arXiv subjects

Lang Liu

Publications and source records attributed to Lang Liu.

At least 55 records · Page 3Linked to original sources

arXiv2023

Expanding proton dripline by employing a number of muons

Through mean-field calculations, we demonstrate that, in a large $Z$ nucleus binding multiple muons, these heavy leptons localize within a few dozen femtometers of the nucleus. The mutual Coulomb interactions between the muons and the protons can lead to a substantial decrease in proton chemical potential, surpassing 1 MeV. These findings allow for expanding the proton-dripline on the nuclear chart in principle, suggesting the possible production of nuclei with Z around 120.

nucl-th↗

arXiv2023

Gravitational and electromagnetic radiation from binary black holes with electric and magnetic charges: Hyperbolic orbits on a cone

We derive the hyperbolic orbit of binary black holes with electric and magnetic charges. In the low-velocity and weak-field regime, by using the Newtonian method, we calculate the total emission rate of energy due to gravitational and electromagnetic radiation from binary black holes with electric and magnetic charges in hyperbolic orbits. Moreover, we develop a formalism to derive the merger rate of binary black holes with electric and magnetic charges from the two-body dynamical capture. We apply the formalism to investigate the effects of the charges on the merger rate for the near-extremal case and find that the effects cannot be ignored.

gr-qc↗

arXiv2023

Constraining the Merger History of Primordial-Black-Hole Binaries from GWTC-3

Primordial black holes (PBHs) can be not only cold dark matter candidates but also progenitors of binary black holes observed by LIGO-Virgo-KAGRA (LVK) Collaboration. The PBH mass can be shifted to the heavy distribution if multi-merger processes occur. In this work, we constrain the merger history of PBH binaries using the gravitational wave events from the third Gravitational-Wave Transient Catalog (GWTC-3). Considering four commonly used PBH mass functions, namely the log-normal, power-law, broken power-law, and critical collapse forms, we find that the multi-merger processes make a subdominant contribution to the total merger rate. Therefore, the effect of merger history can be safely ignored when estimating the merger rate of PBH binaries. We also find that GWTC-3 is best fitted by the log-normal form among the four PBH mass functions and confirm that the stellar-mass PBHs cannot dominate cold dark matter.

astro-ph.CO↗

arXiv2023

Classification of pairing phase transition in the hot nucleus

The hot nucleus $^{162}\mathrm{Dy}$ is investigated using covariant density functional theory, where the shell-model-like approach treats the pairing correlation. Lee-Yang's theorem is applied to classify the pairing phase transition by analyzing the distribution of zeros of the partition function in the complex temperature plane. The distribution of zeros of the partition function converges with increasing particle numbers and illustrates the characteristics of the phase transition. In our calculations, we determine the first order of the phase transition near the critical temperature. Different seniority states show the pairing phase transition from a superfluid to a normal phase, ranging from fully paired states to completely unpaired states.

nucl-th↗

arXiv2023

Is the Observable Universe Consistent with the Cosmological Principle?

The Cosmological Principle (CP) -- the notion that the Universe is spatially isotropic and homogeneous on large scales -- underlies a century of progress in cosmology. It is conventionally formulated through the Friedmann-Lemaître-Robertson-Walker (FLRW) cosmologies as the spacetime metric, and culminates in the successful and highly predictive $Λ$-Cold-Dark-Matter ($Λ$CDM) model. Yet, tensions have emerged within the $Λ$CDM model, most notably a statistically significant discrepancy in the value of the Hubble constant, $H_0$. Since the notion of cosmic expansion determined by a single parameter is intimately tied to the CP, implications of the $H_0$ tension may extend beyond $Λ$CDM to the CP itself. This review surveys current observational hints for deviations from the expectations of the CP, highlighting synergies and disagreements that warrant further study. Setting aside the debate about individual large structures, potential deviations from the CP include variations of cosmological parameters on the sky, discrepancies in the cosmic dipoles, and mysterious alignments in quasar polarizations and galaxy spins. While it is possible that a host of observational systematics are impacting results, it is equally plausible that precision cosmology may have outgrown the FLRW paradigm, an extremely pragmatic but non-fundamental symmetry assumption.

astro-ph.CO↗

arXiv2023

Testing primordial black hole and measuring the Hubble constant with multiband gravitational-wave observations

There exist two kinds of stochastic gravitational-wave backgrounds associated with the primordial curvature perturbations. One is called induced gravitational wave due to the nonlinear coupling of curvature perturbations to tensor perturbations, while the other is produced by coalescences of binary primordial black holes formed when the large amplitude curvature perturbations reenter the horizon in the radiation dominant era. In this paper we find a quite useful relation for the peak frequencies of these two stochastic gravitational-wave backgrounds. This relation can not only offer a smoking-gun criterion for the existence of primordial black holes, but also provide a method for measuring the Hubble constant $H_0$ by multiband observations of the stochastic gravitational-wave backgrounds.

astro-ph.CO↗

arXiv2022

Asymptotics of Discrete Schrödinger Bridges via Chaos Decomposition

Consider the problem of matching two independent i.i.d. samples of size $N$ from two distributions $P$ and $Q$ in $\mathbb{R}^d$. For an arbitrary continuous cost function, the optimal assignment problem looks for the matching that minimizes the total cost. We consider instead in this paper the problem where each matching is endowed with a Gibbs probability weight proportional to the exponential of the negative total cost of that matching. Viewing each matching as a joint distribution with $N$ atoms, we then take a convex combination with respect to the above Gibbs probability measure. We show that this resulting random joint distribution converges, as $N\rightarrow \infty$, to the solution of a variational problem, introduced by Föllmer, called the Schrödinger problem. We also derive the first two error terms of orders $N^{-1/2}$ and $N^{-1}$, respectively. This gives us central limit theorems for integrated test functions, including for the cost of transport, and second order Gaussian chaos limits when the limiting Gaussian variance is zero. The proofs are based on a novel chaos decomposition of the discrete Schrödinger bridge by polynomial functions of the pair of empirical distributions as the first and second order Taylor approximations in the space of measures. This is achieved by extending the Hoeffding decomposition from the classical theory of U-statistics.

math.PR↗

arXiv2022

Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks

We propose Distribution Embedding Networks (DEN) for classification with small data. In the same spirit of meta-learning, DEN learns from a diverse set of training tasks with the goal to generalize to unseen target tasks. Unlike existing approaches which require the inputs of training and target tasks to have the same dimension with possibly similar distributions, DEN allows training and target tasks to live in heterogeneous input spaces. This is especially useful for tabular-data tasks where labeled data from related tasks are scarce. DEN uses a three-block architecture: a covariate transformation block followed by a distribution embedding block and then a classification block. We provide theoretical insights to show that this architecture allows the embedding and classification blocks to be fixed after pre-training on a diverse set of tasks; only the covariate transformation block with relatively few parameters needs to be fine-tuned for each new task. To facilitate training, we also propose an approach to synthesize binary classification tasks, and demonstrate that DEN outperforms existing methods in a number of synthetic and real tasks in numerical studies.

stat.ML↗

arXiv2022

Confidence Sets under Generalized Self-Concordance

This paper revisits a fundamental problem in statistical inference from a non-asymptotic theoretical viewpoint $\unicode{x2013}$ the construction of confidence sets. We establish a finite-sample bound for the estimator, characterizing its asymptotic behavior in a non-asymptotic fashion. An important feature of our bound is that its dimension dependency is captured by the effective dimension $\unicode{x2013}$ the trace of the limiting sandwich covariance $\unicode{x2013}$ which can be much smaller than the parameter dimension in some regimes. We then illustrate how the bound can be used to obtain a confidence set whose shape is adapted to the optimization landscape induced by the loss function. Unlike previous works that rely heavily on the strong convexity of the loss function, we only assume the Hessian is lower bounded at optimum and allow it to gradually becomes degenerate. This property is formalized by the notion of generalized self-concordance which originated from convex optimization. Moreover, we demonstrate how the effective dimension can be estimated from data and characterize its estimation accuracy. We apply our results to maximum likelihood estimation with generalized linear models, score matching with exponential families, and hypothesis testing with Rao's score test.

math.ST↗

arXiv2022

Stochastic Optimization for Spectral Risk Measures

Spectral risk objectives - also called $L$-risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task. We develop stochastic algorithms to optimize these quantities by characterizing their subdifferential and addressing challenges such as biasedness of subgradient estimates and non-smoothness of the objective. We show theoretically and experimentally that out-of-the-box approaches such as stochastic subgradient and dual averaging are hindered by bias and that our approach outperforms them.

stat.ML↗

arXiv2022

Orthogonal Statistical Learning with Self-Concordant Loss

Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal statistical learning methods with a loss function satisfying a self-concordance property. Our bounds improve upon existing bounds by a dimension factor while lifting the assumption of strong convexity. We illustrate the results with examples from multiple treatment effect estimation and generalized partially linear modeling.

stat.ML↗

arXiv2022

Entropy Regularized Optimal Transport Independence Criterion

We introduce an independence criterion based on entropy regularized optimal transport. Our criterion can be used to test for independence between two samples. We establish non-asymptotic bounds for our test statistic and study its statistical behavior under both the null hypothesis and the alternative hypothesis. The theoretical results involve tools from U-process theory and optimal transport theory. We also offer a random feature type approximation for large-scale problems, as well as a differentiable program implementation for deep learning applications. We present experimental results on existing benchmarks for independence testing, illustrating the interest of the proposed criterion to capture both linear and nonlinear dependencies in synthetic data and real data.

stat.ML↗

arXiv2022

Merger rate of charged black holes from the two-body dynamical capture

We consider the two-body dynamical capture of black holes carrying U(1) charge which can not only correspond to electric or magnetic charge but also have other physical interpretations such as dark or hidden charge. In the low-velocity and weak-field regime, we study gravitational and electromagnetic radiations from point masses with U(1) charges in a hyperbolic orbit, and we develop a formalism to derive the merger rate of charged black holes from the two-body dynamical capture. We apply the formalism to find the effects of the charge-to-mass ratio on the merger rate for possible different cases and discover that the effects depend on the models.

gr-qc↗

arXiv2021

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling. We establish non-asymptotic bounds on the sample complexity of divergence frontiers. We also introduce frontier integrals which provide summary statistics of divergence frontiers. We show how smoothed estimators such as Good-Turing or Krichevsky-Trofimov can overcome the missing mass problem and lead to faster rates of convergence. We illustrate the theoretical results with numerical examples from natural language processing and computer vision.

stat.ML↗

arXiv2021

Gravitational and electromagnetic radiation from binary black holes with electric and magnetic charges: Elliptical orbits on a cone

Extending the electromagnetic and gravitational radiations from binary black holes with electric and magnetic charges in circular orbits in Phys. Rev. D {\bf 102}, 103520 (2020), we calculate the total emission rates of energy and angular momentum due to gravitational and electromagnetic radiations from dyonic binary black holes in precessing elliptical orbits. It is shown that the emission rates of energy and angular momentum due to gravitational and electromagnetic radiations have the same dependence on the conic angle for different orbits. Moreover, we obtain the evolutions of orbits and find that a circular orbit remains circular while an elliptic orbit becomes quasi-circular due to electromagnetic and gravitational radiations. Using the evolution of orbits, we derive the waveform models for dyonic binary black hole inspirals and show the amplitudes of the gravitational waves for dyonic binary black hole inspirals differ from those for Schwarzschild binary black hole inspirals, which can be used to test electric and magnetic charges of black holes.

gr-qc↗

arXiv2021

Thermodynamics of pairing transition for Odd-A nuclei

The hot nucleus $^{171}$Yb is investigated by the covariant density functional theory with the PC-PK1 effective interaction. The thermodynamic quantities are evaluated with the canonical ensemble theory. The pairing correlations is treated by the shell-model-like approach, in which the particle numbers are conserved strictly and in which the blocking effect is handled exactly. An S-shaped heat capacity versus temperature of $^{171}$Yb appears. It has been studied in terms of the blocking effect, the single-particle levels, the pairing gap, and defined seniority components, and compared to the heat capacity of $^{172}$Yb. The pairing transition from the superfluid state to the normal state can result in the S-shaped heat capacity of $^{172}$Yb where the one-pair-broken and two-pair-broken states dominate, while the single-particle level structure near the Fermi surface is associated with the S-shaped heat capacity of $^{171}$Yb. For odd-A nuclei, although the one-pair-broken and two-pair-broken states still contribute, the pairing gap and the pairing transition is relatively weak. The S-shaped heat capacity could be affected due to the blocking of the single-particle level near the Fermi surface.

nucl-th↗

arXiv2021

Score-Based Change Detection for Gradient-Based Learning Machines

The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a continuous, possibly evolving, stream of data, it is desirable and often critical to supplement it with a companion change detection algorithm to facilitate its monitoring and control. We present a generic score-based change detection method that can detect a change in any number of components of a machine learning model trained via empirical risk minimization. This proposed statistical hypothesis test can be readily implemented for such models designed within a differentiable programming framework. We establish the consistency of the hypothesis test and show how to calibrate it to achieve a prescribed false alarm rate. We illustrate the versatility of the approach on synthetic and real data.

stat.ML↗

arXiv2020

Gravitational and electromagnetic radiation from binary black holes with electric and magnetic charges: Circular orbits on a cone

We derive the equations of motion of dyonic black hole binaries with electric and magnetic charges and explore features of static orbits. By using a Newtonian method with the inclusion of radiation reaction, we calculate the total emission rate of energy and angular momentum due to gravitational radiation and electromagnetic radiation for circular orbits. Moreover, we obtain the evolutions of orbits and calculate merger times of dyonic binaries. We show that electric and magnetic charges significantly suppress the merger times of dyonic binaries. Our results provide rich information about dyonic binaries and can be used to test black holes with magnetic charges.

gr-qc↗