Abstract
The diffuse X-ray background (DXB) has been extensively studied since its first discovery in 1962, and significant progress has been made during the past few decades. Modern X-ray observatories such as Chandra and XMM-Newton have resolved most of the emission above 1 keV into point sources including AGN, galaxies and stars. However, the soft-band emission of DXB below 1 keV is more complicated. Current theories interpret the DXB below 1 keV as a combination of foreground emission from Local Hot Bubble and Solar Wind Charge eXchange, plus background emission from Galactic Halo (GH)/CircumGalactic Medium (CGM), intergalactic gas, and unresolved point sources. In particular, we are interested in the emission from GH/CGM, as its origin and spatial distribution are still uncertain, and it is believed a significant fraction of the missing baryon of our Milky Way Galaxy resides in the GH/CGM. In this investigation, using data from XMM-Newton and Chandra, we are able to disentangle and analyze the spectral features of different DXB components.
In the first project, by analyzing the spectra for DXB and unresolved Galaxies in Chandra Deep Field South, we found that a significant fraction of the emission at 3/4 keV, which is typically associated with GH/CGM is, in fact, due to emission from typically unresolved Galaxies. The primary goal of my second project is to study the spatial distribution of the GH/CGM emission on a relatively small angular scale (<1 degree). We divided the COSMOS and Stripe 82 fields into small regions and analyzed the spectral features from each region. We visualized the spatial distributions of GH/CGM emission measures and temperatures with heatmaps, and quantitatively studied their spatial variabilities using Autocorrelation Function and power spectrum analysis. The third project aims to build a comprehensive database of all ∼4 million known AGN, with uniform photometry, redshifts and host galaxy properties. To decontaminate non-AGN sources in the database, we tested and implemented machine learning models for source classification.