论文标题
Astromer：用于表示光曲线的基于变压器的嵌入
ASTROMER: A transformer-based embedding for the representation of light curves
论文作者
论文摘要
从自然语言嵌入中汲取灵感，我们提出了Astromer，这是一种基于变压器的模型，以创建光曲线的表示。 Astromer以一种自制的方式进行了预训练，不需要人类标记的数据。我们使用数百万的R波段光序列来调节丝灵重。通过对新来源重新训练Astromer，可以轻松地将学习的表示形式适应其他调查。 Astromer的功率包括使用表示形式提取可以增强其他模型（例如分类器或回归器）训练的光曲线嵌入。例如，我们使用Astromer嵌入来训练两个基于神经的分类器，这些分类器使用Macho，Ogle-III和Atlas的标记变为星星的分类器。在所有实验中，基于Astromer的分类器在有限的标记数据可用时直接在光曲线上训练的基线复发网络的表现优于基线复发网络。此外，使用Astromer嵌入会减少所需的计算资源，同时获得最新的结果。最后，我们提供了一个Python库，其中包括本工作中使用的所有功能。库，主代码和预训练的权重可在https://github.com/astromer-science上找到
Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to other surveys by re-training ASTROMER on new sources. The power of ASTROMER consists of using the representation to extract light curve embeddings that can enhance the training of other models, such as classifiers or regressors. As an example, we used ASTROMER embeddings to train two neural-based classifiers that use labeled variable stars from MACHO, OGLE-III, and ATLAS. In all experiments, ASTROMER-based classifiers outperformed a baseline recurrent neural network trained on light curves directly when limited labeled data was available. Furthermore, using ASTROMER embeddings decreases computational resources needed while achieving state-of-the-art results. Finally, we provide a Python library that includes all the functionalities employed in this work. The library, main code, and pre-trained weights are available at https://github.com/astromer-science
