In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-m Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching r_AB ≈ 22.5 mag. We run our simulations with the software snmachine, a photometric classification pipeline using machine learning. The machine-learning algorithms struggle to classify supernovae when the training sample is magnitude limited, in contrast to representative training samples. Classification performance noticeably improves when we combine the magnitude-limited training sample with a simulated realistic sample of faint high-redshift supernovae observed from larger spectroscopic facilities; the algorithms’ range of average area under receiver operator characteristic curve (AUC) scores over 10 runs increases from 0.547–0.628 to 0.946–0.969 and purity of the classified sample reaches 95 per cent in all runs for two of the four algorithms. By creating new, artificial light curves using the augmentation software avocado, we achieve a purity in our classified sample of 95 per cent in all 10 runs performed for all machine-learning algorithms considered. We also reach a highest average AUC score of 0.986 with the artificial neural network algorithm. Having ‘true’ faint supernovae to complement our magnitude-limited sample is a crucial requirement in optimization of a 4MOST spectroscopic sample. However, our results are a proof of concept that augmentation is also necessary to achieve the best classification results.

中文翻译：

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优化用于超新星光度分类的幅度受限光谱训练样本

为了准备来自传统时空调查 (LSST) 的瞬态光度学分类，我们使用不同的训练数据集运行测试。使用4 米多目标光谱望远镜( 4MOST ) 时域河外巡天 (TiDES) 可以对瞬变进行分类的深度的估计，我们模拟了达到r _AB ≈ 22.5 mag的震级限制样本。我们使用软件snmachine运行我们的模拟，使用机器学习的光度分类管道。与具有代表性的训练样本相比，当训练样本数量有限时，机器学习算法很难对超新星进行分类。当我们将受震级限制的训练样本与从较大光谱设施观察到的微弱高红移超新星的模拟真实样本相结合时，分类性能显着提高；算法在 10 次运行中的接受者操作特征曲线 (AUC) 得分下的平均面积范围从 0.547–0.628 增加到 0.946–0.969，并且对于四种算法中的两种算法，分类样本的纯度在所有运行中均达到 95%。通过使用增强软件avocado创建新的人造光曲线，我们在为所有机器学习算法执行的所有 10 次运行中实现了 95% 的分类样本纯度。我们还使用人工神经网络算法达到了 0.986 的最高平均 AUC 分数。拥有“真正的”微弱超新星来补充我们的震级有限样本是优化4MOST光谱样本的关键要求。然而，我们的结果证明了增强也是实现最佳分类结果所必需的概念证明。