论文标题
用长短期内存网络逐渐降低外球星电灯曲线
Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks
论文作者
论文摘要
从过境光曲线的过境深度的精确推导是测量系外行驶传输光谱的关键组成部分，此后用于研究系外行星大气。但是，它仍然受到各种系统的错误和噪声的深刻影响。在本文中，我们提出了一种新的分解方法，通过在运输时间内重建恒星通量基线。我们训练一个概率长的短期内存（LSTM）网络，以预测过渡外光曲线的下一个数据点，并使用此模型重建无运输的光曲线 - 即在渗透期间仅包含系统。通过不对仪器进行任何假设，只使用过境世代，这提供了一种纠正系统和执行随后的过境拟合的一般方法。所提出的模型的名称是TLCD-LSTM，代表过境光曲线降低LSTM。在这里，我们在Spitzer Space望远镜上使用IRAC摄像头的HD 189733B的六个运输观测值介绍了第一个结果，并讨论了其一些可能的进一步应用。
The precise derivation of transit depths from transit light curves is a key component for measuring exoplanet transit spectra, and henceforth for the study of exoplanet atmospheres. However, it is still deeply affected by various kinds of systematic errors and noise. In this paper we propose a new detrending method by reconstructing the stellar flux baseline during transit time. We train a probabilistic Long Short-Term Memory (LSTM) network to predict the next data point of the light curve during the out-of-transit, and use this model to reconstruct a transit-free light curve - i.e. including only the systematics - during the in-transit. By making no assumption about the instrument, and using only the transit ephemeris, this provides a general way to correct the systematics and perform a subsequent transit fit. The name of the proposed model is TLCD-LSTM, standing for Transit Light Curve Detrending LSTM. Here we present the first results on data from six transit observations of HD 189733b with the IRAC camera on board the Spitzer Space Telescope, and discuss some of its possible further applications.
