In astronomical surveys, such as the Zwicky Transient Facility (ZTF), 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, low number of observations, etc. These issues are particularly detrimental for the analysis of transient events with SN-like light-curves. In this work, we offer three main contributions. First, based on temporal modulation and attention mechanisms, we propose a Deep Attention model called TimeModAttn to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-values assumptions, and explicit imputation and interpolation methods. Second, we propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model (SPM). This allows us to increase the number of samples and the diversity of the cadence. The TimeModAttn model is first pre-trained using synthetic light-curves in a semi-supervised learning scheme. Then, a fine-tuning process is performed for domain adaptation. The proposed TimeModAttn model outperformed a Random Forest classifier, increasing the balanced-$F_1$score from $\approx.525$ to $\approx.596$. The TimeModAttn model also outperformed other Deep Learning models, based on Recurrent Neural Networks (RNNs), in two scenarios: late-classification and early-classification. Finally, we conduct interpretability experiments. High attention scores are obtained for observations earlier than and close to the SN brightness peaks, which are supported by an early and highly expressive learned temporal modulation.
翻译:在天文测量中,例如 Zwicky Transition 设施(ZTF),超新星(Sne) 与其他各类变异事件相比,是相对罕见的物体。除了这种稀缺之外,多波段光曲线的处理是一项具有挑战性的任务,原因是高度不规则的上升、长期差距、缺失值、观测次数少等等。这些问题特别不利于用类似SN的光曲线分析瞬时事件。在这项工作中,我们提供了三大贡献。首先,基于时间流调控和关注机制,我们提议了一个叫TimeModAtten的深调模型,用于对不同SN型的多波段光曲线进行分类,避免光度或手工制作的特征计算、缺失值假设以及明确的内插和内插方法。第二,我们提议了一个基于Spernova 光度模型的合成生成多波段光度光度光度光度光度(SMMMDR) 。我们也可以在最接近的内嵌值上增加样本数量和多样性。 IMFA- more-ral-ral-deal-deal IMold IMold IMold IMold IMest IMest IMest IMdeal rodeal rode rodeal rode rodeal rodemodeal rodeal lautisal a la mode mode la mode mode la mode la la model model mode mode mode la modealtistr model model la mode la modeal la mode model la la modelmental la la la la model la la model model model model la la la la la model la la la la model model model model model model model model model la la la la la la la la la la model model model modelal model la la la la la model