http://arxiv.org/abs/1712.00523
Supermassive black holes at centers of clusters of galaxies strongly interact with their host environment via AGN feedback. Key tracers of such activity are X-ray cavities — regions of lower X-ray brightness within the cluster. We present an automatic method for detecting, and characterizing X-ray cavities in noisy, low-resolution X-ray images. We simulate clusters of galaxies, insert cavities into them, and produce realistic low-quality images comparable to observations at high redshifts. We then train a custom-built convolutional neural network to generate pixel-wise analysis of presence of cavities in a cluster. A ResNet architecture is then used to decode radii of cavities from the pixel-wise predictions. We surpass the accuracy, stability, and speed of current visual inspection based methods on simulated data.
S. Fort
Tue, 5 Dec 17
30/96
Comments: 5 pages, 5 figures, accepted at Workshop on Deep Learning for Physical Sciences (DLPS 2017), NIPS 2017, Long Beach, CA, USA