Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably n...Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably normal gaseous stars whose debris falls onto the BH,sustaining the flares over years.White dwarfs(WDs),which are the most prevalent compact stars and a million times denser-and therefore tougher-than gaseous stars,can only be disrupted by intermediate-mass black holes(IMBHs)of 10^(2)–10^(5) solar masses.WD-TDEs are considered to generate more powerful and short-lived flares,but their evidence has been lacking.Here we report observations of a fast and luminous X-ray transient EP250702a detected by Einstein Probe.Its one-day-long X-ray peak as luminous as 10^(47−49) erg s^(−1) showed strong recurrent flares with hard spectra extending to several tens of MeV gamma-rays,as detected by Fermi/GBM and Konus-Wind,indicating relativistic jet emission.The jet's X-rays dropped sharply from 3×10^(49) erg s^(−1) to around 1044 erg s^(−1) within 20 days(10 days in the source rest frame).These characteristics are inconsistent with any previously known transient phenomena.We suggest that this fast-evolving event over the unprecedentedly short timescale arises likely from disruption of a WD by an IMBH.At late times,a soft component progressively dominates the X-ray spectrum,reaching a luminosity as high as 1044 erg s^(−1),which is consistent with being extreme super-Eddington emission from an accretion disk expected to form in an IMBH-WD TDE.WD-TDEs open a new window for investigating the elusive IMBHs and their surrounding stellar environments,and they are prime sources of gravitational waves in the band of space-based interferometers.展开更多
动床阻力研究是河流动力学中的重要课题,在回顾河流动床阻力研究现状的基础上,剖析了Einstein H A(以下简称Einstein)河流动床阻力公式存在的问题;一是原经验曲线未涵盖高能态区范围;二是试算的黄河沙粒水力半径约30%大于实测水深,导致...动床阻力研究是河流动力学中的重要课题,在回顾河流动床阻力研究现状的基础上,剖析了Einstein H A(以下简称Einstein)河流动床阻力公式存在的问题;一是原经验曲线未涵盖高能态区范围;二是试算的黄河沙粒水力半径约30%大于实测水深,导致按水力半径分割定义式求出的沙波水力半径为负值的物理悖论。通过黄河实测数据补充点群范围,外延曲线且拟合出关系式。利用实测资料计算分析Einstein动床阻力公式适用性,表明国外河流沙波水力半径未出现负值且水力参数大于0.3时适用,适当修正初步适用于黄河下游。引入张红武河床纵向稳定指标关系式求河床比降替代实测水面比降,在降低沙波水力半径负值率的前提下提高了公式验证精度,表明Einstein河流动床阻力公式经过修正后,适用于黄河下游相关计算。展开更多
目前传统卷积网络在爱恩斯坦棋中的运用已颇显成效,但存在着训练速度慢,在浅层次的卷积中无法关注到全局信息的缺点,通过改进深度学习算法和使用GNN取代卷积神经网络(CNN),发现可以显著提升模型性能。研究方法包括将爱恩斯坦棋的棋盘和...目前传统卷积网络在爱恩斯坦棋中的运用已颇显成效,但存在着训练速度慢,在浅层次的卷积中无法关注到全局信息的缺点,通过改进深度学习算法和使用GNN取代卷积神经网络(CNN),发现可以显著提升模型性能。研究方法包括将爱恩斯坦棋的棋盘和移动规则表示为图结构,构建GNN以在较浅层次中捕捉局部与全局特征。同时结合蒙特卡洛树搜索(monte carlo tree search,MCTS),通过神经网络的策略头和价值头,提供行动决策和局势评估。实验中,将改进后的GNN算法与传统CNN算法在多轮自对弈中进行对比,结果显示,GNN在局势预测、策略控制及训练效率方面均优于CNN,随着训练次数的增加,该方法在效率提升方面表现出更显著的优势。GNN的应用提升了爱恩斯坦棋博弈模型的效率与策略能力,为进一步探索GNN在完美信息博弈中的潜在价值提供了理论支持和实践基础。展开更多
文摘Stars getting close enough to black holes(BHs)can be torn apart by strong tidal forces,producing electromagnetic flares.To date,more than 100 tidal disruption events(TDEs)have been observed,each involving invariably normal gaseous stars whose debris falls onto the BH,sustaining the flares over years.White dwarfs(WDs),which are the most prevalent compact stars and a million times denser-and therefore tougher-than gaseous stars,can only be disrupted by intermediate-mass black holes(IMBHs)of 10^(2)–10^(5) solar masses.WD-TDEs are considered to generate more powerful and short-lived flares,but their evidence has been lacking.Here we report observations of a fast and luminous X-ray transient EP250702a detected by Einstein Probe.Its one-day-long X-ray peak as luminous as 10^(47−49) erg s^(−1) showed strong recurrent flares with hard spectra extending to several tens of MeV gamma-rays,as detected by Fermi/GBM and Konus-Wind,indicating relativistic jet emission.The jet's X-rays dropped sharply from 3×10^(49) erg s^(−1) to around 1044 erg s^(−1) within 20 days(10 days in the source rest frame).These characteristics are inconsistent with any previously known transient phenomena.We suggest that this fast-evolving event over the unprecedentedly short timescale arises likely from disruption of a WD by an IMBH.At late times,a soft component progressively dominates the X-ray spectrum,reaching a luminosity as high as 1044 erg s^(−1),which is consistent with being extreme super-Eddington emission from an accretion disk expected to form in an IMBH-WD TDE.WD-TDEs open a new window for investigating the elusive IMBHs and their surrounding stellar environments,and they are prime sources of gravitational waves in the band of space-based interferometers.
文摘动床阻力研究是河流动力学中的重要课题,在回顾河流动床阻力研究现状的基础上,剖析了Einstein H A(以下简称Einstein)河流动床阻力公式存在的问题;一是原经验曲线未涵盖高能态区范围;二是试算的黄河沙粒水力半径约30%大于实测水深,导致按水力半径分割定义式求出的沙波水力半径为负值的物理悖论。通过黄河实测数据补充点群范围,外延曲线且拟合出关系式。利用实测资料计算分析Einstein动床阻力公式适用性,表明国外河流沙波水力半径未出现负值且水力参数大于0.3时适用,适当修正初步适用于黄河下游。引入张红武河床纵向稳定指标关系式求河床比降替代实测水面比降,在降低沙波水力半径负值率的前提下提高了公式验证精度,表明Einstein河流动床阻力公式经过修正后,适用于黄河下游相关计算。
文摘目前传统卷积网络在爱恩斯坦棋中的运用已颇显成效,但存在着训练速度慢,在浅层次的卷积中无法关注到全局信息的缺点,通过改进深度学习算法和使用GNN取代卷积神经网络(CNN),发现可以显著提升模型性能。研究方法包括将爱恩斯坦棋的棋盘和移动规则表示为图结构,构建GNN以在较浅层次中捕捉局部与全局特征。同时结合蒙特卡洛树搜索(monte carlo tree search,MCTS),通过神经网络的策略头和价值头,提供行动决策和局势评估。实验中,将改进后的GNN算法与传统CNN算法在多轮自对弈中进行对比,结果显示,GNN在局势预测、策略控制及训练效率方面均优于CNN,随着训练次数的增加,该方法在效率提升方面表现出更显著的优势。GNN的应用提升了爱恩斯坦棋博弈模型的效率与策略能力,为进一步探索GNN在完美信息博弈中的潜在价值提供了理论支持和实践基础。
