Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Yi Zhao
Wangchen Xue
Shaolin Xiong
Qi Luo
Yuanhao Wang
Jiacong Liu
Heng Yu
Xiaoyun Zhao
Yue Huang
Jinyuan Liao
Jianchao Sun
Xiaobo Li
Qibin Yi
Ce Cai
Shuo Xiao
Shenglun Xie
Chao Zheng
Yanqiu Zhang
Chenwei Wang
Wenjun Tan
Abstract： 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: $\chi^{2}$ 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 $\chi^{2}$ method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than $\chi^{2}$ 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.
Peer Review Status:
Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Yi Zhao
Wangchen Xue
Shaolin Xiong
Qi Luo
Yuanhao Wang
Jiacong Liu
Heng Yu
Xiaoyun Zhao
Yue Huang
Jinyuan Liao
Jianchao Sun
Xiaobo Li
Qibin Yi
Ce Cai
Shuo Xiao
Shenglun Xie
Chao Zheng
Yanqiu Zhang
Chenwei Wang
Wenjun Tan
Abstract： 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: $\chi^{2}$ 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 $\chi^{2}$ method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than $\chi^{2}$ 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.
Peer Review Status:Awaiting Review
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