The Discovery of Quasi‐soft and Supersoft Sources in External Galaxies

Abstract

We apply a uniform procedure to select very soft sources from point sources observed by Chandra in four galaxies. This sample includes one elliptical galaxy (NGC 4967), two face-on spiral galaxies (M101 and M83), and an interacting galaxy (M51). We report on some intriguing results, including the following: 1. We have found very soft X-ray sources (VSSs) in every galaxy. Some of these fit the criteria for canonical supersoft sources (SSSs), while others are somewhat harder. These latter have characteristic values of kT 300 eV; we refer to them as quasi-soft sources (QSSs). We found a combined total of 149 VSSs in the four galaxies we considered; 77 were SSSs and 72 were QSSs. 2. The data are consistent with the existence of a large VSS population, most of whose members we cannot observe because of the effects of distance and obscuration. The total VSS population of sources with L > 1037 ergs s-1 in each galaxy could be on the order of 1000. 3. Whereas in M31 only ~10% of all X-ray sources detected by Chandra are VSSs, more than 35% of all detectable X-ray sources in the face-on galaxy M101 fit the phenomenological definition of VSSs. This difference may be due to differences in NH between typical lines of sight to sources in each galaxy. 4. SSSs can be super-Eddington for Chandrasekhar-mass objects. 5. We find evidence for SSSs and QSSs with luminosities of 1036 ergs s-1 < L < 1037 ergs s-1. These sources have luminosities lower than those of the ~30 soft sources used to establish the class of SSSs. 6. In the spiral galaxies M101, M83, and M51, a large fraction of the SSSs and QSSs appear to be associated with the spiral arms. This may indicate that some SSSs are young systems, possibly younger than 108 yr. 7. In addition to finding hot white dwarfs and soft X-ray binaries, our method should also be efficient at selecting supernova remnants (SNRs). A small fraction of the VSSs in the spiral arms of M101 appear to be associated with SNRs