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首页> 外文期刊>The Astrophysical journal >NOVEL METHODS FOR PREDICTING PHOTOMETRIC REDSHIFTS FROM BROADBAND PHOTOMETRY USING VIRTUAL SENSORS
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NOVEL METHODS FOR PREDICTING PHOTOMETRIC REDSHIFTS FROM BROADBAND PHOTOMETRY USING VIRTUAL SENSORS

机译：虚拟传感器的宽带光度法预测光度红移的新方法

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摘要

We calculate photometric redshifts from the Sloan Digital Sky Survey Main Galaxy Sample, the Galaxy Evolution Explorer All Sky Survey, and the Two Micron All Sky Survey using two new training-set methods. We utilize the broadband photometry from the three surveys alongside Sloan Digital Sky Survey measures of photometric quality and galaxy morphology. Our first training-set method draws from the theory of ensemble learning while the second employs Gaussian process regression, both of which allow for the estimation of redshift along with a measure of uncertainty in the estimation. The Gaussian process models the data very effectively with small training samples of approximately 1000 points or less. These two methods are compared to a well-known artificial neural network training-set method and to simple linear and quadratic regression. We also demonstrate the need to provide confidence bands on the error estimation made by both classes of models. Our results indicate that variations due to the optimization procedure used for almost all neural networks, combined with the variations due to the data sample, can produce models with variations in accuracy that span an order of magnitude. A key contribution of this paper is to quantify the variability in the quality of results as a function of model and training sample. We show how simply choosing the "best" model given a data set and model class can produce misleading results.
机译：我们使用两种新的训练集方法，从斯隆数字天空调查主要Galaxy样本，Galaxy Evolution资源管理器“所有天空调查”和“两个微米全天空调查”计算光度红移。我们利用这三项调查中的宽带光度法以及斯隆数字天空调查法中的光度质量和星系形态来进行测量。我们的第一个训练集方法借鉴了集成学习的理论，而第二个方法则采用了高斯过程回归，这两种方法都可以对红移进行估计，并可以对估计中的不确定性进行度量。高斯过程使用大约1000点或更少的小训练样本非常有效地对数据建模。将这两种方法与著名的人工神经网络训练集方法以及简单的线性和二次回归进行了比较。我们还证明了需要为两类模型所做的误差估计提供置信带。我们的结果表明，由于几乎所有神经网络都使用了优化程序而导致的变化，再加上由于数据样本而产生的变化，可以生成精度跨越一个数量级的模型。本文的关键贡献在于量化结果质量的可变性，该可变性是模型和训练样本的函数。我们展示了在给定数据集和模型类别的情况下，简单地选择“最佳”模型如何会产生误导性的结果。

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