流星统计 I：仪器视星等的分布
Meteor statistics I: The distribution of instrumental magnitudes
AI总结：
本文提出用指数修改高斯分布建模流星视星等分布，以更准确描述观测数据并提高对流星通量和亮度的外推能力。
摘要：
流星视星等的分布已知遵循指数分布，其中这个分布的基底称为种群指数。观测到的视星等分布保持这种行为，但被检测阈值截断。如果能够确定种群指数和检测阈值，则观测到的流星率可以转换为通量并外推到任何期望的亮度或尺寸。我们主张观测到或仪器流星视星等的分布最好建模为指数修改高斯（exGaussian）分布。原因有三个：首先，exGaussian分布是随机变化的检测阈值和/或检测后测量误差在视星等上的自然结果。其次，exGaussian分布比文献中所有其他竞争分布对视星等分布的拟合更好；我们使用一组暗弱光学流星视星等和一组雷达流星回波幅度的数据集来证明这一点。最后，种群指数、平均检测阈值和随机变化/误差项可以从exGaussian分布的最佳拟合参数中轻松提取。
英文摘要：
The distribution of meteor magnitudes is known to follow an exponential distribution, where the base of this distribution is called the population index. The distribution of observed magnitudes preserves this behavior, but is truncated by the detection threshold. If both the population index and detection threshold can be determined, observed meteor rates can be converted to fluxes and extrapolated to any desired brightness or size. We argue that the distribution of observed or instrumental meteor magnitudes is best modeled as an exponentially modified Gaussian (exGaussian) distribution. This is for three reasons: first, an exGaussian distribution is the natural result of random variations in detection threshold and/or post-detection measurement errors in magnitude. Second, an exGaussian distribution provides a better fit to the magnitude distribution than all other competing distributions in the literature; we demonstrate this using both a set of faint optical meteor magnitudes and a set of radar meteor echo amplitudes. Finally, the population index, mean detection threshold, and random variation/error terms are easily extracted from the best-fit parameters of an exGaussian distribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
