We present Cyclic-Permutation Invariant Neural Networks, a novel class of neural networks (NNs) designed to be invariant to phase shifts of period-folded periodic sequences by means of ‘symmetry padding’. In the context of periodic variable star light curves, initial phases are exogenous to the physical origin of the variability and should thus be immaterial to the downstream inference application. Although previous work utilizing NNs commonly operated on period-folded light curves, no approach to date has taken advantage of such a symmetry. Across three different data sets of variable star light curves, we show that two implementations of Cyclic-Permutation Invariant Networks—iTCN and iResNet—consistently outperform state-of-the-art non-invariant baselines and reduce overall error rates by between 4 to 22 per cent. Over a 10-class OGLE-III sample, the iTCN/iResNet achieves an average per-class accuracy of 93.4 per cent/93.3 per cent, compared to recurrent NN/random forest accuracies of 70.5 per cent/89.5 per cent in a recent study using the same data. Finding improvement on a non-astronomy benchmark, we suggest that the methodology introduced here should also be applicable to a wide range of science domains where periodic data abounds.

中文翻译：

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用新颖的循环置换不变神经网络分类周期变星

我们提出了循环置换不变神经网络，这是一类新颖的神经网络 (NN)，旨在通过“对称填充”对周期折叠周期序列的相移保持不变。在周期性变星光曲线的背景下，初始阶段对于变率的物理起源是外生的，因此对于下游推理应用应该是无关紧要的。尽管以前使用 NN 的工作通常在周期折叠光曲线上运行，但迄今为止还没有任何方法利用这种对称性。在三个不同的可变星光曲线数据集上，我们展示了循环置换不变网络的两种实现——iTCN 和 iResNet——始终优于最先进的非不变基线，并将总体错误率降低 4 到 22百分。超过 10 类 OGLE-III 样本，iTCN/iResNet 实现了 93.4%/93.3% 的平均每类准确率，而最近使用相同数据进行的一项研究中的循环 NN/随机森林准确率为 70.5%/89.5%。在非天文学基准上找到改进，我们建议这里介绍的方法也应该适用于周期性数据丰富的广泛科学领域。