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Exoplanet transit analysis using the point spread function

机译:使用点传播功能的Exoplanet过境分析

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The behavior of the point spread function could be analyzed in images of stars with known exoplanets. A transform plot of the log intensity versus the Gaussian or sinc function value was used in the analysis. The intercept was extrapolated for brightness information and the slope or adjacent log intensity difference was also fitted. The robustness of the algorithm was evaluated using a bootstrap statistical method that does not require the error to follow a Gaussian distribution. The bootstrap confidence interval was used to assess the changes in the signal before and during transit with simulations that include Gaussian errors as well as non-Gaussian ones. Simulations using Gaussian noise suggested that transit signal changes at about 1-sigma noise level are detectable with a 90% confidence interval, while non-Gaussian noise simulations suggested a lower confidence level. This bootstrap method and the standard Chi-square method used in the analysis of the point spread function of data taken with space telescopes such as Spitzer was compared. The impact of using under-sampled CCD observations with super-sampled point spread functions on the bootstrap analysis is discussed. The analysis of images such as HD 209548 (captured by our 16-inch telescope) suggested that the point spread function approach is comparable to the photometric approach but requires fewer images. The application to other images from telescopes such as Hubble is also discussed.
机译:点传播功能的行为可以在具有已知的外部产物的恒星的图像中分析。分析中使用了日志强度与高斯或SINC功能值的变换图。截距被推断出用于亮度信息,并且还安装了斜坡或相邻的日志强度差异。使用Bootstrap统计方法评估算法的稳健性,该方法不需要遵循高斯分布。 Bootstrap置信区间用于评估在传输之前和在传输过程中的信号中的变化,其中模拟包括高斯误差以及非高斯误差。使用高斯噪声的模拟表明,在大约1-sigma噪声水平下的传输信号变化是可检测到90%的置信区间,而非高斯噪声模拟表明置信水平较低。比较了这种自举方法和标准的Chi-Square方法,用于分析与SpitzeS等空间望远镜拍摄的数据的点传播功能。讨论了使用欠采样的CCD观察与超级采样点扩散功能的影响进行了讨论。对诸如HD 209548(由我们的16英寸望远镜捕获)的图像的分析表明,点扩散功能方法与光度法相当,但需要较少的图像。还讨论了诸如哈勃等望远镜的其他图像的应用。

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