Convolutional and hybrid neural network for cluster membership

Abstract

We conduct investigations of the membership of galaxies inside a cluster using a machine-learning Convolutional Neural Network (CNN), and a hybrid of CNN with Multi-Layer Perceptron (MLP) neural network. These networks are trained using optical images of each galaxy in I and V bands as well as using various photometric and morphological parameters of galaxies extracted and measured from these images. The optical images are observed with Subaru Suprime-Cam for 16 clusters at redshift ∼ 0.15–0.3. This dataset enables a wide and faint investigation of cluster membership that is essential for the investigations of dwarf galaxies, but it is often difficult to achieve both simultaneously. We optimize the network structure to directly determine the membership classification, rather than estimating the galaxy redshift first as an intermediate step and then determining the membership from the redshifts. We find: 1. The CNN model can be useful for the application of identifying the cluster membership of galaxies, similar to the MLP model, implying that the CNN model can use information beyond galaxy SED. 2. A simple CNN model appears to predict the cluster membership less accurately than the 17-measure MLP model. 3. For better performance of CNN, the masking of the object is important, as well as scaling based on the photometric calibration. 4. The hybrid model of CNN and MLP combined can produce a better prediction than the single MLP (or CNN) model; however, the improvement is small at best implying that the information extracted by 17 measures for the MLP model is fairly comprehensive in sense of the total information contained in pixels of the image. 5. All models appear to predict the membership more accurately inside a smaller radius closer to the cluster center. 6. Faint galaxies are harder to assign their memberships even if using our models, though the performance is more robust than other photometric methods.Artificial intelligence (AI) can help us effectively combine various conventional methods of finding cluster membership. By making it inherit advantages of each of these conventional methods, The AI membership can achieve overall good performance in various performance statistics simultaneously, such as purity, completeness, F1 score, AUC, and accuracy. The overall good performance of the AI membership is vital to cluster studies in the era of faint and data-intensive galaxy survey s in which the complete spectroscopic observation of all galaxies is out of reach.