| 其他摘要 | Since violent formation of stars (i.e. Starburst) and growth of SMBHs (i.e. active galactic nucleus, AGN) are coupled and ongoing together, dust-obscured Starburst-AGN composite galaxies represent important phases in the formation and evolution of galaxies, and are ideal laboratories for studying starburst-AGN connections. The spectral energy distributions (SEDs) are encoded with information about star formations, stellar populations, interstellar mediums (ISM) and AGN in these interesting objects. However, it is currently still very challenging to efficiently extract the basic physical properties of these galaxies from the analysis of their SEDs. In this thesis, we firstly described the field of the analysis of SEDs (or fitting), including the basic concept of SED, the construction of observed multi-wavelength SED of galaxies, and the methods for the analysis of SEDs. Then, we present our work on this field in detail. The luminosity function (LF) of AGN is an important observable quantity for our understanding of AGN activities and their evolution. In practice, the LFs of AGN are measured independently from different wavelength bands, all of which are suffered by different limitations. To obtain an accurate determination of the LF of AGN, multi-wavelength observations need to be combined self-consistently. Given these, we present a detailed comparison between the $2-10$ keV hard X-ray and infrared (IR) LF of AGN, by employing a simple but well tested model for the SEDs of AGN. We find that the IRLFs predicted from HXLFs tend to underestimate the number of the most IR-luminous AGN. This is independent of the choices of HXLF, and even more obvious for the HXLFs recently measured. We show that the discrepancy between the independently obtained HXLFs and IRLFs of AGN can be largely resolved when the anticorrelation between the UV to X-ray slope $\alpha_{\mathrm{ox}}$ and UV luminosity $L_{\rm UV}$ is appropriately considered in the model for the SEDs of AGN. We also discuss other possible explanations for the discrepancy, such as the missing population of Compton-thick AGN and possible contribution of star-formation in the host to the mid-IR. Meanwhile, we find that the HXLFs and IRLFs of AGN can be more consistent with each other if the obscuration mechanisms of Quasars and Seyferts are assumed to be different. This is consistent with the widely accepted idea that the mechanisms of the two types of AGN are fundamentally different. The dust-obscured Starburst-AGN composite galaxies are more complicated than AGN, and the analysis of their multi-wavelength SEDs is more challenging. Given these, we have built BayeSED, a general purpose tool for doing Bayesian analysis of SEDs by using whatever pre-existing model SED libraries or their linear combinations. The artificial neural networks (ANNs), principal component analysis (PCA) and multimodal nested sampling (MultiNest) techniques are employed to allow a highly efficient sampling of posterior distribution and the calculation of Bayesian evidence. As a demonstration, we apply this tool to a sample of hyperluminous infrared galaxies (HLIRGs). The Bayesian evidences obtained for a pure Starburst, a pure AGN, and a linear combination of Starburst+AGN models show that the Starburst+AGN model have the highest evidence for all galaxies in this sample. The Bayesian evidences for the three models and the estimated contributions of starburst and AGN to infrared luminosity show that HLIRGs can be classified into two groups: one dominated by starburst and the other dominated by AGN. Other parameters and corresponding uncertainties about starburst and AGN are also estimated by using the model with the highest Bayesian evidence. We found that the starburst region of the HLIRGs dominated by starburst tends to be more compact and has a higher fraction of OB star than that of HLIRGs dominated by AGN. Meanwhile, the AGN torus of the HLIRGs dominated by AGN tend to be more dusty than that of HLIRGs dominated by starburst. Overall, we believe that BayeSED could be a reliable and efficient tool for exploring the nature of complex systems such as dust-obscured Starburst-AGN composite systems from decoding their SEDs. |
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