论文标题
通过梯度增强决策树来估算本地群体中的质量
Estimation of the masses in the Local Group by Gradient Boosted Decision Trees
论文作者
论文摘要
我们的目标是估计本地群体（LG）及其主要星系的各个质量，即M31和银河系（MW）。我们通过有监督的机器学习算法，梯度增强决策树（GBDT）以及使用两者作为输入参数的观察到的距离和相对速度来执行此操作。 GBDT应用于从一组5个暗物质（DM）模拟的2148模拟LGS样品中，在标准$λ$ CDM \ CDM \ cesmological模型中运行。模拟LGS的选择由LG模型指导，该模型定义了此类对象。通过将模型应用于模拟LGS对的合奏来测量输入参数的观察不确定性的作用，其可观察到的这些输入参数受其相应的观察误差的影响。最后，实际LG的观察数据用于推断其相关质量。我们的主要结果是MW和M31：$ M_ {tot} = 3.31 ^{+0.79} _ { - 0.67} $，$ m_ {mw} = 1.15 ^{+0.25} _ {+0.25} _ { - 0.22} $和0.22} $和0.22} $和和和$ m_ {m31} = 2.01^{+0.65} _ { - 0.39} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ odot} $（对应于中位数和第1和第三夸脱）。质量的比例为$ M_ {M31}/M_ {MW} = 1.75^{+0.54} _ { - 0.28} $，其中按照惯例将M31定义为在此更大。
Our goal is to estimate the mass of the Local Group (LG) and the individual masses of its primary galaxies, the M31 and the Milky Way (MW). We do this by means of a supervised machine learning algorithm, the gradient boosted decision trees (GBDT) and using the observed distance and relative velocity of the two as input parameters. The GBDT is applied to a sample of 2148 mock LGs drawn from a set of 5 dark matter (DM)-only simulations, ran within the standard $Λ$CDM\ cosmological model. The selection of the mock LGs is guided by a LG model, which defines such objects. The role of the observational uncertainties of the input parameters is gauged by applying the model to an ensemble of mock LGs pairs whose observables are these input parameters perturbed by their corresponding observational errors. Finally the observational data of the actual LG is used to infer its relevant masses. Our main results are the sum and the individual masses of the MW and M31: $M_{tot} = 3.31 ^{+0.79}_{-0.67} $, $M_{MW}=1.15^{+0.25}_{-0.22}$ and $M_{M31}=2.01^{+0.65}_{-0.39} \ \ \times 10^{12}M_{\odot}$ (corresponding to the median and the 1st and 3rd quartiles). The ratio of the masses is $M_{M31}/M_{MW}=1.75^{+0.54}_{-0.28}$, where by convention the M31 is defined here to be the more massive of the two halos.
