消除由LISA的加速度噪声引起的漂移趋势
Removing the trend of drift induced from acceleration noise for LISA
摘要：
在本文中，我们展示了一种方法来消除随机加速度在频域中的LISA验证质量引起的漂移的功率。在进行任何进一步分析之前，必须从LISA时间序列数据中清除漂移。清洗通常在时域中通过使用二次函数来拟合时间序列数据，然后从数据中去除拟合部分来执行。对残差进行傅里叶变换，再与LISA传递函数卷积，得到LISA灵敏度曲线。然而，宇宙引力波背景由于其随机性而不能用这种方法恢复。在这里，我们提供了一个新的表示的功率谱给出的离散傅立叶变换，这是应用于找到的漂移功率的功能，在频域中的清洁。我们还给出了用于在频域中分析数据的概率分布。我们结合几种技术，包括马尔可夫链蒙特卡罗方法，模拟退火，和Gelman & Rubin的方法，与贝叶斯定理建立的算法。该算法被用来分析24个模拟的LISA仪器噪声。我们证明，通过这种方法可以恢复LISA的灵敏度。它可以帮助我们建立一些必须在频域完成的LISA数据分析任务的算法。如果其他星载干涉仪的证明质量上的电荷不能完全抵消，则该方法可以应用于其他星载干涉仪。
英文摘要：
In this paper we demonstrate a methodology to remove the power of the drift induced from random acceleration on LISA proof mass in the frequency domain. The drift must be cleaned from LISA time series data in advance of any further analysis. The cleaning is usually performed in the time domain by using a quadratic function to fit the time series data, and then removing the fitted part from the data. Having Fourier transformed the residuals, and then convolved with LISA transfer function, LISA sensitivity curve can be obtained. However, cosmic gravitational-wave background cannot be retrieved with this approach due to its random nature. Here we provide a new representation of power spectrum given by discrete Fourier transform, which is applied to find the function of the drift power for the cleaning in the frequency domain. We also give the probability distribution used to analyze the data in the frequency domain. We combine several techniques, including Markov Chain Monte Carlo method, simulated annealing, and Gelman & Rubin's method, with Baye's theorem to build the algorithm. The algorithm is utilized to analyze 24 simulations of LISA instrumental noise. We prove that the LISA sensitivity can be recovered through this approach. It can help us to build algorithms for some tasks which are must accomplished in the frequency domain for LISA data analysis. This method can be applied to other space-borne interferometers if charges on their proof masses cannot be perfectly cancelled.
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