基于人工神经网络的中心网格淬火参数排序方法
An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies
摘要：
我们提出了一种新的技术排名的相对重要性的星系性质的淬火恒星形成的过程中。具体来说，我们开发了一个人工神经网络（ANN）的模式识别方法，并将其应用于人口超过40万个中央星系从斯隆数字巡天数据发布7。我们利用各种物理星系属性的训练模式识别算法，以识别恒星形成和被动系统，一个“训练集”的$\sim$ 100,000星系。然后，我们将ANN模型应用于$\sim$ 10万个不同星系的“验证集”，这些星系是从剩余样本中随机选择的。每个参数单独的成功率，并与其他参数一起，被视为一个指标的参数是多么重要的中央星系淬火的过程（ES）。我们发现，中心速度色散，隆起质量和B/T是很好的预测系统的被动状态，表明与星系的中心质量的属性是最密切相关的恒星形成的停止。较大尺度的星系性质（总质量或盘状恒星质量）或与环境有关的性质（晕质量或$δ_5$）表现得明显不太好。我们的结果是合理的解释AGN反馈驱动淬火的中央星系，虽然我们也讨论了其他的可能性。
英文摘要：
We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400,000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy properties for training the pattern recognition algorithm to recognise star forming and passive systems, for a `training set' of $\sim$100,000 galaxies. We then apply the ANN model to a `verification set' of $\sim$100,000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and B/T are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disk stellar masses), or those linked to environment (halo masses or $δ_5$) perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
