论文标题
一个自校准的基于光晕的星系组查找器：算法和测试
A Self-Calibrating Halo-Based Galaxy Group Finder: Algorithm and Tests
论文作者
论文摘要
我们描述了基于光环的星系组找到算法的扩展。我们为算法增添了自由度，以更准确地确定哪些星系是中心的，哪些是卫星，并提供对光环质量的无偏估计。我们专注于确定星系和静态星系的星系 - 哈洛关系。通过观察颜色依赖性星系聚类的观察以及对堆叠的光谱中央星系堆叠样品的深度成像数据中总卫星光度的测量，通过观察到颜色依赖的星系聚类的观察以及测量总卫星光度的测量，从而自我校准了组发现算法的额外自由度。我们在一系列的模拟中测试了这种方法，该模拟会改变星系 - 霍洛连接，其中包括一种由Universemachine结果构建的模拟。我们的自校准算法在估计星系的颜色依赖性卫星部分方面显示出比以前的方法明显改善。它使中央星系中的log m_halo中的误差降低了两个以上，至<〜0.2 dex。通过L_SAT数据，它可以量化恒星形成和静态星系的光度至中心质量关系的差异，即使对于只有一个光谱成员的组也是如此。尽管以前的算法不能在固定的M_halo处限制L_GAL中的散射，但自校准技术可以为此散射提供强大的下限。
We describe an extension of the halo-based galaxy group-finding algorithm. We add freedom to the algorithm in order to more accurately determine which galaxies are central and which are satellites, and to provide unbiased estimates of halo masses. We focus on determination of the galaxy-halo relations for star-forming and quiescent galaxies. The added freedom in the group-finding algorithm is self-calibrated using observations of color-dependent galaxy clustering, as well as measurements of the total satellite luminosity in deep imaging data around stacked samples of spectroscopic central galaxies, L_sat. We test this approach on a series of mocks that vary the galaxy-halo connection, including one mock constructed from UniverseMachine results. Our self-calibrated algorithm shows marked improvement over previous methods in estimating the color-dependent satellite fraction of galaxies. It reduces the error in log M_halo for central galaxies by over a factor of two, to <~0.2 dex. Through the L_sat data, it can quantify differences in the luminosity-to-halo mass relations for star-forming and quiescent galaxies, even for groups with only one spectroscopic member. Whereas previous algorithms cannot constrain the scatter in L_gal at fixed M_halo, the self-calibration technique can provide a robust lower limit to this scatter.
