arXiv subjects

Lin Lin

Publications and source records attributed to Lin Lin.

At least 199 records · Page 11Linked to original sources

arXiv2020

Persistent Emission Properties of SGR J1935+2154 During Its 2020 Active Episode

We present detailed spectral and temporal characteristics of the persistent X-ray emission of SGR J1935+2154 based on our XMM-Newton and Chandra observations taken in the aftermath of its April 2020 burst storm, during which hundreds of energetic X-ray bursts were emitted, including one associated with an extraordinary fast radio burst. We clearly detect the pulsed X-ray emission in the XMM-Newton data. An average spin-down rate of 1.6$\times$10$^{-11}$ s s$^{-1}$ is obtained using our spin period measurement combined with three earlier values reported from the same active episode. Our investigations of the XMM-Newton and Chandra spectra with a variety of phenomenological and physically-motivated models, concluded that the magnetic field topology of SGR J1935+2154 is most likely highly non-dipolar. The spectral models indicate that surface field strengths in somewhat localized regions substantially exceed the polar value of 4.4$\times$10$^{14}$ G inferred from a spin-down torque associated with a rotating magnetic dipole.

astro-ph.HE↗

arXiv2020

The NICER View of the 2020 Burst Storm and Persistent Emission of SGR 1935+2154

We report on NICER observations of the Magnetar SGR~1935+2154, covering its 2020 burst storm and long-term persistent emission evolution up to $\sim90$ days post outburst. During the first 1120~seconds taken on April 28 00:40:58 UTC we detect over 217 bursts, corresponding to a burst rate of $>0.2$ bursts s$^{-1}$. Three hours later the rate is at 0.008 bursts s$^{-1}$, remaining at a comparatively low level thereafter. The $T_{90}$ burst duration distribution peaks at 840~ms; the distribution of waiting times to the next burst is fit with a log-normal with an average of 2.1 s. The 1-10 keV burst spectra are well fit by a blackbody, with an average temperature and area of $kT=1.7$ keV and $R^2=53$ km$^2$. The differential burst fluence distribution over $\sim3$ orders of magnitude is well modeled with a power-law form $dN/dF\propto F^{-1.5\pm0.1}$. The source persistent emission pulse profile is double-peaked hours after the burst storm. We find that the bursts peak arrival times follow a uniform distribution in pulse phase, though the fast radio burst associated with the source aligns in phase with the brighter peak. We measure the source spin-down from heavy-cadence observations covering days 21 to 39 post-outburst, $\dotν=-3.72(3)\times10^{-12}$ Hz s$^{-1}$; a factor 2.7 larger than the value measured after the 2014 outburst. Finally, the persistent emission flux and blackbody temperature decrease rapidly in the early stages of the outburst, reaching quiescence 40 days later, while the size of the emitting area remains unchanged.

astro-ph.HE↗

arXiv2020

Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems

We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigenstate of a given Hamiltonian, if we have access to an initial state with non-trivial overlap with the target eigenstate and have a reasonable lower bound for the spectral gap. We apply this algorithm to the quantum linear system problem (QLSP), and present two algorithms based on quantum adiabatic computing (AQC) and quantum Zeno effect respectively. Both algorithms prepare the final solution as a pure state, and achieves the near optimal $\mathcal{\widetilde{O}}(dκ\log(1/ε))$ query complexity for a $d$-sparse matrix, where $κ$ is the condition number, and $ε$ is the desired precision. Neither algorithm uses phase estimation or amplitude amplification.

quant-ph↗

arXiv2020

Discontinuous Galerkin method with Voronoi partitioning for Quantum Simulation of Chemistry

Molecular orbitals based on the linear combination of Gaussian type orbitals are arguably the most employed discretization in quantum chemistry simulations, both on quantum and classical devices. To circumvent a potentially dense two-body interaction tensor and obtain lower asymptotic costs for quantum simulations of chemistry, the discontinuous Galerkin (DG) procedure using a rectangular partitioning strategy was recently piloted [McClean et al, New J. Phys. 22, 093015, 2020]. The DG approach interpolates in a controllable way between a compact description of the two-body interaction tensor through molecular orbitals and a diagonal characterization through primitive basis sets, such as a planewave dual basis set. The DG procedure gives rise to a block-diagonal representation of the two-body interaction with reduced number of two-electron repulsion integrals, which in turn reduces the cost of quantum simulations. In the present work we extend this approach to be applicable to molecular and crystalline systems of arbitrary geometry. We take advantage of the flexibility of the planewave dual basis set, and combine the discontinuous Galerkin procedure with a general partitioning strategy based on the Voronoi decomposition. We numerically investigate the performance, at the mean-field and correlated levels, with quasi-1D, 2D and 3D partitions using hydrogen chains, H$_4$, CH$_4$ as examples, respectively. We also apply the method to graphene as a prototypical example of crystalline systems.

physics.comp-ph↗

arXiv2020

Pinning Stabilizer Design for Large-Scale Probabilistic Boolean Networks

This paper investigates the stabilization of probabilistic Boolean networks (PBNs) via a novel pinning control strategy based on network structure. In a PBN, the evolution equation of each gene switches among a collection of candidate Boolean functions with probability distributions that govern the activation frequency of each Boolean function. Owing to the stochasticity, the uniform state feedback controller, independent of switching signal, might be out of work, and in this case, the non-uniform state feedback controller is required. Subsequently, a criterion is derived to determine whether uniform controllers is applicable to achieve stabilization. It is worth pointing out that the pinning control designed in this paper is based on the network structure, which only requires local in-neighbors' information, rather than global information (state transition matrix). Moreover, this pinning control strategy reduces the computational complexity from $O(2^{2n})$ to $O(n2^α)$, and thus it has the ability to handle some large-scale networks, especially the networks with sparse connections. Finally, the mammalian cell-cycle encountering a mutated phenotype is modelled by a PBN to demonstrate the obtained results.

eess.SY↗

arXiv2020

Towards sharp error analysis of extended Lagrangian molecular dynamics

The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrained latent variables. The XLMD method relaxes the constraints by introducing a fictitious mass $\varepsilon$ for the latent variables, solving a set of singularly perturbed ordinary differential equations. While favorable numerical performance of XLMD has been demonstrated in several different contexts in the past decade, mathematical analysis of the method remains scarce. We propose the first error analysis of the XLMD method in the context of a classical polarizable force field model. While the dynamics with respect to the atomic degrees of freedom are general and nonlinear, the key mathematical simplification of the polarizable force field model is that the constraints on the latent variables are given by a linear system of equations. We prove that when the initial value of the latent variables is compatible in a sense that we define, XLMD converges as the fictitious mass $\varepsilon$ is made small with $\mathcal{O}(\varepsilon)$ error for the atomic degrees of freedom and with $\mathcal{O}(\sqrt{\varepsilon})$ error for the latent variables, when the dimension of the latent variable $d'$ is 1. Furthermore, when the initial value of the latent variables is improved to be optimally compatible in a certain sense, we prove that the convergence rate can be improved to $\mathcal{O}(\varepsilon)$ for the latent variables as well. Numerical results verify that both estimates are sharp not only for $d' =1$, but also for arbitrary $d'$. In the setting of general $d'$, we do obtain convergence, but with the non-sharp rate of $\mathcal{O}(\sqrt{\varepsilon})$ for both the atomic and latent variables.

math.NA↗

arXiv2020

Efficient Long-Range Convolutions for Point Clouds

The efficient treatment of long-range interactions for point clouds is a challenging problem in many scientific machine learning applications. To extract global information, one usually needs a large window size, a large number of layers, and/or a large number of channels. This can often significantly increase the computational cost. In this work, we present a novel neural network layer that directly incorporates long-range information for a point cloud. This layer, dubbed the long-range convolutional (LRC)-layer, leverages the convolutional theorem coupled with the non-uniform Fourier transform. In a nutshell, the LRC-layer mollifies the point cloud to an adequately sized regular grid, computes its Fourier transform, multiplies the result by a set of trainable Fourier multipliers, computes the inverse Fourier transform, and finally interpolates the result back to the point cloud. The resulting global all-to-all convolution operation can be performed in nearly-linear time asymptotically with respect to the number of input points. The LRC-layer is a particularly powerful tool when combined with local convolution as together they offer efficient and seamless treatment of both short and long range interactions. We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential. We also exploit the induced two-level decomposition and propose an efficient strategy to train the combined architecture with a reduced number of samples.

stat.ML↗

arXiv2020

Fermi/GBM View of the 2019 and 2020 Burst Active Episodes of SGR J1935+2154

We present temporal and time-integrated spectral analyses of 148 bursts from the latest activation of SGR J1935+2154, observed with Fermi/GBM from October 4th 2019 through May 20th 2020, excluding a ~130 s segment with a very high burst density on April 27th 2020. The 148 bursts presented here, are slightly longer and softer than bursts from earlier activations of SGR J1935+2154, as well as from other magnetars. The long-term spectral evolution trend is interpreted as being associated with an increase in the average plasma loading of the magnetosphere during bursts. We also find a trend of increased burst activity from SGR J1935+2154 since its discovery in 2014. Finally, we find no association of typical radio bursts with X-ray bursts from the source; this contrasts the association of FRB 200428 with an SGR J1935+2154 X-ray burst, to date unique among the magnetar population.

astro-ph.HE↗

arXiv2020

SDSS-IV MaNGA: the indispensable role of bars in enhancing the central star formation of low-$z$ galaxies

We analyse two-dimensional maps and radial profiles of EW(H$α$), EW(H$δ_A$), and D$_n$(4000) of low-redshift galaxies using integral field spectroscopy from the MaNGA survey. Out of $\approx1400$ nearly face-on late-type galaxies with a redshift $z<0.05$, we identify 121 "turnover" galaxies that each have a central upturn in EW(H$α$), EW(H$δ_A$) and/or a central drop in D$_n$(4000), indicative of ongoing/recent star formation. The turnover features are found mostly in galaxies with a stellar mass above $\sim$10$^{10}$ M$_{\odot}$ and NUV-$r$ colour less than $\approx5$. The majority of the turnover galaxies are barred, with a bar fraction of 89$\pm$3\%. Furthermore, for barred galaxies the radius of the central turnover region is found to tightly correlate with one third of the bar length. Comparing the observed and the inward extrapolated star formation rate surface density, we estimate that the central SFR have been enhanced by an order of magnitude. Conversely, only half of the barred galaxies in our sample have a central turnover feature, implying that the presence of a bar is not sufficient to lead to a central SF enhancement. We further examined the SF enhancement in paired galaxies, as well as the local environment, finding no relation. This implies that environment is not a driving factor for central SF enhancement in our sample. Our results reinforce both previous findings and theoretical expectation that galactic bars play a crucial role in the secular evolution of galaxies by driving gas inflow and enhancing the star formation and bulge growth in the center.

astro-ph.GA↗

arXiv2020

Semidefinite relaxation of multi-marginal optimal transport for strictly correlated electrons in second quantization

We consider the strictly correlated electron (SCE) limit of the fermionic quantum many-body problem in the second-quantized formalism. This limit gives rise to a multi-marginal optimal transport (MMOT) problem. Here the marginal state space for our MMOT problem is the binary set $\{0,1\}$, and the number of marginals is the number $L$ of sites in the model. The costs of storing and computing the exact solution of the MMOT problem both scale exponentially with respect to $L$. We propose an efficient convex relaxation which can be solved by semidefinite programming (SDP). In particular, the semidefinite constraint is only of size $2L\times 2L$. Moreover, the SDP-based method yields an approximation of the dual potential needed to the perform self-consistent field iteration in the so-called Kohn-Sham SCE framework, which, once converged, yields a lower bound for the total energy of the system. We demonstrate the effectiveness of our methods on spinless and spinful Hubbard-type models. Numerical results indicate that our relaxation methods yield tight lower bounds for the optimal cost, in the sense that the error due to the semidefinite relaxation is much smaller than the intrinsic modeling error of the Kohn-Sham SCE method. We also describe how our relaxation methods generalize to arbitrary MMOT problems with pairwise cost functions.

math.OC↗

arXiv2020

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learning-based simulation protocol (Deep Potential Molecular Dynamics), while retaining {\it ab initio} accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining $91$ PFLOPS in double precision ($45.5\%$ of the peak) and {$162$/$275$ PFLOPS in mixed-single/half precision}. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with {\it ab initio} accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling.

physics.comp-ph↗

arXiv2020

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU version is 7 times faster than the CPU version with the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions.

physics.comp-ph↗

arXiv2020

Using models to improve optimizers for variational quantum algorithms

Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer. The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting. In this work, we introduce two optimization methods and numerically compare their performance with common methods in use today. The methods are surrogate model-based algorithms designed to improve reuse of collected data. They do so by utilizing a least-squares quadratic fit of sampled function values within a moving trusted region to estimate the gradient or a policy gradient. To make fair comparisons between optimization methods, we develop experimentally relevant cost models designed to balance efficiency in testing and accuracy with respect to cloud quantum computing systems. The results here underscore the need to both use relevant cost models and optimize hyperparameters of existing optimization methods for competitive performance. The methods introduced here have several practical advantages in realistic experimental settings, and we have used one of them successfully in a separately published experiment on Google's Sycamore device.

quant-ph↗

arXiv2020

The SFR-radius connection: data and implications for wind strength and halo concentration

This paper is one in a series that explores the importance of radius as a second parameter in galaxy evolution. The topic investigated here is the relationship between star formation rate (SFR) and galaxy radius ($R_{\rm e}$) for main-sequence star-forming galaxies. The key observational result is that, over a wide range of stellar mass and redshift in both CANDELS and SDSS, there is little trend between SFR and $R_{\rm e}$ at fixed stellar mass. The Kennicutt-Schmidt law, or any similar density-related star formation law, then implies that smaller galaxies must have lower gas fractions than larger galaxies (at fixed $M_{\ast}$), and this is supported by observations of local star-forming galaxies. We investigate the implication by adopting the equilibrium "bathtub" model: the ISM gas mass is assumed to be constant over time and the net star formation rate is the difference between the accretion rate of gas onto the galaxy from the halo and the outflow rate due to winds. To match the observed null correlation between SFR and radius, the bathtub model requires that smaller galaxies at fixed mass have weaker galactic winds. Our hypothesis is that galaxies are a 2-dimensional family whose properties are set mainly by halo mass and concentration. Galaxy radius and accretion rate plausibly both depend on halo concentration, which predicts how wind strength should vary with $R_{\rm e}$ and SFR.

astro-ph.GA↗

arXiv2020

Spatially-resolved Stellar Population Properties of the M 51-NGC 5195 System from Multi-wavelength Photometric Data

Using multi-band photometric images of M 51 and its companion NGC 5195 from ultraviolet to optical and infrared, we investigate spatially resolved stellar population properties of this interacting system with stellar population synthesis models. The observed IRX is used to constrain dust extinction. Stellar mass is also inferred from the model fitting. By fitting observed spectral energy distributions (SEDs) with synthetical ones, we derive two-dimensional distributions of stellar age, metallicity, dust extinction, and stellar mass. In M 51, two grand-designed spiral arms extending from the bulge show young age, rich metallicity, and abundant dust. The inter-arm regions are filled with older, metal-poorer, and less dusty stellar populations. Except for the spiral arm extending from M 51 into NGC 5195, the stellar population properties of NGC 5195 are quite featureless. NGC 5195 is much older than M 51, and its core is very dusty with $A_V$ up to 1.67 mag and dense in stellar mass surface density. The close encounters might drive the dust in the spiral arm of M51 into the center of NGC 5195.

astro-ph.GA↗

arXiv2020

Burst properties of the most recurring transient magnetar SGR J1935+2154

We present timing and time-integrated spectral analysis of 127 bursts from SGR J1935+2154. These bursts were observed with the Gamma-ray Burst Monitor on the Fermi Gamma-ray Space Telescope and the Burst Alert Telescope on the Neil Gehrels Swift Observatory during the source's four active episodes from 2014 to 2016. This activation frequency makes SGR J1935+2154 the most burst prolific transient magnetar. We find the average duration of all the detected bursts to be much shorter than the typical, anticipated value. We fit the burst time-integrated spectra with two black-body functions, a Comptonized model and three other simpler models. Bursts from SGR J1935+2154 exhibit similar spectral properties to other magnetars, with the exception of the power law index from the Comptonized model, which correlates with burst fluence. We find that the durations and both black-body temperatures of the bursts have significantly evolved across the four active episodes. We also find that the burst time history exhibits two trends, which are strongly correlated with the decay of the persistent emission in each outburst.

astro-ph.HE↗

arXiv2020

Physical Properties of H II Regions in M51 from Spectroscopic Observations

M51 and NGC 5195 is an interacting system that can be explored in great details with ground-based telescopes. The H II regions in M51 were observed using the 2.16 m telescope of the National Astronomical Observatories of the Chinese Academy of Sciences and the 6.5 m Multiple Mirror Telescope with spatial resolution of less than $\sim100$ pc. We obtain a total of 113 spectra across the galaxy and combine the literature data of Croxall et al. to derive a series of physical properties, including the gas-phase extinction, stellar population age, star formation rate (SFR) surface density, and oxygen abundance. The spatial distributions and radial profiles of these properties are investigated in order to study the characteristics of M51 and the clues to the formation and evolution of this galaxy. M51 presents a mild radial extinction gradient. The lower gas-phase extinction in the north spiral arms compared to the south arms are possibly caused by the past encounters with the companion galaxy of NGC 5195. A number of H II regions have the stellar age between 50 and 500 Myr, consistent with the recent interaction history by simulations in the literatures. The SFR surface density presents a mild radial gradient, which is ubiquitous in spiral galaxies. There is a negative metallicity gradient of $-0.08$ dex $R_{e}^{-1}$ in the disk region, which is also commonly found in many spiral galaxies. It is supported by the "inside-out" scenario of galaxy formation. We find a positive abundance gradient of 0.26 dex $R_{e}^{-1}$ in the inner region. There are possible reasons causing the positive gradient, including the freezing of the chemical enrichment due to the star-forming quenching in the bulge and the gas infall and dilution due to the pseudobulge growth and/or galactic interaction.

astro-ph.GA↗

arXiv2020

ELSI -- An Open Infrastructure for Electronic Structure Solvers

Routine applications of electronic structure theory to molecules and periodic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal basis sets, overlap matrices. System sizes can range from few to thousands or, in some examples, millions of atoms. Different discretization schemes (basis sets) and different system geometries (finite non-periodic vs. infinite periodic boundary conditions) yield matrices with different structures. The ELectronic Structure Infrastructure (ELSI) project provides an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. In this paper, we present recent improvements and developments inside ELSI, mainly covering (1) new solvers connected to the interface, (2) matrix layout and communication adapted for parallel calculations of periodic and/or spin-polarized systems, (3) routines for density matrix extrapolation in geometry optimization and molecular dynamics calculations, and (4) general utilities such as parallel matrix I/O and JSON output. The ELSI interface has been integrated into four electronic structure code projects (DFTB+, DGDFT, FHI-aims, SIESTA), allowing us to rigorously benchmark the performance of the solvers on an equal footing. Based on results of a systematic set of large-scale benchmarks performed with Kohn-Sham density-functional theory and density-functional tight-binding theory, we identify factors that strongly affect the efficiency of the solvers, and propose a decision layer that assists with the solver selection process. Finally, we describe a reverse communication interface encoding matrix-free iterative solver strategies that are amenable, e.g., for use with planewave basis sets.

physics.comp-ph↗