The first spatio-spectral Bayesian imaging of SN1006 in X-rays

Abstract

Supernovae (SNs) are an important source of energy in the interstellar medium. Young remnants of supernovae (SNRs) exhibit peak emission in the X-ray region, making them interesting objects for X-ray observations. In particular, the supernova remnant SN1006 is of great interest due to its historical record, proximity, and brightness. Thus, it has been studied with a number of X-ray telescopes. Improving X-ray imaging of this and other remnants is an important but challenging task, as it often requires multiple observations with different instrument responses to image the entire object. Here, we use Chandra observations to demonstrate the capabilities of Bayesian image reconstruction using information field theory (IFT). Our objective is to reconstruct denoised, deconvolved, and spatio-spectral resolved images from X-ray observations and to decompose the emission into different morphologies, namely, diffuse and point-like. Further, we aim to fuse data from different detectors and pointings into a mosaic and quantify the uncertainty of our result. By utilizing prior knowledge on the spatial and spectral correlation structure of the diffuse emission and point sources, this method allows for the effective decomposition of the signal into these two components. In order to accelerate the imaging process, we introduced a multi-step approach, in which the spatial reconstruction obtained for a single energy range is used to derive an informed starting point for the full spatio-spectral reconstruction. We applied this method to 11 Chandra observations of SN1006 from 2008 and 2012, providing a detailed, denoised, and decomposed view of the remnant. In particular, the separated view of the diffuse emission ought to provide new insights into the complex, small-scale structures in the center of the remnant and at the shock front profiles. For example, our analysis reveals sharp X-ray flux increases by up to two orders of magnitude at the shock fronts of SN1006.