The High Energy X-ray telescope (HE) onboard the Insight-HXMT astronomy satellite

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.     

NO分子宏观气体热力学性质的理论研究

采用量子统计系综理论,研究了基态NO分子宏观气体摩尔熵、摩尔内能、摩尔热容等热力学性质.首先应用课题组前期建立的变分代数法(variational algebraic method, VAM)计算获得了基态NO分子的完全振动能级,得到的VAM振动能级作为振动部分,结合欧拉-麦克劳林渐进展开公式的转动贡献,应用于经典的热力学与统计物理公式中,从而计算得到了1000-5000 K温度范围内NO宏观气体的摩尔内能、摩尔熵和摩尔热容.将不同方法计算得到的摩尔热容结果分别与实验值进行比较,结果表明基于VAM完全振动能级获得的结果优于其他方法获得的理论结果.振动部分采用谐振子模型对无限能级求和计算热力学性质的方法有一定的局限性,应当使用有限的完全振动能级进行统计求和.     

驻波声场中单分散细颗粒的相互作用特性

利用外加声场促进悬浮在气相中的细颗粒发生相互作用,进而引起颗粒的碰撞和凝并,使得颗粒平均粒径增大、数目浓度降低,是控制细颗粒排放的重要技术途径.为探究驻波声场中单分散细颗粒的相互作用,建立包含曳力、重力、声尾流效应的颗粒相互作用模型,采用四阶经典龙格-库塔算法和二阶隐式亚当斯插值算法对模型进行求解.将数值模拟得到的颗粒声波夹带速度和相互作用过程与相应的解析解和实验结果进行对比,验证模型的准确性.进而研究颗粒初始条件和直径对相互作用特性的影响.结果表明,初始时刻颗粒中心连线越接近声波波动方向、颗粒位置越接近波腹点,颗粒间的声尾流效应就越强,颗粒发生碰撞所需要的时间就越短.研究还发现,颗粒直径对颗粒相互作用的影响取决于初始时刻颗粒中心连线偏离声波波动方向的程度.当偏离较小时,颗粒直径越大,颗粒发生碰撞所需要的时间越短;当偏离很大时,直径较小的颗粒能够发生碰撞,而直径较大的颗粒则无法发生碰撞.     

基于连续量子级联激光器的1103.4cm–1处NH3混叠吸收光谱特性研究

由于NH3在大气气溶胶化学中具有重要作用,所以快速和精确反演NH3浓度对环境问题非常重要.本文以9.05μm的室温连续量子级联激光器(quantum cascade laser,QCL)作为光源,采用波长扫描直接吸收可调谐二极管激光吸收光谱(tunable diode laser absorption spectroscopy,TDLAS)技术,研究了QCL在1103.4 cm–1的光谱特性,获得了激光器控制的温度电流与波长的关系.设计了QCL二级温控的低压实验平台,测量氨气在1103.4 cm–1处的6条混叠吸收线,在降低压强的情况下谱线展宽变小,使混叠光谱分离,由此计算各条吸收线的线强,进一步对测量不确定度进行分析.针对混叠严重的光谱提出了低压分离单光谱精确反演气体浓度的方法,并进行了实验验证.通过与HITRAN数据库进行结果对比,得出氨气在1103.4 cm–1的实验测量线强值与数据库偏差为2.71%-4.71%,实验测量线强值的不确定度在2.42%-8.92%,极低压条件下反演浓度与实际值的偏差在1%-3%.     

Astrocyte and ions metabolism during epileptogenesis:A review for modeling studies

As a large group of cells in a central nervous system, astrocytes have a great influence on ion and energy metabolism in a nervous system. Disorders of neuronal ion and energy metabolism caused by impaired astrocytes play a key role in the pathogenesis of epilepsy. This paper reviews the existing computational models of epileptogenesis resulting from impaired astrocytes and presents several open perspectives with regard to ion and energy metabolism-induced epileptogenesis in a neuron-astrocyte-capillary coupled model.     

Spin-exchange relaxation of naturally abundant Rb in a K-Rb-~(21)Ne self-compensated atomic comagnetometer

The total effective spin-exchange relaxation of naturally abundant Rb in a K–Rb–21Ne comagnetometer is analyzed,and the results show that the coexistence of 87Rb and 85Rb isotopes in the same volume can lead to a large extra spinexchange broadening compared to pure 87Rb.This broadening mainly comes from the contribution of the equivalent reduction in the Rb spin-exchange rate.On this basis,an approximate relaxation model is proposed and experimentally demonstrated to be more accurate than that from a previous work.This study also provides a method for determining the properties of alkali-metal vapor cells.     

基于深度学习的ELM实时识别研究

基于深度学习的方法,在HL-2A装置上开发出了一套边缘局域模(ELM)实时识别算法。算法使用5200次放电数据(约24.19万数据切片)进行学习,得到一个深度为22层的卷积神经网络。为衡量算法的识别能力,识别了HL-2A装置自2009年实现稳定ELMyH模放电以来所有历史数据(约26000次放电数据),共识别出1665次H模放电,其中误识别35次,误报率为2.10%。在实际的1634次H模放电中,漏识别4次,漏识别率为0.24%。该误报率和漏报率可以满足ELM实时识别的精度要求。识别算法在实时控制环境下,对单个时间点的平均计算时间为0.46ms,可以满足实时控制的计算速度要求。     

氢原子束在大气长程传输中的剥离效应

考虑到中性粒子束对近地轨道太空垃圾的清理作用以及在太空探索中的潜在应用前景,研究了中性粒子束在亚轨道空间长程传输过程中影响束流能量和密度损失的主要物理机制,重点分析了剥离效应对束流损耗的影响。中性束剥离效应包括束流粒子之间碰撞导致的自剥离效应和其与大气粒子碰撞导致的剥离效应。以束密度随传播距离变化的方程为基础,通过引用几何因子来表征束流的自剥离效应强度,建立了剥离效应机制下束流的传输模型,由此评估了束流自剥离效应在中性束长程传输中对传输距离的影响关系。研究结果表明,在固定的高度,当中性束密度大于空气粒子密度时,自剥离效应的影响将非常突出,随着传输高度的升高,即使束密度和空气密度同时降低维持量级一致,自剥离效应对传输距离的影响在大几何因子的情况下仍会增强。     

Quantum Thermalization and Vanishing Thermal Entanglement in the Open Jaynes–Cummings Model

The quantum thermalization of the Jaynes–Cummings (JC) model in both equilibrium and non-equilibrium open-system cases is studied, in which the two subsystems, a two-level system and a single-mode bosonic field, are in contact with either two individual heat baths or a common heat bath. It is found that in the individual heat-bath case, the JC model can only be thermalized when either the two heat baths have the same temperature or the coupling of the JC system to one of the two baths is turned off. In the common heat-bath case, the JC system can be thermalized irrespective of the bath temperature and the system–bath coupling strengths. The thermal entanglement in this system is also studied. A counterintuitive phenomenon of vanishing thermal entanglement in the JC system is found and proved.     

Some optimizations on detecting gravitational wave using convolutional neural network

This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coefficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank.