The detection and characterization of exoplanets is one of the hot spots in astronomy in recent years, and the effective characterization of the atmospheric and surface characteristics of exoplanets can directly answer the basic questions of human interest, such as whether exoplanets are habitable or not and whether there is other life in universe. The direct imaging spectrum of the planet holds rich information, including atmospheric chemical composition, cloud, and surface properties, which will play a vital role in the future research of planetary science and astrobiology.
Recently, the research team of He Jinping professor from National Astronomical Observatories/Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences, in cooperation with Dr. Yuka Fujii from the National Astronomical Observatory of Japan, has conducted in-depth research on the retrieval of important planetary parameters based on direct imaging spectra, and made some new progress. The related work was published in The Astrophysical Journal (https://doi.org/10.3847/1538-4357/ac67e5) on May 24th.
Compared with previous research results, the team added additional cloud parameters and wavelength-dependent surface albedo parameters when building the model, trying to obtain a more general model that is in line with the actual situation of exoplanets. In the model, 14 parameters are involved, namely, surface atmospheric pressure (Ps), surface gravity (g), three parameters of atmospheric composition (H2O/O2/O3 mixing ratios), six parameters of cloud characteristics (Pb/dp/τ/ω/g0/fcld) and three parameters of surface albedo (blue band albedo Ab, green band albedo Ag, red band albedo Ar). In order to explore the performance comparison of various models with different parameters, one fiducial model and five control models were established in the research, as shown in Table 1. Table 1. Summary of the Observational and Model Scenarios Explored  This study mainly discusses the following four questions: 1. How does the wavelength dependence of albedo disturb our spectral retrieval? 2. How do the unknown cloud scattering properties influence our spectral retrieval? 3. Can the multi-epoch observations or added Rayleigh-bandpass help overcome the difficulty with unknown cloud scattering properties? 4. Which parameters are critical to know or assume priori for the trustworthy parameter estimation?
Figure 1 shows retrieval differences among the fiducial model (Cω,g0S3bands, wavelength-dependent surface albedo parameters and known cloud scattering properties), model I(Cω,g0Sgray, wavelength-independent surface albedo parameters and known cloud scattering properties) and model II(CS3bands, wavelength-dependent surface albedo parameters and unknown cloud scattering properties) on nine important planetary properties. Based on the performance comparison between the fiducial model and model I, it can be seen that the wavelength-dependent surface albedo can help to obtain better parameter estimations. Comparison between fiducial model and the model II shows that unknown cloud scattering properties lead to large biases in parameter estimation, which is mainly caused by the degeneracy among four cloud parameters (τ/ω/g0/fcld). In order to disentangle this degeneracy, this work additionally introduces multi-epoch observations (model III in Table 1), additional short-wave spectral information (model IV in Table 1) or partial prior constraints on cloud fraction. Please refer to Figures 4-6 and 9 in the paper for details. However, if the prior information of cloud scattering properties or cloud fraction can be obtained, other planetary parameters can be well-constrained. Please refer to Table 3 and Figure 13 in the paper for details. Fig. 1 Influence of wavelength-dependent surface albedo and cloud scattering properties on parameter estimation Due to the difference of wavelength-dependent albedo between ocean, bare land and green land, the model can not only constrain the 14 parameters given above, but also give some restrictions on planetary surface types. The research was supported by the general project (No.11773045, No.11973009) and the key project (No.11933005) of the National Natural Science Foundation of China. |
