Abstract
In high-energy astronomy, spectro-imaging instruments such as X-ray detectors allow investigation of the spatial and spectral properties of extended sources including galaxy clusters, galaxies, diffuse interstellar medium, supernova remnants, and pulsar wind nebulae. In these sources, each physical component possesses a different spatial and spectral signature, but the components are entangled. Extracting the intrinsic spatial and spectral information of the individual components from this data is a challenging task. Current analysis methods do not fully exploit the 2D-1D (x, y, E) nature of the data, as spatial information is considered separately from spectral information. Here we investigate the application of a blind source separation (BSS) algorithm that jointly exploits the spectral and spatial signatures of each component in order to disentangle them. We explore the capabilities of a new BSS method (the general morphological component analysis; GMCA), initially developed to extract an image of the cosmic microwave background from Planck data, in an X-ray context. The performance of the GMCA on X-ray data is tested using Monte-Carlo simulations of supernova remnant toy models designed to represent typical science cases. We find that the GMCA is able to separate highly entangled components in X-ray data even in high-contrast scenarios, and can extract the spectrum and map of each physical component with high accuracy. A modification of the algorithm is proposed in order to improve the spectral fidelity in the case of strongly overlapping spatial components, and we investigate a resampling method to derive realistic uncertainties associated to the results of the algorithm. Applying the modified algorithm to the deep Chandra observations of Cassiopeia A, we are able to produce detailed maps of the synchrotron emission at low energies (0.6–2.2 keV), and of the red- and blueshifted distributions of a number of elements including Si and Fe K.
Highlights
Beginning in the 1970s, it was realised that the X-ray sky is full of extended sources, among which we find emission from the Milky Way itself, other Galactic sources such as pulsar wind nebulae or supernova remnants (SNRs), and extragalactic sources such as galaxies and clusters of galaxies
The error bars obtained through generalized morphological components analysis (GMCA) applied on 100 block bootstrap resamplings are slightly overestimated in comparison with those obtained with Monte-Carlo, but this does not have a crucial impact on the best-fit parameters obtained in Xspec
We present a method based on the GMCA, a blind source-separation algorithm developed to extract the cosmic microwave background (CMB) from Planck data
Summary
Beginning in the 1970s, it was realised that the X-ray sky is full of extended sources, among which we find emission from the Milky Way itself, other Galactic sources such as pulsar wind nebulae or supernova remnants (SNRs), and extragalactic sources such as galaxies and clusters of galaxies. Our method is based on an algorithm that uses the ability of wavelets to provide a sparse representation for astrophysical images to find a solution to BSS problems In this context, we consider our 2D-1D data cube as the product between an image and a spectrum. We adapt the GMCA algorithm to the study of extended sources in X-rays, and test its implementation by applying the method to SNR data. We can cite Jones et al (2015), who used Bayesian statistical methods to infer the number of sources and probabilistically separate photons among the sources These methods work with event lists (x, y, E), and do not retrieve images or spectra associated with the sources, as our method does
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
