星系在我們理解宇宙結構形成的過程中扮演著至關重要的角色。對宇宙學或星系形成的任何精密研究都需要大量模擬的星系目錄。鑒於這一艱巨任務，也因為從完整的流體力學模擬或半解析模型中生成目錄過於昂貴，能夠直接將暗物質晕與星系進行對應的方法備受青睞。本論文提出了兩種模型，結合暗物質晕在只有暗物質的模擬中的性質，可以準確預測IllustrisTNG300（TNG300）模擬中關鍵的星系性質。

第一部分，我們提出了一種新的亞暗物質晕豐度匹配（SHAM）方案，可以用僅三個自由參數準確預測恆星質量和質量相關兩點相關函數。本質上，SHAM框架是找到與星系屬性（如恆星質量）最相關的暗物質晕屬性。 Tonnesen & Ostriker (2021) 最近的一項研究表明，通過結合兩個暗物質晕屬性，一種名為φ的新屬性能夠比先前的SHAM方案更好地預測恆星質量。受到他們方法的啟發，我們發現引入一個名為ψ5的暗物質暈屬性可以進一步提高預測準確性。基於TNG300模擬的測試表明，我們的新SHAM方案可以在$z=0-2$的紅移範圍內，分別比φ改進15%的恆星質量和18%的質量相關兩點相關函數的預測。

第二部分，我們提出了一種基於圖神經網絡（GNN）的模型，能夠藉由暗物質暈的合成歷史準確預測恆星質量、g-r顏色、恆星形成率（SFR）、氣體質量、恆星金屬豐度和氣體金屬豐度。基於TNG300模擬的測試表明，我們的模型可以在廣泛的紅移範圍（z=0-2）內高精度地重現所有星系及其元星系的重子屬性，並較現有最先進的方法有顯著改進。

在這篇論文中，我們展示了我們的方法在理解IllustrisTNG模擬的星系形成模型方面取得了實質性進展。

Galaxies play a crucial role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs. For such a daunting task, methods that can produce a direct mapping between dark matter halos and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical (HD) simulations or semi-analytical models (SAMs) is too expensive. Here we present two models that can accurately predict key galaxies' properties in the IllustrisTNG300 (TNG300) with dark matter properties from halos in the corresponding dark matter-only (DMO) simulation.

In Part I, we present a new Subhalo abundance matching (SHAM) scheme, which can accurately predict the stellar mass and mass-dependent clustering with just three free parameters. In essence, the SHAM framework is to find a dark matter halo property that best correlates with an attribute of galaxies, such as stellar mass. A recent study by Tonnesen & Ostriker (2021) suggested that by combining two dark matter halo properties, a new quantity φ performs better in predicting stellar mass than previous SHAM schemes. Inspired by their approach, we find that further improvement can be achieved by a quantity ψ5. Tests based on the TNG300 simulation show that our new SHAM scheme can improve the stellar mass prediction and mass-dependent clustering by 15% and 18% from φ, respectively, over the redshift range z=0-2.

In Part II, we present a Graph Neural Network (GNN)-based model that is able to accurately predict stellar mass, g-r color, star formation rate (SFR), gas mass, stellar metallicity, and gas metallicity, from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range (z = 0-2), for all galaxies and their progenitors, with strong improvements over the state-of-the-art methods.

We show that our methods make substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model.

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