论文标题
在存在恒星变异性和仪器噪声的情况下，小波的功率在运输和相位曲线的分析中。 ii。过境参数的准确性
Power of wavelets in analyses of transit and phase curves in the presence of stellar variability and instrumental noise. II. Accuracy of the transit parameters
论文作者
论文摘要
语境。系外行星光曲线中的噪声（例如恒星活动的噪声，对流噪声和仪器噪声）会扭曲外部电流光曲线，并导致最佳拟合过境参数的偏见。最佳拟合算法可以在存在相关噪声的情况下提供稳定性，并导致统计上一致的结果，即，实际偏见通常在误差间隔内。在日常使用中，大多数算法都无法自动满足这一点，因此必须对算法进行测试。目标。在本文中，我们描述了一种类似自举的测试，用于处理一般情况，并将其应用于基于小波的运输和光曲线模块（TLCM）算法，对其进行测试以实现针对相关噪声的稳定性。我们比较和对比FITSH算法的结果，该算法基于白噪声的假设。方法。在存在自回归积分移动平均值（ARIMA）过程的相关噪声模型的情况下，我们模拟了先前已知参数的过境光曲线。然后，我们解决了模拟观测值，并检查了所得参数和误差间隔。结果。我们发现，FITSH的假设，即只有白噪声，导致结果不一致：最佳拟合参数的分布比确定的误差间隔的宽度更广泛。另一方面，基于小波的TLCM算法正确处理相关噪声，从而导致正确确定的参数和误差间隔与实际偏见完全一致
Context. Correlated noise in exoplanet light curves, such as noise from stellar activity, convection noise, and instrumental noise, distorts the exoplanet transit light curves and leads to biases in the best-fit transit parameters. An optimal fitting algorithm can provide stability against the presence of correlated noises and lead to statistically consistent results, namely, the actual biases are usually within the error interval. This is not automatically satisfied by most of the algorithms in everyday use and the testing of the algorithms is necessary. Aims. In this paper, we describe a bootstrapping-like test to handle with the general case and we apply it to the wavelet-based Transit and Light Curve Modeller (TLCM) algorithm, testing it for the stability against the correlated noise. We compare and contrast the results with regard to the FITSH algorithm, which is based on an assumption of white noise. Methods. We simulated transit light curves with previously known parameters in the presence of a correlated noise model generated by an Autoregressive Integrated Moving Average (ARIMA) process. Then we solved the simulated observations and examined the resulting parameters and error intervals. Results. We have found that the assumption of FITSH, namely, that only white noise is present, has led to inconsistencies in the results: the distribution of best-fit parameters is then broader than the determined error intervals by a factor of 3-6. On the other hand, the wavelet-based TLCM algorithm handles the correlated noise properly, leading to both properly determined parameter and error intervals that are perfectly consistent with the actual biases
