Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Computer Science

1 2025

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Characteristic sky-background features around galaxy mergers

Suelves Luis E, Pearson William J., Pollo Agnieszka

Computer Science

2025

T. 26 (SI)

111--126

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.