CHARACTERISTIC SKY BACKGROUND FEATURES AROUND GALAXY MERGERS

Abstract

In the context of finding galaxy merger in large-scale surveys, we applied MachineLearning algorithms that, instead of using the images as it is the currentstandard, made used of flux measurements. Training multiple NNs using aclass-balanced dataset of mergers and non-mergers Sloan Digital Sky Survey,we found that the sky background error parameters could provide a validation92.64 ± 0.15 % accuracy of and a training accuracy of 92.36 ± 0.21 %.Moreover, analysing the NN identifications led us to find that a simple decisiondiagram using the sky error for two flux filters is enough to get a 91.59 % accuracy.By understanding how the galaxies vary along the diagram, and trying toparametrize the methodology in the deeper images of the Hyper Suprime-Cam,we are currently trying to define and generalize this sky error-based methodology