Merger identification through photometric bands, colours, and their errors

TL;DR

This paper demonstrates that a neural network can effectively identify galaxy mergers using only photometric data, especially sky error backgrounds, achieving over 92% accuracy, and explores the physical significance of these features.

Contribution

It introduces a neural network approach for galaxy merger detection using solely photometric information, highlighting the importance of sky error backgrounds and data normalization.

Findings

Sky error background is the most sensitive feature for merger detection.

Neural network achieves over 92% accuracy using sky error data.

Data normalization significantly impacts training performance.

Abstract

Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method's efficiency, but also to understand what merger properties the NN can learn and what their physical interpretation is. Methods. We created a class-balanced training dataset of 5\,860 galaxies split into mergers and non-mergers. The galaxy observations came from SDSS DR6 and were visually identified in Galaxy Zoo. The 2$\,$930 mergers were selected from known SDSS mergers and the respective non-mergers were the closest match in both redshift and $r$ magnitude. The NN architecture was built by testing a different number of layers with different sizes and variations of the dropout rate. We compared input spaces constructed using: the five SDSS filters: $u$, $g$, $r$, $i$, and $z$; combinations of bands, colours, and their errors; six magnitude types; and variations of input normalization. Results. We find that the fibre magnitude errors contribute the most to the training accuracy. Studying the parameters from which they are calculated, we show that the input space built from the sky error background in the five SDSS bands alone leads to 92.64 $\pm$ 0.15 \% training accuracy. We also find that the input normalization, that is to say, how the data are presented to the NN, has a significant effect on the training performance. Conclusions. We conclude that, from all the SDSS photometric information, the sky error background is the most sensitive to merging processes. This finding is supported by an analysis of its five-band feature space by means of data visualization. Moreover, studying the plane of the $g$ and $r$ sky error bands shows that a decision boundary line is enough to achieve an accuracy of 91.59\%.