论文标题
通才,自动苜蓿巴属tully-fisher关系
A Generalist, Automated ALFALFA Baryonic Tully-Fisher Relation
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
The Baryonic Tully-Fisher Relation (BTFR) has applications in galaxy evolution as a testbed for the galaxy-halo connection and in observational cosmology as a redshift-independent secondary distance indicator. We use the 31,000+ galaxy ALFALFA sample -- which provides redshifts, velocity widths, and HI content for a large number of gas-bearing galaxies in the local universe -- to fit and test an extensive local universe BTFR. This BTFR is designed to be as inclusive of ALFALFA and comparable samples as possible. Velocity widths measured via an automated method and $M_{b}$ proxies extracted from survey data can be uniformly and efficiently measured for other samples, giving this analysis broad applicability. We also investigate the role of sample demographics in determining the best-fit relation. We find that the best-fit relations are changed significantly by changes to the sample mass range and to second order, mass sampling, gas fraction, different stellar mass and velocity width measurements. We use a subset of ALFALFA with demographics that reflect the full sample to measure a robust BTFR slope of $3.30\pm0.06$. We apply this relation and estimate source distances, finding general agreement with flow-model distances as well as average distance uncertainties of $\sim0.17$ dex for the full ALFALFA sample. We demonstrate the utility of these distance estimates by applying them to a sample of sources in the Virgo vicinity, recovering signatures of infall consistent with previous work.