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。