自2017年发射以来,慧眼硬X射线调制望远镜(Insight Hard X-ray Modulation Telescope,InsightHXMT)凭借着宽能段、大有效面积和高观测频率的优势,已经成为吸积X射线脉冲星研究领域中最重要的天文卫星之一.它在回旋吸收线和吸积物理研究...自2017年发射以来,慧眼硬X射线调制望远镜(Insight Hard X-ray Modulation Telescope,InsightHXMT)凭借着宽能段、大有效面积和高观测频率的优势,已经成为吸积X射线脉冲星研究领域中最重要的天文卫星之一.它在回旋吸收线和吸积物理研究中取得了重要进展,主要包括探测到高能回旋吸收线、揭示回旋线能量的演化和观测到辐射压主导的吸积盘.展开更多
银河系中心的Sgr A lobes是一对垂直于银盘、关于银心对称的气泡结构,高度约为15 pc。X射线观测表明,这对气泡具有清晰的边界,很可能是由某种能量爆发现象产生的激波扫过银心附近的气体介质所形成。银心黑洞的活动产生的外流是一个可以...银河系中心的Sgr A lobes是一对垂直于银盘、关于银心对称的气泡结构,高度约为15 pc。X射线观测表明,这对气泡具有清晰的边界,很可能是由某种能量爆发现象产生的激波扫过银心附近的气体介质所形成。银心黑洞的活动产生的外流是一个可以作为该气泡形成原因的机制,因此该气泡的形成历史对理解银河系中心的演化和高能天体物理过程具有重要意义。通过流体力学模拟研究了短时标的活动星系核喷流作为气泡成因的模型。数值模拟研究结果表明,一次持续500 a的喷流可以较好地还原该气泡的形态、密度、温度、X射线辐射等性质。基于目前结果还不能排除其他的气泡成因模型,例如潮汐撕裂事件产生的外流模型。未来通过多波段联合观测,将能对气泡成因施加更严格的约束。展开更多
We identify an S-shaped main-jet axis in the Vela core-collapse supernova remnant(CCSNR)that we attribute to a pair of precessing jets,one of the tens of pairs of jets that exploded the progenitor of Vela according to...We identify an S-shaped main-jet axis in the Vela core-collapse supernova remnant(CCSNR)that we attribute to a pair of precessing jets,one of the tens of pairs of jets that exploded the progenitor of Vela according to the jittering jets explosion mechanism(JJEM).A main-jet axis is a symmetry axis across the CCSNR and through the center.We identify the S-shaped main-jet axis by the high abundance of ejecta elements,oxygen,neon,and magnesium.We bring the number of identified pairs of clumps and ears in Vela to seven,two pairs shaped by the pair of precessing jets that formed the main-jet axis.The pairs and the main-jet axis form the point-symmetric wind-rose structure of Vela.The other five pairs of clumps/ears do not have signatures near the center,only on two opposite sides of the CCSNR.We discuss different possible jet-less shaping mechanisms to form such a point-symmetric morphology and dismiss these processes because they cannot explain the point-symmetric morphology of Vela,the S-shaped high ejecta abundance pattern,and the enormous energy required to shape the S-shaped structure.Our findings strongly support the JJEM and further severely challenge the neutrino-driven explosion mechanism.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
文摘自2017年发射以来,慧眼硬X射线调制望远镜(Insight Hard X-ray Modulation Telescope,InsightHXMT)凭借着宽能段、大有效面积和高观测频率的优势,已经成为吸积X射线脉冲星研究领域中最重要的天文卫星之一.它在回旋吸收线和吸积物理研究中取得了重要进展,主要包括探测到高能回旋吸收线、揭示回旋线能量的演化和观测到辐射压主导的吸积盘.
文摘银河系中心的Sgr A lobes是一对垂直于银盘、关于银心对称的气泡结构,高度约为15 pc。X射线观测表明,这对气泡具有清晰的边界,很可能是由某种能量爆发现象产生的激波扫过银心附近的气体介质所形成。银心黑洞的活动产生的外流是一个可以作为该气泡形成原因的机制,因此该气泡的形成历史对理解银河系中心的演化和高能天体物理过程具有重要意义。通过流体力学模拟研究了短时标的活动星系核喷流作为气泡成因的模型。数值模拟研究结果表明,一次持续500 a的喷流可以较好地还原该气泡的形态、密度、温度、X射线辐射等性质。基于目前结果还不能排除其他的气泡成因模型,例如潮汐撕裂事件产生的外流模型。未来通过多波段联合观测,将能对气泡成因施加更严格的约束。
基金A grant from the Pazy Foundation supported this research
文摘We identify an S-shaped main-jet axis in the Vela core-collapse supernova remnant(CCSNR)that we attribute to a pair of precessing jets,one of the tens of pairs of jets that exploded the progenitor of Vela according to the jittering jets explosion mechanism(JJEM).A main-jet axis is a symmetry axis across the CCSNR and through the center.We identify the S-shaped main-jet axis by the high abundance of ejecta elements,oxygen,neon,and magnesium.We bring the number of identified pairs of clumps and ears in Vela to seven,two pairs shaped by the pair of precessing jets that formed the main-jet axis.The pairs and the main-jet axis form the point-symmetric wind-rose structure of Vela.The other five pairs of clumps/ears do not have signatures near the center,only on two opposite sides of the CCSNR.We discuss different possible jet-less shaping mechanisms to form such a point-symmetric morphology and dismiss these processes because they cannot explain the point-symmetric morphology of Vela,the S-shaped high ejecta abundance pattern,and the enormous energy required to shape the S-shaped structure.Our findings strongly support the JJEM and further severely challenge the neutrino-driven explosion mechanism.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
