论文标题
全智能射线显示器的高能量瞬变的本地化方法
A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor
论文作者
论文摘要
高能瞬变的快速可靠定位对于表征爆发特性和指导后续观测至关重要。基于不同检测器的相对计数的定位已被广泛用于全套伽马射线监测器。对于此计数分布的本地化，有两种主要方法：$χ^{2} $最小化方法和贝叶斯方法。在这里，我们提出了一种修改的贝叶斯方法，该方法可以利用贝叶斯方法的准确性和$χ^{2} $方法的简单性。通过综合模拟，我们发现我们具有泊松可能性的贝叶斯方法通常比$χ^{2} $方法更适用于各种爆发，尤其是对于弱爆发。我们进一步提出了基于贝叶斯推论的位置谱迭代方法，这可以减轻由爆发和位置模板之间的光谱差异引起的问题。我们的方法非常适合具有有限的计算资源或时间敏感应用程序的方案，例如机上本地化软件，以及用于快速后续观察的低延迟定位。
Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this counts distribution localization: $χ^{2}$ minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the $χ^{2}$ method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than $χ^{2}$ method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapid follow-up observations.
