Subjects: Astronomy >> Astrophysical processes submitted time 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
Abstract： 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.
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
Subjects: Astronomy >> Astrophysical processes submitted time 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
Abstract： 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.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 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
Abstract： 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.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 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
Abstract： 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.
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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