Speaker
Description
AGN feedback is a key process in the evolution of massive systems in the Universe, and one for which new observational information is crucial for better implementation of numerical models. An image-manipulation technique capable of providing such information, X-arithmetic, is applied here to a sample of 15 galaxy clusters and groups deeply observed with Chandra. This technique decomposes perturbations in feedback-dominated regions into images excluding either (1) weak shocks, (2) cooling and slow gas motions, or (3) bubbles inflated by jets. We use the images to verify previously identified features and reveal the nature of new ones. We identify trends in features around galaxy groups vs. clusters, and showcase the potential for applying this technique to numerical simulations of AGN feedback, providing an ultimate test of feedback models.
