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An algorithm for the fitting of planet models to Kepler light curves

机译:行星模型与开普勒光曲线拟合的算法

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We describe an algorithm which fits model planetary system parameters to light curves from Kepler Mission target stars. The algorithm begins by producing an initial model of the system which is used to seed the fit, with particular emphasis on obtaining good transit timing parameters. An attempt is then made to determine whether the observed transits are more likely due to a planet or an eclipsing binary. In the event that the transits are consistent with a transiting planet, an iterative fitting process is initiated: a wavelet-based whitening filter is used to eliminate stellar variations on timescales long compared to a transit; a robust nonlinear fitter operating on the whitened light curve produces a new model of the system; and the procedure iterates until convergence upon a self-consistent whitening filter and planet model. The fitted transits are removed from the light curve and a search for additional planet candidates is performed upon the residual light curve. The fitted models are used in additional tests which identify false positive planet detections: multiple planet candidates with near-identical fitted periods are far more likely to be an eclipsing binary, for example, while target stars in which the model light curve is correlated with the star centroid position may indicate a background eclipsing binary, and subtraction of all model planet candidates yields a light curve of pure noise and stellar variability, which can be used to study the probability that the planet candidates result from statistical fluctuations in the data.
机译:我们描述了一种算法,该算法适合模型行星系统参数以适应开普勒任务目标恒星的光曲线。该算法首先生成系统的初始模型,该初始模型用于为拟合提供种子,特别着重于获取良好的运输时间参数。然后尝试确定观察到的越界是否更有可能是由于行星还是日食双星。如果过境与过境行星一致,则将启动迭代拟合过程:与小过境相比,基于小波的白化滤波器可消除时标上的恒星变化;在白光曲线上运行的稳健的非线性拟合器产生了该系统的新模型;并重复该过程,直到收敛到自洽的白化滤镜和行星模型为止。从光曲线中去除拟合的过境,并在剩余光曲线上搜索其他候选行星。拟合的模型用于其他测试中,这些测试可识别虚假的阳性行星检测:例如,具有近似相同拟合周期的多个候选行星更可能是黯淡的双星,例如,目标恒星中模型光曲线与恒星相关星形质心位置可能表示背景黯淡,所有星系候选者相减会产生纯噪声和恒星变异性的光曲线,可用于研究星系候选者由数据统计波动引起的概率。

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