研究目的
To compare the optical morphologies of galaxies from the IllustrisTNG and original Illustris simulations with Pan-STARRS observations using synthetic images and non-parametric morphological diagnostics.
研究成果
The optical morphologies of IllustrisTNG galaxies are in good agreement with Pan-STARRS observations, representing an improvement over the original Illustris simulation. However, the IllustrisTNG model struggles with producing strong morphology-color and morphology-size relations, leading to higher fractions of red discs and blue spheroids and incorrect size trends compared to observations. Future work should explore higher resolution simulations and potential model adjustments.
研究不足
The study is limited to low-redshift galaxies (z≈0.05) and specific stellar mass range. The synthetic image generation is computationally intensive, and the simulations may not fully capture starbursts or nuclear gas inflows due to resolution constraints. The morphology-color and morphology-size relations in IllustrisTNG show discrepancies with observations.
1:Experimental Design and Method Selection:
The study uses hydrodynamic cosmological simulations (IllustrisTNG and Illustris) to generate synthetic images with the SKIRT radiative transfer code, including dust attenuation and scattering. The statmorph code is used for morphological analysis, calculating Gini-M20, CAS statistics, and Sérsic fits.
2:Sample Selection and Data Sources:
Observational data from Pan-STARRS for galaxies at z≈
3:05 with stellar masses log10(M?/M⊙)≈8–Simulated galaxies from IllustrisTNG and Illustris at z=0485 with M?>10^5 M⊙. List of Experimental Equipment and Materials:
SKIRT radiative transfer code, GALAXEV stellar population synthesis code, MAPPINGS-III photoionization code, statmorph Python package, Pan-STARRS telescope and camera.
4:Experimental Procedures and Operational Workflow:
Generate synthetic images with SKIRT or GALAXEV pipelines based on star-forming gas fraction. Post-process images with PSF convolution, noise addition, and segmentation. Analyze images with statmorph to compute morphological parameters.
5:Data Analysis Methods:
Compare median trends and scatter of morphological parameters between simulations and observations using statistical methods.
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