Towards understanding feedback from supermassive black holes using convolutional neural networks
This addresses the challenge of analyzing noisy astronomical data for astrophysicists, though it is incremental as it builds on existing simulation and neural network techniques.
The paper tackled the problem of detecting and characterizing X-ray cavities from supermassive black hole feedback in galaxy clusters by developing an automatic method using convolutional neural networks on simulated low-resolution images, achieving improved accuracy, stability, and speed over visual inspection methods.
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.