Abstract
We develop a deep neural network (DNN) to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The DNN can obtain unbiased photometry for stars from g=4 to 14 mag with a dispersion (15%-85% 1sigma range around median) of 0.12 mag for saturated (g<11.5 mag) stars. More importantly, the light curve of a non-variable saturated star has a median dispersion of only 0.037 mag. The DNN light curves are, in many cases, spectacularly better than provided by the standard ASAS-SN pipelines. While the network was trained on g band data from only one of ASAS-SN's 20 cameras, initial experiments suggest that it can be used for any camera and the older ASAS-SN V band data as well. The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars more than the DNN itself. The method is publicly available as a light curve option on ASAS-SN Sky Patrol v1.0.
Abstract (translated)
我们开发了一个深度神经网络（DNN）用于获取全星自动 survey for Supernovae (ASAS-SN) 中饱和恒星的光度测量值。该 DNN 可以获得从 g=4 到 14 mag 的恒星的不带偏差光度测量值，光度（在均值附近，15%-85% 的 1σ 范围）为 0.12 mag。更重要的是，非变星的饱和恒星的光曲线具有仅 0.037 mag 的均方差。DNN 光曲线在许多情况下比 ASAS-SN 标准管道提供的更令人印象深刻。虽然该网络仅在 ASAS-SN 的 20 个相机上训练，但初始实验结果表明，它可以用于任何相机，甚至是更旧的 ASAS-SN V 带数据。主导问题似乎与 ASAS-SN 数据 reduction 管道中饱和恒星的数据有关，而不是与 DNN 本身有关。该方法作为 ASAS-SN Sky Patrol v1.0 上的光度选项是公开的。
URL
https://arxiv.org/abs/2404.15405
