Search

The diffuse Galactic $\gamma$-ray emission originates from the interactions between cosmic rays and the interstellar medium or radiation fields within our Galaxy, where the production of neutrinos is also anticipated. Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported measurements of diffuse $\gamma$-rays from the Galactic plane with energies ranging from sub-TeV to 1 PeV. Using publicly available 7 years of IceCube track data with the full detector, we conduct a template search using the $\gamma$-ray flux map observed by LHAASO-KM2A as the neutrino emission template and perform a scan search of the Galactic plane. In the template search, a mild excess of neutrinos is observed in the Galactic plane with a pretrial significance of $1.9\sigma$. The measured muon neutrino intensity at 25 TeV is $4.73^{+2.53}_{-2.51}\times10^{-14}\,{\rm TeV^{-1}\,cm^{-2}\,s^{-1}\,sr^{-1}}$, consistent with the expected neutrino flux assuming that all the diffuse Galactic $\gamma$-rays originate from hadronic interactions. In the Galactic plane scan search, the most significant location is found at $l=63.57^{\circ}$ and $b=0.93^{\circ}$ with a pretrial (posttrial) significance of $4.6\sigma$ ($1.8\sigma$).

Robotic Process Automation (RPA) offers a valuable solution for efficiently automating tasks on the graphical user interface (GUI), by emulating human interactions, without modifying existing code. However, its broader adoption is constrained by the need for expertise in both scripting languages and workflow design. To address this challenge, we present PromptRPA, a system designed to comprehend various task-related textual prompts (e.g., goals, procedures), thereby generating and performing corresponding RPA tasks. PromptRPA incorporates a suite of intelligent agents that mimic human cognitive functions, specializing in interpreting user intent, managing external information for RPA generation, and executing operations on smartphones. The agents can learn from user feedback and continuously improve their performance based on the accumulated knowledge. Experimental results indicated a performance jump from a 22.28% success rate in the baseline to 95.21% with PromptRPA, requiring an average of 1.66 user interventions for each new task. PromptRPA presents promising applications in fields such as tutorial creation, smart assistance, and customer service., Comment: 34 pages

The Cygnus region, which contains massive molecular and atomic clouds and young stars, is a promising Galactic neutrino source candidate. Cosmic rays transport in the region can produce neutrinos and $\gamma$-rays. Recently, the Large High Altitude Air Shower Observatory (LHAASO) detected an ultrahigh-energy $\gamma$-ray bubble (Cygnus Bubble) in this region. Using publicly available track events detected by the IceCube Neutrino Observatory in 7 years of full detector operation, we conduct searches for correlated neutrino signals from the Cygnus Bubble with neutrino emission templates based on LHAASO $\gamma$-ray observations. No significant signals were found for any employed templates. With the 7 TeV $\gamma$-ray flux template, we set a flux upper limit of 90% confidence level (C.L.) for the neutrino emission from the Cygnus Bubble to be $5.7\times10^{-13}\, \mathrm{TeV}^{-1}\mathrm{cm}^{-2}\mathrm{s}^{-1}$ at 5 TeV.

Radio interferometers targeting the 21cm brightness temperature fluctuations at high redshift are subject to systematic effects that operate over a range of different timescales. These can be isolated by designing appropriate Fourier filters that operate in fringe-rate (FR) space, the Fourier pair of local sidereal time (LST). Applications of FR filtering include separating effects that are correlated with the rotating sky vs. those relative to the ground, down-weighting emission in the primary beam sidelobes, and suppressing noise. FR filtering causes the noise contributions to the visibility data to become correlated in time however, making interpretation of subsequent averaging and error estimation steps more subtle. In this paper, we describe fringe rate filters that are implemented using discrete prolate spheroidal sequences, and designed for two different purposes -- beam sidelobe/horizon suppression (the `mainlobe' filter), and ground-locked systematics removal (the `notch' filter). We apply these to simulated data, and study how their properties affect visibilities and power spectra generated from the simulations. Included is an introduction to fringe-rate filtering and a demonstration of fringe-rate filters applied to simple situations to aid understanding., Comment: 21 pages, 18 figures, submitted to Monthly Notices of the Royal Astronomical Society

This paper presents the design and deployment of the Hydrogen Epoch of Reionization Array (HERA) phase II system. HERA is designed as a staged experiment targeting 21 cm emission measurements of the Epoch of Reionization. First results from the phase I array are published as of early 2022, and deployment of the phase II system is nearing completion. We describe the design of the phase II system and discuss progress on commissioning and future upgrades. As HERA is a designated Square Kilometer Array (SKA) pathfinder instrument, we also show a number of "case studies" that investigate systematics seen while commissioning the phase II system, which may be of use in the design and operation of future arrays. Common pathologies are likely to manifest in similar ways across instruments, and many of these sources of contamination can be mitigated once the source is identified.

Detection of the faint 21 cm line emission from the Cosmic Dawn and Epoch of Reionisation will require not only exquisite control over instrumental calibration and systematics to achieve the necessary dynamic range of observations but also validation of analysis techniques to demonstrate their statistical properties and signal loss characteristics. A key ingredient in achieving this is the ability to perform high-fidelity simulations of the kinds of data that are produced by the large, many-element, radio interferometric arrays that have been purpose-built for these studies. The large scale of these arrays presents a computational challenge, as one must simulate a detailed sky and instrumental model across many hundreds of frequency channels, thousands of time samples, and tens of thousands of baselines for arrays with hundreds of antennas. In this paper, we present a fast matrix-based method for simulating radio interferometric measurements (visibilities) at the necessary scale. We achieve this through judicious use of primary beam interpolation, fast approximations for coordinate transforms, and a vectorised outer product to expand per-antenna quantities to per-baseline visibilities, coupled with standard parallelisation techniques. We validate the results of this method, implemented in the publicly-available matvis code, against a high-precision reference simulator, and explore its computational scaling on a variety of problems., Comment: 25 pages, 20 figures, submitted to RAS Techniques and Instruments, matvis is publicly available at https://github.com/HERA-Team/matvis

Optimization problems find widespread use in both single-objective and multi-objective scenarios. In practical applications, users aspire for solutions that converge to the region of interest (ROI) along the Pareto front (PF). While the conventional approach involves approximating a fitness function or an objective function to reflect user preferences, this paper explores an alternative avenue. Specifically, we aim to discover a method that sidesteps the need for calculating the fitness function, relying solely on human feedback. Our proposed approach entails conducting direct preference learning facilitated by an active dueling bandit algorithm. The experimental phase is structured into three sessions. Firstly, we assess the performance of our active dueling bandit algorithm. Secondly, we implement our proposed method within the context of Multi-objective Evolutionary Algorithms (MOEAs). Finally, we deploy our method in a practical problem, specifically in protein structure prediction (PSP). This research presents a novel interactive preference-based MOEA framework that not only addresses the limitations of traditional techniques but also unveils new possibilities for optimization problems.

Recently, the Large High Altitude Air Shower Observatory (LHAASO) identified 12 $\gamma$-ray sources emitting gamma rays with energies above 100 TeV, making them potential PeV cosmic-ray accelerators (PeVatrons). Neutrino observations are crucial in determining whether the gamma-ray radiation process is of hadronic or leptonic origin. In this paper, we study three detected sources, LHAASO J1908+0621, LHAASO J2018+3651, and LHAASO J2032+4102, which are also the most promising galactic high-energy neutrino candidate sources with the lowest pre-trial p-value based on the stacking searches testing for excess neutrino emission by IceCube Neutrino Observatory. We study the lepto-hadronic scenario for the observed multiband spectra of these LHAASO sources considering the possible counterpart source of the LHAASO sources. The very-high-energy gamma rays are entirely attributed to the hadronic contribution, therefore the most optimistic neutrino flux can be derived. Then, we evaluate the statistical significance (p-value) as a function of the observation time of IceCube and the next-generation IceCube-Gen2 neutrino observatory respectively. Our results tend to disfavor that all gamma rays above $100\,\rm GeV$ from LHAASO J1908+0621 are of purely hadronic origin based on current IceCube observations, but the purely hadronic origin of gamma rays above $100\,\rm TeV$ is still possible. By IceCube-Gen2, the origin of gamma rays above $100\,\rm TeV$ from LHAASO J1908+0621 can be further determined at a $5\sigma$ significance level within a running time of $\sim 3$ years. For LHAASO J2018+3651 and LHAASO J2032+4102, the required running time of IceCube-Gen2 is $\sim 10$ years ($3\sigma$) and $\sim 10$ years ($5\sigma$), respectively. Future observations by the next-generation neutrino telescope will be crucial to understanding the particle acceleration and radiation processes inside the sources., Comment: 11 pages, 5 figures, 2 tables; Accepted by ApJ

Conventional lens-based imaging techniques have long been limited to capturing only the intensity distribution of objects, resulting in the loss of other crucial dimensions such as spectral data. Here, we report a spectral lens that captures both spatial and spectral information, and further demonstrate a minimalist framework wherein hyperspectral imaging can be readily achieved by replacing lenses in standard cameras with our spectral lens. As a paradigm, we capitalize on planar liquid crystal optics to implement the proposed framework. Our experiments with various targets show that the resulting hyperspectral camera exhibits excellent performance in both spectral and spatial domains. With merits such as ultra-compactness and strong compatibility, our framework paves a practical pathway for advancing hyperspectral imaging apparatus toward miniaturization, with great potential for portable applications.

We detail the the REACH radiometric system designed to enable measurements of the 21-cm neutral hydrogen line. Included is the radiometer architecture and end-to-end system simulations as well as a discussion of the challenges intrinsic to highly-calibratable system development. Following this, we share laboratory results based on the calculation of noise wave parameters utilising an over-constrained least squares approach demonstrating a calibration RMSE of 80 mK for five hours of integration on a custom-made source with comparable impedance to that of the antenna used in the field. This paper therefore documents the state of the calibrator and data analysis in December 2022 in Cambridge before shipping to South Africa., Comment: 30 pages, 19 figures

Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency. Specialized hardware implementations of those neurons clearly have advantages over general-purpose devices in terms of power and performance, but exhibit poor scalability when it comes to accelerating large neural networks. DeepFire2 introduces a hardware architecture which can map large network layers efficiently across multiple super logic regions in a multi-die FPGA. That gives more control over resource allocation and parallelism, benefiting both throughput and energy consumption. Avoiding the use of lookup tables to implement the AND operations of an SNN, prevents the layer size to be limited by logic resources. A deep pipeline does not only lead to an increased clock speed of up to 600 MHz. We double the throughput and power efficiency compared to our previous version of DeepFire, which equates to an almost 10-fold improvement over other previous implementations. Importantly, we are able to deploy a large ImageNet model, while maintaining a throughput of over 1500 frames per second.

As an efficient algorithm to solve the multi-view registration problem,the motion averaging (MA) algorithm has been extensively studied and many MA-based algorithms have been introduced. They aim at recovering global motions from relative motions and exploiting information redundancy to average accumulative errors. However, one property of these methods is that they use Guass-Newton method to solve a least squares problem for the increment of global motions, which may lead to low efficiency and poor robustness to outliers. In this paper, we propose a novel motion averaging framework for the multi-view registration with Laplacian kernel-based maximum correntropy criterion (LMCC). Utilizing the Lie algebra motion framework and the correntropy measure, we propose a new cost function that takes all constraints supplied by relative motions into account. Obtaining the increment used to correct the global motions, can further be formulated as an optimization problem aimed at maximizing the cost function. By virtue of the quadratic technique, the optimization problem can be solved by dividing into two subproblems, i.e., computing the weight for each relative motion according to the current residuals and solving a second-order cone program problem (SOCP) for the increment in the next iteration. We also provide a novel strategy for determining the kernel width which ensures that our method can efficiently exploit information redundancy supplied by relative motions in the presence of many outliers. Finally, we compare the proposed method with other MA-based multi-view registration methods to verify its performance. Experimental tests on synthetic and real data demonstrate that our method achieves superior performance in terms of efficiency, accuracy and robustness.

The history of eddy covariance (EC) measuring system could be dated back to 100 years ago, but it was not until the recent decades that EC gains popularity and being widely used in global change ecological studies, with explosion of related work published in papers from various journals. Investigating 8297 literature related with EC from 1981 to 2018, we make a comprehensive and critical review of scientific development of EC from a scientometric perspective. First, the paper outlines general bibliometric statistics, including publication number, country contribution, productive institutions, active authors, journal distribution, highly cited articles and fund support, to provide an informative picture of EC studies. Second, research trends are revealed by network visualization and modeling based on keyword analysis, from where we could discover the knowledge structure of EC and detect the research focus and hotspots transitions at different periods. Third, collaboration in EC research community has been explored. FLUXNET is the largest global network uniting EC researchers, here we have quantified and evaluated its performance by using bibliometric indicators of cooperation and citation. Specific discussions have been given to the historical development of EC, including technical maturation and application promotion. Considering the current barrier for collaboration, the review closes by analyzing the reasons hindering data sharing and makes a prospect of new models for data-intensive collaboration in the future.

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.

Born in the late 20s, R is one of the most popular software for statistical computing and graphics. With the development of information technology and the advent of the big data era, great changes have taken place in the R ecosystem. Based on the meta information of the Comprehensive R Archive Network (CRAN) and the bibliometric data of literature citing R, we discovered that while R is initiated by statistics, its development is benefited greatly from computer science and the main user group in academics come from various disciplines such as agricultural science, biological science, environmental science and medical science. In addition, we displayed the collaboration patterns among R developers and analyze the possible effects of collaboration in the R community.

The Large High Altitude Air Shower Observatory (LHAASO) observed a dozen of gamma-ray sources with significant emission above 100 TeV, which may be strong candidates of PeVatrons. Neutrino observations are crucial to diagnose whether the gamma-ray radiative process is hadronic or leptonic. We use the Bayesian method to analyze the ten-year (2008-2018) IceCube data, and hence constrain the hadronic gamma-ray emission in the LHAASO sources. The present neutrino data show that the hadronic gamma-ray flux from Crab Nebula is lower than the observed gamma-ray flux at the 90% C.L. and contributes less than 86%, which disfavors the hadronic origin of the gamma-rays below tens of TeV. For the other LHAASO sources, the present neutrino observations cannot put useful constraints on the gamma-ray radiative process. We consider the uncertainty of the source extension: the upper limits on hadronic gamma-ray flux tend to increase with the extension; and some sources, i.e., LHAASO J2032+4102, LHAASO J1929+1745, and LHAASO J1908+0621, show relatively high statistical significance of neutrino signals if the extension is <0.6 deg. We finally estimate the future observational results of LHAASO sources by the proposed neutrino telescopes. If the LHAASO-observed PeV gamma-rays are of hadronic origin, Crab Nebula may be detected at >100 TeV at 3-sigma C.L. within 20 years by a neutrino detector with the effective area 30 times that of IceCube., Comment: 11 pages, 6 figures, 2 tables, accepted for publication in MNRAS

We present a framework for identifying and flagging malfunctioning antennas in large radio interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study. Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets., Comment: 31 pages, 17 figures

LHAASO detected 12 gamma-ray sources above 100 TeV which are the possible origins of Galactic cosmic-rays. We summarize the neutrino measurements by IceCube and ANTARES in the vicinity of LHAASO sources to constrain the contribution of hadronic gamma-rays in these sources. We find that the current observations constrain that the hadronic gamma-rays contribute no more than ~60% of the gamma-rays from Crab Nebula. Gamma-rays from two LHAASO sources, LHAASO J1825-1326 and LHAASO J1907+0626, are dominated by leptonic components up to ~200 TeV, under the hypotheses in the analysis by IceCube. The uncertainties of the constraint on the hadronic gamma-ray emission are discussed. We also constrain the total 100 TeV gamma-ray emission from TeV PWNe relying on the remarkable sensitivity of LHAASO at that energies., Comment: 11 pages, 4 figures, 2 tables, accepted by ApJ

Neural networks training on edge terminals is essential for edge AI computing, which needs to be adaptive to evolving environment. Quantised models can efficiently run on edge devices, but existing training methods for these compact models are designed to run on powerful servers with abundant memory and energy budget. For example, quantisation-aware training (QAT) method involves two copies of model parameters, which is usually beyond the capacity of on-chip memory in edge devices. Data movement between off-chip and on-chip memory is energy demanding as well. The resource requirements are trivial for powerful servers, but critical for edge devices. To mitigate these issues, We propose Resource Constrained Training (RCT). RCT only keeps a quantised model throughout the training, so that the memory requirements for model parameters in training is reduced. It adjusts per-layer bitwidth dynamically in order to save energy when a model can learn effectively with lower precision. We carry out experiments with representative models and tasks in image application and natural language processing. Experiments show that RCT saves more than 86\% energy for General Matrix Multiply (GEMM) and saves more than 46\% memory for model parameters, with limited accuracy loss. Comparing with QAT-based method, RCT saves about half of energy on moving model parameters., Comment: 14 pages

An increasingly popular method for solving a constrained combinatorial optimisation problem is to first convert it into a quadratic unconstrained binary optimisation (QUBO) problem, and solve it using a standard QUBO solver. However, this relaxation introduces hyper-parameters that balance the objective and penalty terms for the constraints, and their chosen values significantly impact performance. Hence, tuning these parameters is an important problem. Existing generic hyper-parameter tuning methods require multiple expensive calls to a QUBO solver, making them impractical for performance critical applications when repeated solutions of similar combinatorial optimisation problems are required. In this paper, we propose the QROSS method, in which we build surrogate models of QUBO solvers via learning from solver data on a collection of instances of a problem. In this way, we are able capture the common structure of the instances and their interactions with the solver, and produce good choices of penalty parameters with fewer number of calls to the QUBO solver. We take the Traveling Salesman Problem (TSP) as a case study, where we demonstrate that our method can find better solutions with fewer calls to QUBO solver compared with conventional hyper-parameter tuning techniques. Moreover, with simple adaptation methods, QROSS is shown to generalise well to out-of-distribution datasets and different types of QUBO solvers., Comment: 17 pages

We evaluate critically the use of pulsed gradient spin-echo nuclear magnetic resonance (PGSE NMR) to measure molecular mobility during chemical reactions. With raw NMR spectra available in a public depository, we confirm boosted mobility during the click chemical reaction (Science 2020, 369, 537) regardless of the order of magnetic field gradient (linearly-increasing, linearly-decreasing, random sequence). We also confirm boosted mobility for the Diels-Alder chemical reaction. The conceptual advantage of the former chemical system is that constant reaction rate implies constant catalyst concentration, whereas that of the latter is the absence of a paramagnetic catalyst, precluding paramagnetism as objection to the measurements. Data and discussion in this paper show the reliability of experiments when one avoids convection, allows decay of nuclear spin magnetization between successive pulses and recovery of its intensity between gradients, and satisfies quasi-steady state during the time window to acquire each datum. Especially important is to make comparisons on the time scale of actual chemical reaction kinetics. We discuss possible sources of mistaken conclusions that are desirable to avoid., Comment: 18 pages, 4 figures

Learn in-situ is a growing trend for Edge AI. Training deep neural network (DNN) on edge devices is challenging because both energy and memory are constrained. Low precision training helps to reduce the energy cost of a single training iteration, but that does not necessarily translate to energy savings for the whole training process, because low precision could slows down the convergence rate. One evidence is that most works for low precision training keep an fp32 copy of the model during training, which in turn imposes memory requirements on edge devices. In this work we propose Adaptive Precision Training. It is able to save both total training energy cost and memory usage at the same time. We use model of the same precision for both forward and backward pass in order to reduce memory usage for training. Through evaluating the progress of training, APT allocates layer-wise precision dynamically so that the model learns quicker for longer time. APT provides an application specific hyper-parameter for users to play trade-off between training energy cost, memory usage and accuracy. Experiment shows that APT achieves more than 50% saving on training energy and memory usage with limited accuracy loss. 20% more savings of training energy and memory usage can be achieved in return for a 1% sacrifice in accuracy loss., Comment: 6 pages

The dense wind environment (or circumstellar medium) may be ubiquitous for the regular Type II supernovae (SNe) before the explosion, the interaction of which with the SN ejecta could result in a wind breakout event. The shock generated by the interaction of the SN ejecta and the wind can accelerate the protons and subsequently the high-energy gamma-rays and neutrinos could arise from the inelastic pp collisions. In this work, we present the detailed calculations of gamma-ray and neutrino production for the regular Type II SNe. The calculation is executed by applying time-dependent evolutions of dynamic and proton distribution so that the emission could be shown at different times. Our results show, for the SN 2013fs-like wind environment, the multi-GeV and ~ few-100 TeV gamma-rays are detectable with a time window of several days at <~ 2-3 Mpc by Fermi/LAT and CTA during the ejecta-wind interaction, respectively, and can be detected at a further distance if the wind environment is denser. Besides, we found the contribution of the wind breakouts of regular Type II SNe to diffuse neutrino flux is subdominant by assuming all Type II SNe are SN 2013fs-like, whereas for a denser wind environment the contribution could be conspicuous above 300TeV., Comment: 11 pages, 4 figures; accepted for publication in ApJ

We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the statistical analysis of interest. Our algorithm uses a gradient-based approach which can be used with an arbitrary loss function provided the latter is differentiable. The speed and effectiveness of our algorithm for dimension reduction is demonstrated in the context of supervised classification of some real high-dimensional data sets from the bioinformatics literature.

We compare three methods for computing invariant Lyapunov exponents (LEs) in general relativity. They involve the geodesic deviation vector technique (M1), the two-nearby-orbits method with projection operations and with coordinate time as an independent variable (M2), and the two-nearby-orbits method without projection operations and with proper time as an independent variable (M3). An analysis indicates that M1 and M3 do not need any projection operation. In general, the values of LEs from the three methods are almost the same. As an advantage, M3 is simpler to use than M2. In addition, we propose to construct the invariant fast Lyapunov indictor (FLI) with two-nearby-trajectories and give its algorithm in order to quickly distinguish chaos from order. Taking a static axisymmetric spacetime as a physical model, we apply the invariant FLIs to explore the global dynamics of phase space of the system where regions of chaos and order are clearlyidentified., Comment: 12 pages, 5 figures

J. W. Moffat and V. T. Toth submitted recently a comment (arXiv:0903.5291) on our latest paper "Modified scalar-tensor-vector gravity theory and the constraint on its parameters" [Deng, et al., Phys. Rev. D 79, 044014 (2009); arXiv:0901.3730 ]. We reply to each of their comments and justify our work and conclusions. Especially, their general STVG (MOG) theory has to be modified to fit the modern precision experiments., Comment: 4 pages, no figure

A gravity theory called scalar-tensor-vector gravity (STVG) has been recently developed and succeeded in solar system, astrophysical and cosmological scales without dark matter [J. W. Moffat, J. Cosmol. Astropart. Phys. 03, 004 (2006)]. However, two assumptions have been used: (i) $B(r)=A^{-1}(r)$, where $B(r)$ and $A(r)$ are $g_{00}$ and $g_{rr}$ in the Schwarzschild coordinates (static and spherically symmetric); (ii) scalar field $G=Const.$ in the solar system. These two assumptions actually imply that the standard parametrized post-Newtonian parameter $\gamma=1$. In this paper, we relax these two assumptions and study STVG further by using the post-Newtonian (PN) approximation approach. With abandoning the assumptions, we find $\gamma\neq1$ in general cases of STVG. Then, a version of modified STVG (MSTVG) is proposed through introducing a coupling function of scalar field G: $\theta(G)$. We have derived the metric and equations of motion (EOM) in 1PN for general matter without specific equation of state and $N$ point masses firstly. Subsequently, the secular periastron precession $\dot{\omega}$ of binary pulsars in harmonic coordinates is given. After discussing two PPN parameters ($\gamma$ and $\beta$) and two Yukawa parameters ($\alpha$ and $\lambda$), we use $\dot{\omega}$ of four binary pulsars data (PSR B1913+16, PSR B1534+12, PSR J0737-3039 and PSR B2127+11C) to constrain the Yukawa parameters for MSTVG: $\lambda=(3.97\pm0.01)\times10^{8}$m and $\alpha=(2.40\pm0.02)\times10^{-8}$ if we fix $|2\gamma-\beta-1|=0$., Comment: 39 pages, 4 figures, accepted by PRD

In this paper, second post-Newtonian approximation of Einstein-aether theory is obtained by Chandrasekhar's approach. Five parameterized post-Newtonian parameters in first post-Newtonian approximation are presented after a time transformation and they are identical with previous works, in which $\gamma=1$, $\beta=1$ and two preferred-frame parameters remain. Meanwhile, in second post-Newtonian approximation, a parameter, which represents third order nonlinearity for gravity, is zero the same as in general relativity. For an application for future deep space laser ranging missions, we reduce the metric coefficients for light propagation in a case of $N$ point masses as a simplified model of the solar system. The resulting light deflection angle in second post-Newtonian approximation poses another constraint on the Einstein-aether theory., Comment: 29 pages, no figures

Deep space laser ranging missions like ASTROD I (Single-Spacecraft Astrodynamical Space Test of Relativity using Optical Devices) and ASTROD, together with astrometry missions like GAIA and LATOR will be able to test relativistic gravity to an unprecedented level of accuracy. More precisely, these missions will enable us to test relativistic gravity to $10^{-7}-10^{-9}$, and will require 2nd post-Newtonian approximation of relevant theories of gravity. The first post-Newtonian approximation is valid to $10^{-6}$ and the second post-Newtonian is valid to $10^{-12}$ in the solar system. The scalar-tensor theory is widely discussed and used in tests of relativistic gravity, especially after the interests in inflation, cosmological constant and dark energy in cosmology. In the Lagrangian, intermediate-range gravity term has a similar form as cosmological term. Here we present the full second post-Newtonian approximation of the scalar-tensor theory including viable examples of intermediate-range gravity. We use Chandrasekhar's approach to derive the metric coefficients and the equation of the hydrodynamics governing a perfect fluid in the 2nd post-Newtonian approximation in scalar-tensor theory; all terms inclusive of $O(c^{-4})$ are retained consistently in the equation of motion., Comment: 20 pages, COSPAR2006 H0.1-1

Lyapunov exponents (LEs) are key indicators of chaos in dynamical systems. In general relativity the classical definition of LE meets difficulty because it is not coordinate invariant and spacetime coordinates lose their physical meaning as in Newtonian dynamics. We propose a new definition of relativistic LE and give its algorithm in any coordinate system, which represents the observed changing law of the space separation between two neighboring particles (an 'observer' and a 'neighbor'), and is truly coordinate invariant in a curved spacetime., Comment: 13 pages, 3 figures

From H-alpha movies from Aryabhatta Research Institute of Observational Sciences and from Prairie View Solar Observatory, hard X-ray movies from RHESSI, line-of-sight magnetogram movies from SOHO/MDI, and vector magnetograms from Marshal Space Flight Center, we examine the magnetic structure and evolution of the large delta-sunspot active region NOAA 10486 in relation to the onset and development of the two X-class flares that occurred in this active region on 28 and 29 October 2003. We find evidence that each of these flares was triggered by strongly sheared magnetic field via "tether-cutting" reconnection with adjacent/overlying strongly sheared field. In the first flare, the initial brightening in H-alpha: (1) was partly rooted in emerging sheared magnetic field along the edge of the large positive-polarity flux domain of the delta sunspot, and (2) consisted of four flare kernels, two in negative magnetic flux and two in positive magnetic flux. In the second flare, the brightening started in the core of a Z-shaped sigmoidal sheared magnetic field and the inner two of four H-alpha kernels were visible in 30-50 Kev hard X-ray image from RHESSI. Each flare spread from the initial quadrupolar brightening to develop into a much larger two-ribbon flare straddling a much more extensive swath of strongly sheared field along the edge of the large positive-flux domain of the delta sunspot, the first flare on the leading side and the second flare on the trailing side of this domain. Thus, localized internal reconnection triggered the explosion of these extensive sheared magnetic fields.