光变曲线的多波段嵌入
Multiband Embeddings of Light Curves
AI总结：
本文提出一种无需复杂特征且不依赖完整光变曲线的RNN集成模型，可适应多波段数据，在Gaia、Pan-STARRS1和ZTF数据集上展现优异性能，并支持物理参数回归，为早期分类和多任务拓展提供有力工具。
摘要：
在这项工作中，我们提出了一种新颖的循环神经网络（RNN）集成方法，该方法无需计算复杂特征即可考虑多波段和非均匀观测节奏。我们提出的模型由RNN集成组成，其推理过程不需要完整的光变曲线，从而使推理过程更加简单。该集成能够适应不同数量的波段，并在三个真实光变曲线数据集（即Gaia、Pan-STARRS1和ZTF）上进行了测试，以展示其泛化潜力。我们还展示了深度学习不仅能够进行分类，还能对有效温度和半径等物理参数进行回归的能力。我们的集成模型在观测次数较少的情况下表现出优越的性能，从而为来自诸如Vera C. Rubin天文台LSST等设施的光源早期分类提供了潜力。结果凸显了该模型的有效性和灵活性，使其成为未来天文巡天的一个有前景的工具。我们的研究表明，多任务学习方法可以丰富模型获得的嵌入，使其有助于解决额外任务，例如确定双星系统的轨道参数或估算周期型以外天体类型的参数。
英文摘要：
In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our proposed model consists of an ensemble of RNNs, which do not require the entire light curve to perform inference, making the inference process simpler. The ensemble is able to adapt to varying numbers of bands, tested on three real light curve datasets, namely Gaia, Pan-STARRS1, and ZTF, to demonstrate its potential for generalization. We also show the capabilities of deep learning to perform not only classification, but also regression of physical parameters such as effective temperature and radius. Our ensemble model demonstrates superior performance in scenarios with fewer observations, thus providing potential for early classification of sources from facilities such as Vera C. Rubin Observatory's LSST. The results underline the model's effectiveness and flexibility, making it a promising tool for future astronomical surveys. Our research has shown that a multitask learning approach can enrich the embeddings obtained by the models, making them instrumental to solve additional tasks, such as determining the orbital parameters of binary systems or estimating parameters for object types beyond periodic ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
