Towards understanding feedback from supermassive black holes using convolutional neural networks

TL;DR

This paper introduces a convolutional neural network approach to automatically detect and characterize X-ray cavities caused by supermassive black holes in galaxy clusters, improving accuracy and efficiency over traditional methods.

Contribution

The authors develop a novel CNN-based method for identifying and measuring X-ray cavities in noisy, low-resolution images, surpassing existing visual inspection techniques.

Findings

CNN outperforms traditional methods in accuracy and speed

Method works effectively on simulated high-redshift galaxy cluster images

Automates cavity detection and measurement process

Abstract

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.