A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor

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 th...

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Bibliographic Details
Published in:arXiv.org 2023-02
Main Authors: Zhao, Yi, Xue, Wangchen, Xiong, Shaolin, Luo, Qi, Wang, Yuanhao, Liu, Jiacong, Yu, Heng, Zhao, Xiaoyun, Huang, Yue, Liao, Jinyuan, Sun, Jianchao, Li, Xiaobo, Yi, Qibin, Cai, Ce, Xiao, Shuo, Xie, Shenglun, Zheng, Chao, Zhang, Yanqiu, Wang, Chenwei, Tan, Wenjun, Guo, Zhiwei, Li, Chaoyang, An, Zhenghua, Chen, Gang, Du, Yanqi, Gao, Min, Gong, Ke, Guo, Dongya, He, Jiang, He, Jianjian, Li, Bing, Li, Gang, Li, Xinqiao, Liang, Jing, Liang, Xiaohua, Liu, Yaqing, Ma, Xiang, Qiao, Rui, Song, Liming, Song, Xinying, Sun, Xilei, Wang, Jin, Wang, Ping, Wen, Xiangyang, Wu, Hong, Xu, Yanbing, Yang, Sheng, Zhang, Dali, Zhang, Fan, Zhang, Hongmei, Zhang, Peng, Zhang, Shu, Zhang, Zhen, Zheng, Shijie, Zhang, Keke, Han, Xingbo, Wu, Haiyan, Hu, Tai, Geng, Hao, Lu, Gaopeng, Xu, Wei, Lyu, Fanchao, Zhang, Hongbo, Lu, Fangjun, Zhang, Shuangnan
Format: Article
Language:English
Subjects:
Bayesian analysis
Gamma ray bursts
Localization
Monitors
Repeaters
Statistical analysis
Statistical inference
Online Access:Get full text
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Summary: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.
ISSN:2331-8422