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Astrophysical light curves are particularly challenging data objects due tothe intensity and variety of noise contaminating them. Yet, despite theastronomical volumes of light curves available, the majority of algorithms usedto process them are still operating on a per-sample basis. To remedy this, wepropose a simple Transformer model -- called Denoising Time Series Transformer(DTST) -- and show that it excels at removing the noise and outliers indatasets of time series when trained with a masked objective, even when noclean targets are available. Moreover, the use of self-attention enables richand illustrative queries into the learned representations. We presentexperiments on real stellar light curves from the Transiting Exoplanet SpaceSatellite (TESS), showing advantages of our approach compared to traditionaldenoising techniques.

Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer