论文标题
拥挤领域的准确不确定性估计：自适应光学和斑点数据
Accurate uncertainty estimation in crowded fields: adaptive optics and speckle data
论文作者
论文摘要
最佳误差估计是实现准确的光度法和天体测量值的关键。高角度分辨率图像中的恒星通量和位置通常使用PSF拟合程序（例如Starfinder）测量。但是，这些软件包计算出的正式不确定性往往会严重低估相关的不确定性。我们提出了一种使用重采样方法来解决此问题的新方法，以获得强大而可靠的不确定性而不会丧失敏感性。
Optimal error estimation is key to achieve accurate photometry and astrometry. Stellar fluxes and positions in high angular resolution images are typically measured with PSF fitting routines, such as StarFinder. However, the formal uncertainties computed by these software packages tend to seriously underestimate the relevant uncertainties. We present a new approach to deal with this problem using a resampling method to obtain robust and reliable uncertainties without loss of sensitivity.
