用贝叶斯方法分类天文光谱并评估分类不确定性
Bayesian classification of astronomical spectra with class uncertainties
这是关于如何用机器学习分类天文光谱的论文，作者测试了四种方法，最后推荐了在CNN上使用蒙特卡洛 dropout 的方案，准确率最高，还能提供不确定性。
我们开发了一种概率机器学习方法，用于对恒星和类星体光谱进行10类以上的分类，该方法能表达输入数据和预测中的不确定性。在SDSS数据库和4MOST模拟数据集上训练了四种方法（卷积神经网络、狄利克雷分布、蒙特卡洛 dropout 和贝叶斯神经网络+变分推断），比较了准确率、AUC、ECE等指标。结果显示，在CNN上使用蒙特卡洛 dropout 的方法准确率最高（93.9%），同时能提供校准良好的不确定性。
Bayesian classification of astronomical spectra with class uncertainties
Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.