论文标题
不要注意噪音：通过Denoising Time序列变压器学习光曲线的自我监督表示
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer
论文作者
论文摘要
天体物理光曲线尤其具有挑战性的数据对象，因为它们的强度和噪音污染了它们。然而，尽管可用的光曲线有天文数量，但用于处理它们的大多数算法仍在按样本基础上运行。为了解决这个问题，我们提出了一个简单的变压器模型 - 称为Denoising时间序列变压器（DTST） - 并表明它在接受掩盖目标训练时，即使没有可用的目标，它也可以在时间序列数据集中删除噪声和离群值。此外，自我发作的使用可以使丰富而说明性的查询成为学习的表示形式。我们介绍了从过渡系外行星卫星（TESS）的真实恒星光曲线进行的实验，与传统的Denoising技术相比，我们的方法具有优势。
Astrophysical light curves are particularly challenging data objects due to the intensity and variety of noise contaminating them. Yet, despite the astronomical volumes of light curves available, the majority of algorithms used to process them are still operating on a per-sample basis. To remedy this, we propose a simple Transformer model -- called Denoising Time Series Transformer (DTST) -- and show that it excels at removing the noise and outliers in datasets of time series when trained with a masked objective, even when no clean targets are available. Moreover, the use of self-attention enables rich and illustrative queries into the learned representations. We present experiments on real stellar light curves from the Transiting Exoplanet Space Satellite (TESS), showing advantages of our approach compared to traditional denoising techniques.
