A comparison of approaches in fitting continuum SEDs

作     者：Yao Liu David Madlener Sebastian Wolf Hong-Chi Wang 

作者机构：Purple Mountain Observatory and Key Laboratory for Radio AstronomyChinese Academy of Sciences Graduate University of Chinese Academy of Sciences Institut fr Theoretische Physik und AstrophysikUniversitt zu Kiel 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2013年第13卷第4期

页      面：420-434页

核心收录：

学科分类：0709[理学-地质学] 07[理学] 0708[理学-地球物理学] 070401[理学-天体物理] 0704[理学-天文学] 0825[工学-航空宇航科学与技术] 

基　　金：support by the German Academic Exchange Service. H.W. support by the National Natural Science Foundation of China (Grant Nos. 10733030,10921063 and 11173060) 

主　　题：methods numerical   radiative transfer   protoplanetary disks 

摘      要：We present a detailed comparison of two approaches, the use of a precalculated database and simulated annealing （SA）, for fitting the continuum spectral energy distribution （SED） of astrophysical objects whose appearance is dominated by surrounding dust. While pre-calculated databases are commonly used to model SED data, only a few studies to date employed SA due to its unclear accuracy and convergence time for this specific problem. From a methodological point of view, different approaches lead to different fitting quality, demand on computational resources and calculation time. We compare the fitting quality and computational costs of these two approaches for the task of SED fitting to provide a guide to the practitioner to find a compromise between desired accuracy and available resources. To reduce uncertainties inherent to real datasets, we introduce a reference model resembling a typical circumstellar system with 10 free parameters. We derive the SED of the reference model with our code MC3 D at 78 logarithmically distributed wavelengths in the range [0.3 μm, 1.3 mini and use this setup to simulate SEDs for the database and SA. Our result directly demonstrates the applicability of SA in the field of SED modeling, since the algorithm regularly finds better solutions to the optimization problem than a precalculated database. As both methods have advantages and shortcomings, a hybrid approach is preferable. While the database provides an approximate fit and overall probability distributions for all parameters deduced using Bayesian analysis, SA can be used to improve upon the results returned by the model grid.