arXiv subjects

Lin Lin

Publications and source records attributed to Lin Lin.

At least 199 records · Page 11Linked to original sources

arXiv2021

Star Formation and Quenching of Central Galaxies from Stacked HI Measurements

We quantitatively investigate the dependence of central galaxy HI mass ($M_{\rm HI}$) on the stellar mass ($M_\ast$), halo mass ($M_{\rm h}$), star formation rate (SFR), and central stellar surface density within 1 kpc ($Σ_1$), taking advantage of the HI spectra stacking technique using both the Arecibo Fast Legacy ALFA Survey and the Sloan Digital Sky Survey. We find that the shapes of $M_{\rm HI}$-$M_{\rm h}$ and $M_{\rm HI}$-$M_\ast$ relations are remarkably similar for both star-forming and quenched galaxies, with massive quenched galaxies having constantly lower HI masses of around 0.6 dex. This similarity strongly suggests that neither halo mass nor stellar mass is the direct cause of quenching, but rather the depletion of HI reservoir. While the HI reservoir for low-mass galaxies of $M_\ast<10^{10.5}M_\odot$ strongly increases with $M_{\rm h}$, more massive galaxies show no significant dependence of $M_{\rm HI}$ on $M_{\rm h}$, indicating the effect of halo to determine the smooth cold gas accretion. We find that the star formation and quenching of central galaxies are directly regulated by the available HI reservoir, with an average relation of ${\rm SFR}\propto M_{\rm HI}^{2.75}/M_\ast^{0.40}$, implying a quasi-steady state of star formation. We further confirm that galaxies are depleted of their HI reservoir once they drop off the star-formation main sequence and there is a very tight and consistent correlation between $M_{\rm HI}$ and $Σ_1$ in this phase, with $M_{\rm HI}\proptoΣ_1^{-2}$. This result is in consistent with the compaction-triggered quenching scenario, with galaxies going through three evolutionary phases of cold gas accretion, compaction and post-compaction, and quenching.

astro-ph.GA↗

arXiv2021

Time-dependent unbounded Hamiltonian simulation with vector norm scaling

The accuracy of quantum dynamics simulation is usually measured by the error of the unitary evolution operator in the operator norm, which in turn depends on certain norm of the Hamiltonian. For unbounded operators, after suitable discretization, the norm of the Hamiltonian can be very large, which significantly increases the simulation cost. However, the operator norm measures the worst-case error of the quantum simulation, while practical simulation concerns the error with respect to a given initial vector at hand. We demonstrate that under suitable assumptions of the Hamiltonian and the initial vector, if the error is measured in terms of the vector norm, the computational cost may not increase at all as the norm of the Hamiltonian increases using Trotter type methods. In this sense, our result outperforms all previous error bounds in the quantum simulation literature. Our result extends that of [Jahnke, Lubich, BIT Numer. Math. 2000] to the time-dependent setting. We also clarify the existence and the importance of commutator scalings of Trotter and generalized Trotter methods for time-dependent Hamiltonian simulations.

quant-ph↗

arXiv2021

Staggered mesh method for correlation energy calculations of solids: Second order Møller-Plesset perturbation theory

The calculation of the MP2 correlation energy for extended systems can be viewed as a multi-dimensional integral in the thermodynamic limit, and the standard method for evaluating the MP2 energy can be viewed as a trapezoidal quadrature scheme. We demonstrate that existing analysis neglects certain contributions due to the non-smoothness of the integrand, and may significantly underestimate finite-size errors. We propose a new staggered mesh method, which uses two staggered Monkhorst-Pack meshes for occupied and virtual orbitals, respectively, to compute the MP2 energy. The staggered mesh method circumvents a significant error source in the standard method, in which certain quadrature nodes are always placed on points where the integrand is discontinuous. One significant advantage of the proposed method is that there are no tunable parameters, and the additional numerical effort needed can be negligible compared to the standard MP2 calculation. Numerical results indicate that the staggered mesh method can be particularly advantageous for quasi-1D systems, as well as quasi-2D and 3D systems with certain symmetries.

physics.comp-ph↗

arXiv2021

Simultaneous 2.25/8.60 GHz observations of the newly discovered magnetar -- Swift J1818.0-1607

Swift J1818.0-1607 discovered in early 2020 is not only the fifth magnetar known with periodic radio pulsations but also the fastest rotating one. Simultaneous 2.25 and 8.60 GHz observations of Swift J1818.0-1607 were carried out with Shanghai Tian Ma Radio Telescope (TMRT) from MJD 58936 to 59092. The spin-frequency $ν$ and first-order derivative $\dotν$ of this magnetar were obtained with piecewise fitting method because of its instable timing properties. We found that the amplitude of short-term $\dotν$ fluctuations decreased with time, and the long-term declining trend of $ν$ discovered previously continued in our observations. The best fitted long-term $\dotν$ were about $-2.25 \times 10^{-11}~s^{-2}$ using our observation data spanning 156 days. The derived characteristic age was about 522 yr, supporting the recent viewpoint that this magnetar may be older than initially thought shortly after its discovery. The flux density of this magnetar was increased at both 2.25 and 8.60 GHz during our observations, and its radio spectrum became flatter at the same time. We also detected bright-quiet type emission mode switching in Swift J1818.0-1607.

astro-ph.HE↗

arXiv2021

NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy

Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring possible hierarchy among the covariates. This strategy may not be optimal for complex large-scale problems, where hierarchical information often exists among those test-level covariates. We propose NeurT-FDR which boosts statistical power and controls FDR for multiple hypothesis testing while leveraging the hierarchy among test-level covariates. Our method parametrizes the test-level covariates as a neural network and adjusts the feature hierarchy through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR has strong FDR guarantees and makes substantially more discoveries in synthetic and real datasets compared to competitive baselines.

stat.ML↗

arXiv2021

Bursts before Burst: A Comparative Study on FRB 200428-associated and FRB-absent X-ray Bursts from SGR J1935+2154

Accompanied by an X-ray burst, the fast radio burst (FRB) FRB 200428 was recently confirmed as originating from the Galactic magnetar soft gamma repeater (SGR) SGR J1935+2154. Just before and after FRB 200428 was detected, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) had been monitoring SGR J1935+2154 for eight hours. From UTC 2020 April 27 23:55:00 to 2020 April 28 00:50:37, FAST detected no pulsed radio emission from SGR J1935+2154, while Fermi/Gamma-ray Burst Monitor registered 34 bursts in the X/soft $γ$-ray band, forming a unique sample of X-ray bursts in the absence of FRBs. After a comprehensive analysis on light curves, time-integrated, and time-resolved spectral properties of these FRB-absent X-ray bursts, we compare this sample with the FRB-associated X-ray burst detected by Insight-HXMT, INTEGRAL, and Konus-Wind. The FRB-associated burst distinguishes itself from other X-ray bursts by its nonthermal spectrum and a higher spectral peak energy, but otherwise is not atypical. We also compare the cumulative energy distribution of our X-ray burst sample with that of first repeating FRB source, FRB 121102, with the calibration of FRB 200428-X-ray burst association. We find a similarity between the two, offering indirect support of the magnetar origin of cosmological FRBs. The event rate density of magnetar bursts is about $\sim 150$ times higher than the FRB event rate density at the energy of FRB 200428. This again suggests that, if all FRBs originate from magnetars, only a small fraction of X-ray bursts are associated with FRBs.

astro-ph.HE↗

arXiv2020

A comparison between repeating bursts of FRB 121102 and giant pulses from Crab pulsar and its applications

There are some similarities between bursts of repeating fast radio bursts (FRBs) and giant pulses (GPs) of pulsars. To explore possible relations between them, we study the cumulative energy distributions of these two phenomena using the observations of repeating FRB 121102 and the GPs of Crab pulsar. We find that the power-law slope of GPs (with fluence $\geq$130 Jy ms) is $2.85\pm0.10$. The energy distribution of FRB 121102 can be well fitted by a smooth broken power-law function. For the bursts of FRB 121102 above the break energy (1.22 $\times 10^{37}$ erg), the best-fitting slope is $2.90_{-0.44}^{+0.55}$, similar to the index of GPs at the same observing frequency ($\sim$1.4 GHz). We further discuss the physical origin of the repeating FRB 121102 in the framework of the super GPs model. And we find that the super GPs model involving a millisecond pulsar is workable and favored for explaining FRB 121102 despite that the magnetar burst model is more popular.

astro-ph.HE↗

arXiv2020

Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks

Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses a formidable challenge. Using reinforcement learning (RL), we present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. The hybrid policy is modeled by a deep autoregressive neural network to capture causality. We employ the algorithm to prepare the ground state of the nonintegrable quantum Ising model in a unitary process, parametrized by a generalized quantum approximate optimization ansatz: the RL agent solves the discrete combinatorial problem of constructing the optimal sequences of unitaries out of a predefined set and, at the same time, it optimizes the continuous durations for which these unitaries are applied. We demonstrate the noise-robust features of the agent by considering three sources of uncertainty: classical and quantum measurement noise, and errors in the control unitary durations. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.

quant-ph↗

arXiv2020

Near-optimal ground state preparation

Preparing the ground state of a given Hamiltonian and estimating its ground energy are important but computationally hard tasks. However, given some additional information, these problems can be solved efficiently on a quantum computer. We assume that an initial state with non-trivial overlap with the ground state can be efficiently prepared, and the spectral gap between the ground energy and the first excited energy is bounded from below. With these assumptions we design an algorithm that prepares the ground state when an upper bound of the ground energy is known, whose runtime has a logarithmic dependence on the inverse error. When such an upper bound is not known, we propose a hybrid quantum-classical algorithm to estimate the ground energy, where the dependence of the number of queries to the initial state on the desired precision is exponentially improved compared to the current state-of-the-art algorithm proposed in [Ge et al. 2019]. These two algorithms can then be combined to prepare a ground state without knowing an upper bound of the ground energy. We also prove that our algorithms reach the complexity lower bounds by applying it to the unstructured search problem and the quantum approximate counting problem.

quant-ph↗

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↗