论文标题
多频带光曲线的深度注意的超新星分类
Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
论文作者
论文摘要
与其他类别的可变事件相比，在天文学调查（例如Zwicky瞬态设施）中，超新星（SNE）是相对罕见的对象。由于这种稀缺性，多波段灯犬的处理是一项具有挑战性的任务，因为高度不规则的节奏，长时间的差距，缺失值，几乎没有观察到的观察等。这些问题尤其有害于对瞬态事件的分析：SN样光曲线。我们提供三个主要贡献：1）基于时间调制和注意机制，我们提出了一个深切的注意模型（Timemodattn），以对不同SN类型的多波段光曲线进行分类，避免光度计算或手工制作的特征计算，缺失值假设，并进行明确的插入/插入方法。 2）我们提出了一个基于超新星参数模型合成生成SN多波段光曲线的模型，使我们能够增加样品数量和节奏的多样性。因此，首先，使用合成光曲犬对Timemodattn模型进行了预训练。然后，进行微调过程。 Timemodattn模型在两种情况下，基于复发性神经网络的其他深度学习模型优于其他深度学习模型：晚期分类和早期分类。此外，TimeModattn模型的表现优于平衡的随机森林（BRF）分类器（经过真实数据培训），将平衡的-F_1 $得分从$ \ \ \ $ \ 525 $提高到$ \ $ \ off.596 $。当使用合成数据训练BRF时，该模型的性能与提出的Timemodattn模型相似，同时仍保持额外的优势。 3）我们进行了可解释性实验。比更早的观测值比SN亮度峰值提前并获得了高度注意力。这也与学习的时间调制的早期高度可变性有关。
In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light-curves. We offer three main contributions: 1) Based on temporal modulation and attention mechanisms, we propose a Deep attention model (TimeModAttn) to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. 2) We propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pre-trained using synthetic light-curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other Deep Learning models, based on Recurrent Neural Networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-$F_1$score from $\approx.525$ to $\approx.596$. When training the BRF with synthetic data, this model achieved similar performance to the TimeModAttn model proposed while still maintaining extra advantages. 3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.
