拟合上坡读出数据中的运动天体及其在罗曼太空望远镜和JWST中的应用
Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST
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中文总结 AI 辅助
本文提出一种拟合上坡读出数据中运动天体路径的方法，通过完整似然函数优化位置和速度，可准确移除源并提取最大信息，适用于罗曼和JWST等天基及地基观测。
中文摘要 AI 辅助
运动天体破坏了在天文图像上坡读出过程中每像素计数率恒定的基本性质。在本文中，我们展示了如何在探测器被非破坏性读出时拟合运动天体在探测器上的路径。我们为每个像素写出完整的似然函数，该函数受恒定计数率以及由运动源引起的随时间变化的计数率影响。假设运动源为点源，且有效点扩散函数已知，我们剩下四个非线性进入似然函数的参数：两个用于位置，两个用于速度。所有其余参数可以通过闭式表达式进行优化。我们的方法提取了关于运动源位置和速度的最大信息，并使得能够从图像中准确移除该源。我们研究了通量、位置和速度精度对目标速度和读出模式的依赖性。我们还发现，由于需要拟合不确定的位置和速度，恢复的通量存在一个小的正偏差。我们的方法可用于前景中存在太阳系小天体的天基图像，例如来自罗曼和JWST的图像，或用于前景中存在卫星的地基观测。我们通过拟合JWST上NIRISS仪器偶然观测到的小行星轨迹来展示我们方法的潜力，并将其与JWST管道的性能进行比较。实现我们方法的Python代码可在该https URL获取。在2023款Macbook Pro上，拟合运动天体轨迹的总计算成本约为1秒。
英文摘要
A moving object breaks the fundamental property of constant per-pixel count rates in an astronomical image read out up-the-ramp. In this paper, we show how to fit a moving object's path across a detector as that detector is read out nondestructively. We write the full likelihood function for every pixel subject to a constant count rate plus a time-dependent count rate due to a moving source. Assuming the moving source to be point-like and assuming the effective point-spread function to be known, we are left with four parameters that enter the likelihood nonlinearly: two for position and two for velocity. All remaining parameters can be optimized using closed-form expressions. Our approach extracts maximal information on a moving source's position and speed and enables the source to be accurately removed from the image. We investigate the dependence of flux, position, and velocity precision on the target's speed and the readout pattern. We also find a small, positive bias on the recovered flux due to the need to fit for an uncertain position and speed. Our approach can be used for space-based images with minor Solar system bodies in the foreground, e.g.~from Roman and JWST, or for ground-based observations with satellites in the foreground. We demonstrate the promise of our method with a fit to an asteroid track observed serendipitously by the NIRISS instrument on JWST, comparing it to the performance of the JWST pipeline. Python code implementing our approach is available at https://github.com/t-brandt/moving_source. The total computational cost to fit the track of a moving object is $\sim$1 second on a 2023 Macbook Pro.
发表机构
- Space Telescope Science Institute（太空望远镜科学研究所）
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