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| 1 | Insight-HXMT observations of the first binary neutron star merger GW170817显示文摘Finding the electromagnetic(EM) counterpart of binary compact star merger, especially the binary neutron star(BNS) merger,is critically important for gravitational wave(GW) astronomy, cosmology and fundamental physics. On Aug. 17, 2017,Advanced LIGO and Fermi/GBM independently triggered the first BNS merger, GW170817, and its high energy EM counterpart,GRB 170817 A, respectively, resulting in a global observation campaign covering gamma-ray, X-ray, UV, optical, IR, radio as well as neutrinos. The High Energy X-ray telescope(HE) onboard Insight-HXMT(Hard X-ray Modulation Telescope) is the unique high-energy gamma-ray telescope that monitored the entire GW localization area and especially the optical counterpart(SSS17 a/AT2017 gfo) with very large collection area(~1000 cm^2) and microsecond time resolution in 0.2-5 MeV. In addition,Insight-HXMT quickly implemented a Target of Opportunity(ToO) observation to scan the GW localization area for potential X-ray emission from the GW source. Although Insight-HXMT did not detect any significant high energy(0.2-5 MeV) radiation from GW170817, its observation helped to confirm the unexpected weak and soft nature of GRB 170817 A. Meanwhile,Insight-HXMT/HE provides one of the most stringent constraints(~10^(-7) to 10^(-6) erg/cm^2/s) for both GRB170817 A and any other possible precursor or extended emissions in 0.2-5 MeV, which help us to better understand the properties of EM radiation from this BNS merger. Therefore the observation of Insight-HXMT constitutes an important chapter in the full context of multi-wavelength and multi-messenger observation of this historical GW event. | TiPei Li ShaoLin Xiong ShuangNan Zhang FangJun Lu LiMing Song XueLei Cao Zhi Chang Gang Chen Li Chen TianXiang Chen Yong Chen YiBao Chen YuPeng Chen Wei Cui WeiWei Cui JingKang Deng YongWei Dong YuanYuan Du MinXue Fu GuanHua Gao He Gao Min Gao MingYu Ge YuDong Gu Ju Guan ChengCheng Guo DaWei Han Wei Hu Yue Huang Jia Huo ShuMei Jia LuHua Jiang WeiChun Jiang Jing Jin YongJie Jin Bing Li ChengKui Li Gang Li MaoShun Li Wei Li Xian Li XiaoBo Li XuFang Li YanGuo Li ZiJian Li ZhengWei Li XiaoHua Liang JinYuan Liao CongZhan Liu GuoQing Liu HongWei Liu ShaoZhen Liu XiaoJing Liu Yuan Liu YiNong Liu Bo Lu XueFeng Lu Tao Luo Xiang Ma Bin Meng Yi Nang JianYin Nie Ge OU JinLu Qu Na Sai Liang Sun Yin Tan Lian Tao WenHui Tao YouLi Tuo GuoFeng Wang HuanYu Wang Juan Wang WenShuai Wang YuSa Wang XiangYang Wen BoBing WU Mei Wu GuangCheng Xiao He Xu YuPeng Xu LinLi Yan JiaWei Yang Sheng Yang YanJi Yang AiMei Zhang ChunLei Zhang ChengMo Zhang Fan Zhang HongMei Zhang Juan Zhang Qiang Zhang Shu Zhang Tong Zhang Wei Zhang WanChang Zhang WenZhao Zhang Yi Zhang Yue Zhang YiFei Zhang YongJie Zhang Zhao Zhang ZiLiang Zhang HaiSheng Zhao JianLing Zhao XiaoFan Zhao ShiJie Zheng Yue Zhu YuXuan Zhu ChangLin Zou | 2018 | Science China(Physics,Mechanics & Astronomy)2018，61，3： | 12 |
| 2 | The High Energy X-ray telescope (HE) onboard the Insight-HXMT astronomy satellite显示文摘The Insight-Hard X-ray Modulation Telescope(Insight-HXMT) is a broadband X-ray and γ-ray(1-3000 ke V) astronomy satellite. One of its three main telescopes is the High Energy X-ray telescope(HE). The main detector plane of HE comprises 18 Na I(Tl)/Cs I(Na) phoswich detectors, where Na I(Tl) is used as the primary detector to measure ~ 20-250 ke V photons incident from the field of view(FOV) defined by collimators, and Cs I(Na) is used as the active shielding detector to Na I(Tl) by pulse shape discrimination. Additionally, Cs I(Na) is used as an omnidirectional γ-ray monitor. The HE collimators have a diverse FOV,i.e. 1.1°×5.7°(15 units), 5.7°×5.7°(2 units), and blocked(1 unit). Therefore, the combined FOV of HE is approximately5.7°×5.7°. Each HE detector has a diameter of 190 mm resulting in a total geometrical area of approximately 5100 cm2, and the energy resolution is ~15% at 60 ke V. For each recorded X-ray event by HE, the timing accuracy is less than 10 μs and the deadtime is less than 10 μs. HE is used for observing spectra and temporal variability of X-ray sources in the 20-250 ke V band either by pointing observations for known sources or scanning observations to unveil new sources. Additionally, HE is used for monitoring the γ-ray burst in 0.2-3 Me V band. This paper not only presents the design and performance of HE instruments but also reports results of the on-ground calibration experiments. | CongZhan Liu YiFei Zhang XuFang Li XueFeng Lu Zhi Chang ZhengWei Li AiMei Zhang YongJie Jin HuiMing Yu Zhao Zhang MinXue Fu YiBao Chen JianFeng Ji YuPeng Xu JingKang Deng RenCheng Shang GuoQing Liu FangJun Lu ShuangNan Zhang YongWei Dong TiPei Li Mei Wu YanGuo Li HuanYu Wang BoBing Wu YongJie Zhang Zhi Zhang ShaoLin Xiong Yuan Liu Shu Zhang HongWei Liu YiJung Yang Fan Zhang | 2020 | Science China(Physics,Mechanics & Astronomy)2020，63，4： | 9 |
| 3 | Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite显示文摘As China’s first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band(1-250 ke V) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 Me V. It was designed to perform pointing, scanning and gamma-ray burst(GRB)observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed.Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results. | Shuang-Nan Zhang TiPei Li FangJun Lu LiMing Song YuPeng X u CongZhan Liu Yong Chen XueLei Cao QingCui Bu Zhi Chang Gang Chen Li Chen TianXiang Chen YiBao Chen YuPeng Chen Wei Cui WeiWei Cui JingKang Deng YongWei Dong Yuan Yuan Du MinXue Fu GuanHua Gao He Gao Min Gao MingYu Ge YuDong Gu Ju Guan Can Gungor ChengCheng Guo DaWei Han Wei Hu Yue Huang Jia Huo ShuMei Jia LuHua Jiang WeiChun Jiang Jing Jin YongJie Jin Bing Li ChengKui Li Gang Li MaoShun Li Wei Li Xian Li XiaoBo Li XuFang Li YanGuo Li ZiJian Li ZhengWei Li XiaoHua Liang JinYuan Liao GuoQing Liu HongWei Liu ShaoZhen Liu XiaoJing Liu Yuan Liu YiNong Liu Bo Lu XueFeng Lu Tao Luo Xiang Ma Bin Meng Yi Nang JianYin Nie Ge Ou JinLu Qu Na Sai RenCheng Shang GuoHong Shen Liang Sun Ying Tan Lian Tao YouLi Tuo Chen Wang ChunQin Wang GuoFeng Wang HuanYu Wang Juan Wang WenShuai Wang YuSa Wang XiangYang Wen BaiYang Wu BoBing Wu Mei Wu GuangCheng Xiao ShaoLin Xiong LinLi Yan JiaWei Yang Sheng Yang YanJi Yang QiBin Yi Bin Yuan AiMei Zhang ChunLei Zhang ChengMo Zhang Fan Zhang HongMei Zhang Juan Zhang Qiang Zhang ShenYi Zhangs Shu Zhang Tong Zhang WanChang Zhang Wei Zhang WenZhao Zhang Yi Zhang YiFei Zhang YongJie Zhang Yue Zhang Zhao Zhang Zhi Zhang ZiLiang Zhang HaiSheng Zhao XiaoFan Zhao ShiJie Zheng JianFeng Zhou YuXuan Zhu Yue Zhu RenLin Zhuang | 2020 | Science China(Physics,Mechanics & Astronomy)2020，63，4： | 2 |
| 4 | Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice ( Oryza sativa L.)显示文摘 | Weiwei Wen Hanwei Mei Fangjun Feng Sibin Yu Zhicheng Huang Jinhong Wu Liang Chen Xiaoyan Xu Lijun Luo | 2009 | Theoretical and Applied Genetics2009，，3： | 1 |
| 5 | MicroRNA-210 as a Novel Therapy for Treatment of Ischemic Heart Disease显示文摘 | Shijun Hu Mei Huang Zongjin Li Fangjun Jia Zhumur Ghosh Maarten A. Lijkwan Pasquale Fasanaro Ning Sun Xi Wang Fabio Martelli Robert C. Robbins Joseph C. Wu | 2010 | Circulation ( Suppl )2010，，11： | 1 |
| 6 | LaNets:Hybrid Lagrange Neural Networks for Solving Partial Differential Equations显示文摘We propose new hybrid Lagrange neural networks called LaNets to predict the numerical solutions of partial differential equations.That is,we embed Lagrange interpolation and small sample learning into deep neural network frameworks.Concretely,we first perform Lagrange interpolation in front of the deep feedforward neural network.The Lagrange basis function has a neat structure and a strong expression ability,which is suitable to be a preprocessing tool for pre-fitting and feature extraction.Second,we introduce small sample learning into training,which is beneficial to guide themodel to be corrected quickly.Taking advantages of the theoretical support of traditional numerical method and the efficient allocation of modern machine learning,LaNets achieve higher predictive accuracy compared to the state-of-the-artwork.The stability and accuracy of the proposed algorithmare demonstrated through a series of classical numerical examples,including one-dimensional Burgers equation,onedimensional carburizing diffusion equations,two-dimensional Helmholtz equation and two-dimensional Burgers equation.Experimental results validate the robustness,effectiveness and flexibility of the proposed algorithm. | Ying Li Longxiang Xu Fangjun Mei Shihui Ying | 2023 | Computer Modeling in Engineering & Sciences2023，，1： | 0 |
| 7 | Experimental validation of inter-subspecific genetic diversity in rice represented by the differences between the DNA sequences of 'Nipponbare' and '93-11'显示文摘The pedigrees of three sequenced rice cultivars were analyzed to show that a majority of the genetic composition of 'Nipponbare' originates from japonica cultivars while the minority originates from indica cultivars. In contrast, '93-11' is derived mainly from indica cultivars with a smaller contribution from japonica cultivars. All ancestors of 'Guang lu ai 4' appeared to be indica lines. A set of molecular markers (46 InDels and 53 SSRs) polymorphic between 'Nipponbare' and '93-11' were examined in 46 typical indica and 47 typical japonica cultivars selected from 443 accessions according to Cheng's index. All cultivars were divided into indica and japonica groups without overlapping when clustered by Cheng's index, InDels and SSRs. Much higher InDel and SSR diversity between groups than within groups implies that the marker polymorphisms between 'Nipponbare' and '93-11' represent a large proportion of inter-subspecific diversity. About 85% of indica cultivars and more than 90% of japonica cultivars were confirmed to have the same PCR banding patterns as '93-11' and 'Nipponbare', respectively. Some polymorphic loci between 'Nipponbare' and '93-11' cannot be validated in other indica and japonica cultivars, either as subspecies-specific but not predominant alleles, or alleles not specific between the two groups. It was concluded that molecular markers developed from sequence polymorphism between 'Nipponbare' and '93-11' often represent inter-subspecific diversity, although some exceptions were sensitive to either particular marker loci or particular cultivars. | MEI HanWei FENG FangJun LU BaoRong WEN WeiWei Andrew H. PATERSON CAI XingXing CHEN Liang Frank A. FELTUS XU XiaoYan WU JingHong YU XinQiao CHEN HongWei LI Ying LUO LiJun | 2007 | Chinese Science Bulletin2007，52，10： | 0 |
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