ASTROPHYSICS, ASTRONOMY, COSMOLOGY
A Deep Learning View of the Sensus of Galaxy Clusters in IllustrisTNG [Paper] [arXiv] [doi] [bibTex] [media]
The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc=81%, or surface brightness concentrations, giving BAcc=73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r ≈ 300 kpc and r ≈ 500 kpc to identify CC and NCC clusters, respectively. |
Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate [Paper] [arXiv] [Poster] [code] [webpage] [bibTex]
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in modern astronomy and cosmology. The determination of optical richness can be challenging. We propose a self-supervised approach for estimating optical richness from multi-band optical images. The method uses the data properties of the multi-band optical images for pre-training, which enables learning feature representations from a large but unlabeled dataset. We apply the proposed method to the Sloan Digital Sky Survey. The result shows our estimate of optical richness lowers the mean absolute error and intrinsic scatter by 11.84% and 20.78%, respectively, while reducing the need for labeled training data by up to 60%. We believe the proposed method will benefit astronomy and cosmology, where a large number of unlabeled multi-band images are available, but acquiring image labels is costly. |
Multi-Branch Attention Networks for Classifying Galaxy Clusters [doi] [bibTex]
This paper addresses the task of classifying galaxy clusters, which are the largest known objects in the Universe. Galaxy Clusters can be categorized into cool-core (CC), weak-cool-core (WCC), and non-cool-core (NCC) clusters, defined by their central cooling times. Conventional approaches in astrophysics for conducting such classification are through measuring their surface brightness concentrations or central gas densities, which are inaccurate. Off-the-shelf deep learning approaches for solving this problem would be taking entire images as inputs and predicting cluster types directly. However, this strategy is limited in that central cooling times are usually related to only small informative regions near the center, and feeding unrelated outer regions into the network may hurt the performance. We propose multi-branch attention networks that utilize attention and bivariate Gaussian distribution to identify the galaxy cluster type. Our loss function is designed by encompassing our domain knowledge that the central cooling time of three different types of galaxy clusters (CC, WCC, NCC) varies continuously. |