论文标题
通过选择特征选择控制核性质的外推
Controlling extrapolations of nuclear properties with feature selection
论文作者
论文摘要
远离测量数据的核特性的预测本质上是不精确的，因为我们对核力量的了解以及我们在强烈相互作用的系统中对量子多体效应的处理。虽然当可用实验数据时可以直接计算模型偏差，但只能在没有此类测量的情况下进行估计。当输入变量（如质子或中子数）被推断出来，导致在核合成模拟等应用中导致不受控制的不确定性时，当前计算估计偏差的方法很快就会失去预测能力。在这封信中，我们提出了一种新型技术，可以识别机器学习算法的输入变量，该变量可以提供强大的模型偏差估计值。我们的过程基于选择输入变量或特征，基于其概率分布在整个核图表中的函数。我们说明了关于用密度功能理论（DFT）计算出的核结合能中模型偏差的问题的方法。我们表明，特征选择可以系统地改善理论预测而不会增加不确定性。
Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when input variables such as proton or neutron number are extrapolated, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We show that feature selection can systematically improve theoretical predictions without increasing uncertainties.
