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arXiv 1408.3607astro-ph.HEastro-ph.IM
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提高RXTE PCA光谱质量的经验方法

An Empirical Method for Improving the Quality of RXTE PCA Spectra

Javier Garcia, Jeffrey E. McClintock, James F. Steiner, Ronald A. Remillard, Victoria Grinberg

摘要:

我们将螃蟹的所有几百个{ \it RXTE} PCA光谱分别拟合到一个简单的幂律模型;复合光谱中的计数总数为$>10^9$。然后,我们使用残差的光谱导出一个校准工具,称为{ \tt pcacorr},我们将其应用于GX~339- 4、H ~ 1743 - 322和XTE J ~ 1550 - 564的大样本光谱。该工具的应用提高了所有拟合的质量,并且对于具有$\gtrsim10^7$计数的光谱,这种提高是显著的。对于五个PCA探测器中的每一个,Crab剩余光谱都有些不同,但在任务过程中相对稳定。我们建议将{ \tt pcacorr}常规应用于具有$\gtrsim10^6$计数的光谱,并且其中包括0.1\%的系统误差,而不是通常使用的0.5- 1\ %值。我们期望该工具的应用将导致PCA对微弱光谱特征的灵敏度增加一个数量级。

英文摘要:

We fitted all of the several hundred {\it RXTE} PCA spectra of the Crab individually to a simple power-law model; the total number of counts in the composite spectrum is $>10^9$. We then used the spectrum of residuals to derive a calibration tool, called {\tt pcacorr}, that we apply to large samples of spectra for GX~339--4, H1743--322, and XTE J1550--564. Application of the tool improved the quality of all the fits, and the improvement is dramatic for spectra with $\gtrsim10^7$ counts. The Crab residual spectrum is somewhat different for each of the five PCA detectors, but it was relatively stable over the course of the mission. We recommend that {\tt pcacorr} be routinely applied to spectra with $\gtrsim10^6$ counts and that one include a systematic error of 0.1\%, rather than the 0.5--1\% value that has customarily been used. We expect that application of the tool will result in an increase in sensitivity of the PCA to faint spectral features by up to an order of magnitude.

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