2025
• Tom: T. 26 (SI)
• Strony: 111--126
• Opis fizyczny: Bibliogr. 60 poz., rys., wykr.

Twórcy

autor

Suelves Luis E

• Tartu Observatory, University of Tartu, Observatooriumi 1, Toravere 61602, Estonia
• National Centre for Nuclear Research, Pasteura 7, 02-093 Warszawa, Poland

autor

Pearson William J.

• National Centre for Nuclear Research, Pasteura 7, 02-093 Warszawa, Poland

autor

Pollo Agnieszka

• National Centre for Nuclear Research, Pasteura 7, 02-093 Warszawa, Poland
• Astronomical Observatory of Jagiellonian University, Faculty of Physics
• Astronomy, and Applied Computer Science, ul. Orla 171, 30-244 Krakow, Poland

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