分类： 天文学 >> 天文学 提交时间： 2023-02-19
Ce Cai
Shao-Lin Xiong
Wang-Chen Xue
Yi Zhao
Shuo Xiao
Qi-Bin Yi
Zhi-Wei Guo
Jia-Cong Liu
Yan-Qiu Zhang
Chao Zheng
Sheng-Lun Xie
Yan-Qi Du
Xiao-Yun Zhao
Cheng-Kui Li
Ping Wang
Wen-Xi Peng
Shi-Jie Zheng
Li-Ming Song
Xin-Qiao Li
Xiang-Yang Wen
摘要： Searching for X-ray and gamma-ray bursts, including Gamma-ray bursts (GRBs), Soft Gamma-ray Repeaters (SGRs) and high energy transients associated with Gravitational wave (GW) events or Fast radio bursts (FRBs), is of great importance in the multi-messenger and multi-wavelength era. Although a coherent search based on the likelihood ratio and Gaussian statistics has been established and utilized in many studies, this Gaussian-based method could be problematic for weak and short bursts which usually have very few counts. To deal with all bursts including weak ones, here we propose the coherent search in Poisson statistics. We studied the difference between Poisson-based and Gaussian-based search methods by Monte Carlo simulations, and find that the Poisson-based search method has advantages compared to the Gaussian case especially for weak bursts. Our results show that, for very weak bursts with very low number of counts, the Poisson-based search can provide higher significance than the Gaussian-based search, and its likelihood ratio (for background fluctuation) still generally follows the chi2 distribution, making the significance estimation of searched bursts very convenient. Thus, we suggest that the coherent search based on Poisson likelihood ratio is more appropriate in the search for generic transients including very weak ones.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Dali Zhang
Chao Zheng
Jiacong Liu
Zhenghua An
Chenwei Wang
Xiangyang Wen
Xinqiao Li
Xilei Sun
Ke Gong
Yaqing Liu
Xiaojing Liu
Sheng Yang
Wenxi Peng
Rui Qiao
Dongya Guo
Peiyi Feng
Yanqiu Zhang
Wangchen Xue
Wenjun Tan
Ce Cai
摘要： As a new member of GECAM mission, the GECAM-C (also called High Energy Burst Searcher, HEBS) is a gamma-ray all-sky monitor onboard SATech-01 satellite, which was launched on July 27th, 2022 to detect gamma-ray transients from 6 keV to 6 MeV, such as Gamma-Ray Bursts (GRBs), high energy counterpart of Gravitational Waves (GWs) and Fast Radio Bursts (FRBs), and Soft Gamma-ray Repeaters (SGRs). Together with GECAM-A and GECAM-B launched in December 2020, GECAM-C will greatly improve the monitoring coverage, localization, as well as temporal and spectral measurements of gamma-ray transients. GECAM-C employs 12 SiPM-based Gamma-Ray Detectors (GRDs) to detect gamma-ray transients . In this paper, we firstly give a brief description of the design of GECAM-C GRDs, and then focus on the on-ground tests and in-flight performance of GRDs. We also did the comparison study of the SiPM in-flight performance between GECAM-C and GECAM-B. The results show GECAM-C GRD works as expected and is ready to make scientific observations.
分类： 天文学 >> 天文学 提交时间： 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
摘要： 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.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Ce Cai
Shao-Lin Xiong
Wang-Chen Xue
Yi Zhao
Shuo Xiao
Qi-Bin Yi
Zhi-Wei Guo
Jia-Cong Liu
Yan-Qiu Zhang
Chao Zheng
Sheng-Lun Xie
Yan-Qi Du
Xiao-Yun Zhao
Cheng-Kui Li
Ping Wang
Wen-Xi Peng
Shi-Jie Zheng
Li-Ming Song
Xin-Qiao Li
Xiang-Yang Wen
摘要： Searching for X-ray and gamma-ray bursts, including Gamma-ray bursts (GRBs), Soft Gamma-ray Repeaters (SGRs) and high energy transients associated with Gravitational wave (GW) events or Fast radio bursts (FRBs), is of great importance in the multi-messenger and multi-wavelength era. Although a coherent search based on the likelihood ratio and Gaussian statistics has been established and utilized in many studies, this Gaussian-based method could be problematic for weak and short bursts which usually have very few counts. To deal with all bursts including weak ones, here we propose the coherent search in Poisson statistics. We studied the difference between Poisson-based and Gaussian-based search methods by Monte Carlo simulations, and find that the Poisson-based search method has advantages compared to the Gaussian case especially for weak bursts. Our results show that, for very weak bursts with very low number of counts, the Poisson-based search can provide higher significance than the Gaussian-based search, and its likelihood ratio (for background fluctuation) still generally follows the chi2 distribution, making the significance estimation of searched bursts very convenient. Thus, we suggest that the coherent search based on Poisson likelihood ratio is more appropriate in the search for generic transients including very weak ones.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Yan-Qiu Zhang
Shao-Lin Xiong
Rui Qiao
Dong-Ya Guo
Wen-Xi Peng
Xin-Qiao Li
Wang-Chen Xue
Chao Zheng
Jia-Cong Liu
Wen-Jun Tan
Chen-Wei Wang
Peng Zhang
Ping Wang
Ce Cai
Shuo Xiao
Yue Huang
Pei-Yi Feng
Xiao-Bo Li
Li-Ming Song
Qi-Bin Yi
摘要： The gamma-ray detectors (GRDs) of GECAM-C onborad SATech-01 satellite is designed to monitor gamma-ray transients all over the sky from 6 keV to 6 MeV. The energy response matrix is the key to do spectral measurements of bursts, which is usually generated from GEANT4 simulation and partially verified by the ground calibration. In this work, energy response matrix of GECAM-C GRD is cross-calibrated with Fermi/GBM and Swift/BAT using a sample of Gamma-Ray Bursts (GRBs) and Soft Gamma-Ray Repeaters (SGRs). The calibration results show there is a good agreement between GECAM-C and other reasonably well calibrated instrument (i.e. Fermi/GBM and Swift/BAT). We also find that different GRD detectors of GECAM-C also show consistency with each other. All these results indicate that GECAM-C GRD can provide reliable spectral measurements.
分类： 天文学 >> 天文学 提交时间： 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
摘要： 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.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Dali Zhang
Chao Zheng
Jiacong Liu
Zhenghua An
Chenwei Wang
Xiangyang Wen
Xinqiao Li
Xilei Sun
Ke Gong
Yaqing Liu
Xiaojing Liu
Sheng Yang
Wenxi Peng
Rui Qiao
Dongya Guo
Peiyi Feng
Yanqiu Zhang
Wangchen Xue
Wenjun Tan
Ce Cai
摘要： As a new member of GECAM mission, the GECAM-C (also called High Energy Burst Searcher, HEBS) is a gamma-ray all-sky monitor onboard SATech-01 satellite, which was launched on July 27th, 2022 to detect gamma-ray transients from 6 keV to 6 MeV, such as Gamma-Ray Bursts (GRBs), high energy counterpart of Gravitational Waves (GWs) and Fast Radio Bursts (FRBs), and Soft Gamma-ray Repeaters (SGRs). Together with GECAM-A and GECAM-B launched in December 2020, GECAM-C will greatly improve the monitoring coverage, localization, as well as temporal and spectral measurements of gamma-ray transients. GECAM-C employs 12 SiPM-based Gamma-Ray Detectors (GRDs) to detect gamma-ray transients . In this paper, we firstly give a brief description of the design of GECAM-C GRDs, and then focus on the on-ground tests and in-flight performance of GRDs. We also did the comparison study of the SiPM in-flight performance between GECAM-C and GECAM-B. The results show GECAM-C GRD works as expected and is ready to make scientific observations.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Yi Zhao
Wang-Chen Xue
Shao-Lin Xiong
Yuan-Hao Wang
Jia-Cong Liu
Qi Liuo
Yan-Qiu Zhang
Jian-Chao Sun
Xiao-Yun Zhao
Ce Cai
Shuo Xiao
Yue Huang
Xiao-Bo Li
Zhen Zhang
Jin-Yuan Liao
Sheng Yang
Rui Qiao
Dong-Ya Guo
Chao Zheng
Qi-Bin Yi
摘要： Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is $\sim 2.5^{\circ}$. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Yan-Qiu Zhang
Shao-Lin Xiong
Rui Qiao
Dong-Ya Guo
Wen-Xi Peng
Xin-Qiao Li
Wang-Chen Xue
Chao Zheng
Jia-Cong Liu
Wen-Jun Tan
Chen-Wei Wang
Peng Zhang
Ping Wang
Ce Cai
Shuo Xiao
Yue Huang
Pei-Yi Feng
Xiao-Bo Li
Li-Ming Song
Qi-Bin Yi
摘要： The gamma-ray detectors (GRDs) of GECAM-C onborad SATech-01 satellite is designed to monitor gamma-ray transients all over the sky from 6 keV to 6 MeV. The energy response matrix is the key to do spectral measurements of bursts, which is usually generated from GEANT4 simulation and partially verified by the ground calibration. In this work, energy response matrix of GECAM-C GRD is cross-calibrated with Fermi/GBM and Swift/BAT using a sample of Gamma-Ray Bursts (GRBs) and Soft Gamma-Ray Repeaters (SGRs). The calibration results show there is a good agreement between GECAM-C and other reasonably well calibrated instrument (i.e. Fermi/GBM and Swift/BAT). We also find that different GRD detectors of GECAM-C also show consistency with each other. All these results indicate that GECAM-C GRD can provide reliable spectral measurements.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Xiao-Yun Zhao
Shao-Lin Xiong
Xiang-Yang Wen
Xin-Qiao Li
Ce Cai
Shuo Xiao
Qi Luo
Wen-Xi Peng
Dong-Ya Guo
Zheng-Hua An
Ke Gong
Jin-Yuan Liao
Yan-Qiu Zhang
Yue Huang
Lu Li
Xing Wen
Fei Zhang
Jing Duan
Chen-Wei Wang
Dong-Li Shi
摘要： Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Yi Zhao
Wang-Chen Xue
Shao-Lin Xiong
Yuan-Hao Wang
Jia-Cong Liu
Qi Liuo
Yan-Qiu Zhang
Jian-Chao Sun
Xiao-Yun Zhao
Ce Cai
Shuo Xiao
Yue Huang
Xiao-Bo Li
Zhen Zhang
Jin-Yuan Liao
Sheng Yang
Rui Qiao
Dong-Ya Guo
Chao Zheng
Qi-Bin Yi
摘要： Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e. GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including gravitational waves high-energy electromagnetic counterparts, Gamma-ray Bursts, Soft Gamma-ray Repeaters, Solar Flares and Terrestrial Gamma-ray Flashes. Since launch in December 2020, GECAM-B has detected hundreds of astronomical and terrestrial events. For these bursts, localization is the key for burst identification and classification as well as follow-up observations in multi-wavelength. Here, we propose a Bayesian localization method with Poisson data with Gaussian background profile likelihood to localize GECAM bursts based on the burst counts distribution in detectors with different orientations. We demonstrate that this method can work well for all kinds of bursts, especially for extremely short ones. In addition, we propose a new method to estimate the systematic error of localization based on a confidence level test, which can overcome some problems of the existing method in literature. We validate this method by Monte Carlo simulations, and then apply it to a burst sample with accurate location and find that the mean value of the systematic error of GECAM-B localization is $\sim 2.5^{\circ}$. By considering this systematic error, we can obtain a reliable localization probability map for GECAM bursts. Our methods can be applied to other gamma-ray monitors.
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