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.

我们开发了一个深度神经网络 (DNN)，用于获取全天自动超新星巡天 (ASAS-SN) 中饱和恒星的光度。该 DNN 可以在 g=4 到 14 mag 之间获得无偏光度，饱和 (g<11.5 mag) 恒星的离散度 (15%-85% 中位数附近的 1sigma 范围) 为 0.12 mag。更重要的是，非变化的饱和恒星的光变曲线仅具有中位数离散度为 0.037 mag。DNN 的光变曲线在许多情况下明显优于标准 ASAS-SN 管道所提供的曲线。虽然该网络仅使用 ASAS-SN 的 20 个相机之一的 g 波段数据进行训练，但初步实验表明其也适用于任何相机和早期 ASAS-SN 的 V 波段数据。主要问题似乎与 ASAS-SN 数据降噪管道中关于饱和恒星的可纠正问题有关，而非 DNN 本身。该方法已作为全天自动超新星巡天 Sky Patrol v1.0 上的光变曲线选项公开提供。

用机器学习进行饱和星体的光度测量

Photometry of Saturated Stars with Machine Learning

Exoplanet discovery at long orbital periods requires reliably detecting
individual transits without additional information about the system. Techniques
like phase-folding of light curves and periodogram analysis of radial velocity
data are more sensitive to planets with shorter orbital periods, leaving a
dearth of planet discoveries at long periods. We present a novel technique
using an ensemble of Convolutional Neural Networks incorporating the onboard
spacecraft diagnostics of \emph{Kepler} to classify transits within a light
curve. We create a pipeline to recover the location of individual transits, and
the period of the orbiting planet, which maintains $>80\%$ transit recovery
sensitivity out to an 800-day orbital period. Our neural network pipeline has
the potential to discover additional planets in the \emph{Kepler} dataset, and
crucially, within the $\eta$-Earth regime. We report our first candidate from
this pipeline, KOI 1271.02. KOI 1271.01 is known to exhibit strong Transit
Timing Variations (TTVs), and so we jointly model the TTVs and transits of both
transiting planets to constrain the orbital configuration and planetary
parameters and conclude with a series of potential parameters for KOI 1271.02,
as there is not enough data currently to uniquely constrain the system. We
conclude that KOI 1271.02 has a radius of 5.32 $\pm$ 0.20 $R_{\oplus}$ and a
mass of $28.94^{0.23}_{-0.47}$ $M_{\oplus}$. Future constraints on the nature
of KOI 1271.02 require measuring additional TTVs of KOI 1271.01 or observing a
second transit of KOI 1271.02.

通过使用一组卷积神经网络和开普勒宇宙飞船的诊断工具，我们提出了一种新的技术，可以在光度曲线中对行星凌星进行分类，并且可以保持高达 80％的准确度，对高达 800 天的轨道周期的行星凌星进行定位和周期恢复。