arXiv subjects

Junhao Peng

Publications and source records attributed to Junhao Peng.

At least 19 recordsLinked to original sources

arXiv2026

Detection of Aliphatically Deuterated Aromatic Hydrocarbons in the Large Magellanic Cloud 30 Doradus Star-Forming Complex

The unidentified infrared (IR) emission (UIE) bands at 3.3, 6.2, 7.7, 8.6, 11.3 and 12.7 micron are ubiquitously seen in a wide variety of astrophysical environments. While the exact assignment of these UIE bands remains controversial, they are generally ascribed to C--H and C--C stretching and bending vibrations of aromatic hydrocarbon molecules. Here, based on observations made with the Near Infrared Spectrograph (NIRSpec) and the Mid Infrared Instrument (MIRI) aboard the James Webb Space Telescope (JWST), we report that the UIE emitters in the 30 Doradus star-forming complex in the Large Magellanic Cloud (LMC) are deuterated and have an appreciable amount of aliphatic content. The spatially resolved NIRSpec and MIRI spectra of 30 Doradus reveal a widespread detection of the 3.4 and 6.85 micron emission features attributed to aliphatic C--H stretch and deformation, respectively, as well as the 4.65 micron feature attributed to aliphatic C--D stretch. Notably, the 6.85 micron feature exhibits three complex substructures at ~6.83, 6.86 and 6.88 micron that have never been reported before.

astro-ph.GA↗

arXiv2026

Aromatics and Aliphatics in Local Star-Forming Galaxies as Probed by AKARI

Polycyclic aromatic hydrocarbon (PAH) molecules are abundant and widespread in galaxies and their infrared (IR) emission traces star formation. PAH molecules in astronomical environments often have aliphatic contents as revealed by the detection of the 3.4 micron aliphatic C--H stretch, a weak satellite feature accompanying the 3.3 micron aromatic C--H stretch. Here, we selected 102 local star-forming galaxies from the AKARI archive, including 66 galaxies each of which hosts an active galactic nucleus (AGN). We analyzed their AKARI near-IR spectra, which exhibit pronounced 3.3 micron aromatic and 3.4 micron aliphatic C--H emission. We also compiled their multi-wavelength photometric data and performed a decompositional analysis of their spectral energy distributions (SEDs) from the ultraviolet (UV) to the far-IR to derive the star formation rates (SFRs), stellar masses, metallicities, and luminosity of the galaxies. We explored the 3.3 micron PAH emission luminosity ($L_{3.3}$) as a calibrator of the SFR and found a close agreement with previous studies. We also found that $L_{3.3}/L_{\rm IR}$ and $L_{3.4}/L_{\rm IR}$ exhibit a strong dependence on metallicity, but remain nearly constant above 12+log(O/H)$\sim\,$8.5, where $L_{\rm IR}$ is the total luminosity emitted by dust, and $L_{3.4}$ is the luminosity of the 3.4 micron aliphatic emission. We derived from $L_{3.4}/L_{3.3}$ the PAH aliphatic fractions, defined as the fractions of carbon atoms in aliphatic units, to be in the range of $\sim\,$0.38%--6.8%, with a median fraction of $\sim\,$3.1%. The PAH aliphatic fractions are lower in AGN hosts and show a weak negative correlation with the SFR and $L_{\rm IR}$, suggesting that UV photons in regions with AGN or strong star formation activities may photodissociate the aliphatic structures associated with PAH molecules.

astro-ph.GA↗

arXiv2026

LenghuSky-8: An 8-Year All-Sky Cloud Dataset with Star-Aware Masks and Alt-Az Calibration for Segmentation and Nowcasting

Ground-based time-domain observatories require minute-by-minute, site-scale awareness of cloud cover, yet existing all-sky datasets are short, daylight-biased, or lack astrometric calibration. We present LenghuSky-8, an eight-year (2018-2025) all-sky imaging dataset from a premier astronomical site, comprising 429,620 $512 \times 512$ frames with 81.2% night-time coverage, star-aware cloud masks, background masks, and per-pixel altitude-azimuth (Alt-Az) calibration. For robust cloud segmentation across day, night, and lunar phases, we train a linear probe on DINOv3 local features and obtain 93.3% $\pm$ 1.1% overall accuracy on a balanced, manually labeled set of 1,111 images. Using stellar astrometry, we map each pixel to local alt-az coordinates and measure calibration uncertainties of approximately 0.37 deg at zenith and approximately 1.34 deg at 30 deg altitude, sufficient for integration with telescope schedulers. Beyond segmentation, we introduce a short-horizon nowcasting benchmark over per-pixel three-class logits (sky/cloud/contamination) with four baselines: persistence (copying the last frame), optical flow, ConvLSTM, and VideoGPT. ConvLSTM performs best but yields only limited gains over persistence, underscoring the difficulty of near-term cloud evolution. We release the dataset, calibrations, and an open-source toolkit for loading, evaluation, and scheduler-ready alt-az maps to boost research in segmentation, nowcasting, and autonomous observatory operations.

astro-ph.IM↗

arXiv2025

Interpretable descriptors enable prediction of hydrogen-based superconductors at moderate pressures

Room temperature superconductivity remains elusive, and hydrogen-base compounds despite remarkable transition temperatures(Tc) typically require extreme pressures that hinder application. To accelerate discovery under moderate pressures, an interpretable framework based on symbolic regression is developed to predict Tc in hydrogen-based superconductors. A key descriptor is an integrated density of states (IDOS) within 1 eV of the Fermi level (EF), which exhibits greater robustness than conventional single-point DOS features. The resulting analytic model links electronic-structure characteristics to superconducting performance, achieves high accuracy (RMSEtrain = 20.15 K), and generalizes well to external datasets. By relying solely on electronic structure calculations, the approach greatly accelerates materials screening. Guided by this model, four hydrogen-based candidates are identified and validated via calculation: Na2GaCuH6 with Tc =42.04 K at ambient pressure (exceeding MgB2), and NaCaH12, NaSrH12, and KSrH12 with Tc up to 162.35 K, 86.32 K, and 55.13 K at 100 GPa, 25 GPa, and 25 GPa, respectively. Beyond rapid screening, the interpretable form clarifies how hydrogen-projected electronic weight near EF and related features govern Tc in hydrides, offering a mechanism-aware route to stabilize high-Tc phases at reduced pressures.

cond-mat.supr-con↗

arXiv2025

Optimally Tensile Strained La3Ni2O7 Films as Candidate High-Temperature Superconductors on Designer Ba1-xSrxO (001) and SrO-SrTiO3 Substrates

Recent experiments have observed superconductivity up to 48 K in La3Ni2O7-derived films under compressive strain imposed by the SrLaAlO4 substrate, while such films on the SrTiO3 substrate with tensile strain have failed to reach the superconducting state. Here we propose to broadly expand the choices of materials platforms to achieve high-Tc superconducting La3Ni2O7 films by proposing designer substrates of Ba1-xSrxO (x = 0 - 1) that allow to continuously tune the strain in the films from being tensile to compressive. Our systematic study of the structural and electronic reconstructions of the strained La3Ni2O7 bilayer film leads to the central finding that at the optimal tensile strain of ~2% (x ~0.25), the spectral weight of the Ni dz2 orbital is peaked right at the Fermi level, and its hybridization with the Ni dx2-y2 orbital is substantially enhanced. Consequently, the expected Tc should be unprecedentedly high, at least substantially higher than those achieved in the compressive regime. Furthermore, our detailed thickness-dependent energetic analyses show that such films can be stably grown for thicknesses equal to or beyond the bilayer regime, and predict that the SrO-terminated SrTiO3 should also be able to stabilize the films with optimal tensile strain and higher Tc's.

cond-mat.supr-con↗

arXiv2025

Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients

Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate atomistic dynamic simulations. Trained by derivative-free optimization, the normal NEP achieves good accuracy, but suffers from inefficiency due to the high-dimensional parameter search. To overcome this problem, we present a gradient-optimized NEP (GNEP) training framework employing explicit analytical gradients and the Adam optimizer. This approach greatly improves training efficiency and convergence speedily while maintaining accuracy and physical interpretability. By applying GNEP to the training of Sb-Te material systems(datasets include crystalline, liquid, and disordered phases), the fitting time has been substantially reduced-often by orders of magnitude-compared to the NEP training framework. The fitted potentials are validated by DFT reference calculations, demonstrating satisfactory agreement in equation of state and radial distribution functions. These results confirm that GNEP retains high predictive accuracy and transferability while considerably improved computational efficiency, making it well-suited for large-scale molecular dynamics simulations.

cond-mat.dis-nn↗

arXiv2025

Transport Efficiency for Networks Obtained by Vertex Merging Operation

Transport is an important function of networks. Studying transport efficiency sheds light on the dynamic processes occurring within various underlying structures and offers a wide range of applications. To construct networks with different transport efficiencies, we focus on the networks obtained by vertex merging operation, which involves connecting multiple graphs through a single node. In this paper, we examine unbiased random walks on these networks and analyze their first-passage properties, including the mean first-passage time (MFPT), the mean trapping time (MTT), and the global-mean first-passage time (GFPT), which characterizes the transport (search) efficiency within the networks. We rigorously derive close-form solutions for these quantities. Results show that all these quantities are governed by the first-passage properties of the constituent components. Additionally, we propose a general method for optimizing the transport (search) efficiency by selecting a suitable node and adjusting the growth of the number of nodes in the subgraphs. We validate our findings using lollipop and barbell graphs. Our results indicate that for an arbitrary GFPT scaling exponent $\alpha \in [1, 3]$, we can construct a network with GFPT scales with the network size $N$ as $\text{GFPT} \sim N^{\alpha}$ through vertex merging operation. These conclusions provide valuable insights for designing and optimizing network structures.

nlin.CD↗

arXiv2025

Thermal-induced ion magnetic moment in H$_4$O superionic state

The hydrogen ions in the superionic ice can move freely, playing the role of electrons in metals. Its electromagnetic behavior is the key to explaining the anomalous magnetic fields of Uranus and Neptune. Based on the ab initio evolutionary algorithm, we searched for the stable H4O crystal structure under pressures of 500-5000 GPa and discovered a new layered chain $Pmn2_1$-H$_4$O structure with H$_3$ ion clusters. Interestingly, H3 ion clusters rotate above 900 K (with an instantaneous speed of 3000 m/s at 900 K), generating an instantaneous magnetic moment ($10^{-26}$ Am$^2 \approx 0.001 \mu_B$). Moreover, H ions diffuse in a direction perpendicular to the H-O atomic layer at 960-1000 K. This is because the hydrogen oxygen covalent bonds within the hydrogen oxygen plane hinder the diffusion behavior of H$_3$ ion clusters within the plane, resulting in the diffusion of H$_3$ ion clusters between the hydrogen oxygen planes and the formation of a one-dimensional conductive superionic state. One-dimensional diffusion of ions may generate magnetic fields. We refer to these two types of magnetic moments as "thermal-induced ion magnetic moments". When the temperature exceeds 1000 K, H ions diffuse in three directions. When the temperature exceeds 6900 K, oxygen atoms diffuse and the system becomes fluid. These findings provide important references for people to re-recognize the physical and chemical properties of hydrogen and oxygen under high pressure, as well as the sources of abnormal magnetic fields in Uranus and Neptune.

cond-mat.mtrl-sci↗

arXiv2022

The normalized Laplacian spectrum of $n$-polygon graphs and its applications

Given an arbitrary connected $G$, the $n$-polygon graph $\tau_n(G)$ is obtained by adding a path with length $n$ $(n\geq 2)$ to each edge of graph $G$, and the iterated $n$-polygon graphs $\tau_n^g(G)$ ($g\geq 0$), is obtained from the iteration $\tau_n^g(G)=\tau_n(\tau_n^{g-1}(G))$, with initial condition $\tau_n^0(G)=G$. In this paper, a method for calculating the eigenvalues of normalized Laplacian matrix for graph $\tau_n(G)$ is presented if the eigenvalues of normalized Laplacian matrix for graph $G$ is given firstly. Then, the normalized Laplacian spectrums for the graph $\tau_n(G)$ and the graphs $\tau_n^g(G)$ ($g\geq 0$) can also be derived. Finally, as applications, we calculate the multiplicative degree-Kirchhoff index, Kemeny's constant and the number of spanning trees for the graph $\tau_n(G)$ and the graphs $\tau_n^g(G)$ by exploring their connections with the normalized Laplacian spectrum, exact results for these quantities are obtained.

math.CO↗

arXiv2022

Stability of xenon-sodium compounds at moderately low pressures

A growing body of theoretical and experimental evidence suggests that inert gases (He, Ne, Ar, Kr, Xe, Rn) become less and less inert under increasing pressure. Here we use the ab initio evolutionary algorithm to predict stable compounds of Xe and Na at pressures below 100 GPa, and find three stable compounds, NaXe, NaXe$_3$ and NaXe$_4$. The NaXe belongs to a well-known cubic CsCl structure type. The NaXe$_4$'s structure is common in amphiboles, whereas the NaXe$_3$ has a unique structure, analogous to the "post-perovskite" orthorhombic CaIrO$_3$-type structure with Ir atoms removed. This is the first time that a cation-vacant version of the CaIrO$_3$ is found in any compound. NaXe, NaXe$_3$ and NaXe$_4$ are found to be metallic.

cond-mat.mtrl-sci↗

arXiv2020

First encounters on Bethe Lattices and Cayley Trees

In this work we consider the first encounter problems between a fixed and/or mobile target A and a moving trap B on Bethe Lattices and Cayley trees. The survival probability (SP) of the target A on the both kinds of structures are analyzed analytically and compared. On Bethe Lattices, the results show that the fixed target will still prolong its survival time, whereas, on Cayley trees, there are some initial positions where the target should move to prolong its survival time. The mean first encounter time (MFET) for mobile target A is evaluated numerically and compared with the mean first passage time (MFPT) for the fixed target A. Different initial settings are addressed and clear boundaries are obtained. These findings are helpful for optimizing the strategy to prolong the survival time of the target or to speed up the search process on Cayley trees, in relation to the target's movement and the initial position configuration of the two walkers. We also present a new method, which uses a small amount of memory, for simulating random walks on Cayley trees.

cond-mat.stat-mech↗

arXiv2020

Optimal networks measured by global mean first return time

Random walks have wide application in real lives, ranging from target search, reaction kinetics, polymer chains, to the forecast of the arrive time of extreme events, diseases or opinions. In this paper, we consider discrete random walks on general connected networks and focus on the analysis of the global mean first return time (GMFRT), which is defined as the mean first return time averaged over all the possible starting positions (vertices), aiming at finding the structures who have the maximal (or the minimal) GMFRT among all connected graphs with the same number of vertices and edges. Our results show that, among all trees with the same number of vertices, trees with linear structure are the structures with the minimal GMFRT and stars are the structures with the maximal GMFRT. We also find that, among all connected graphs with the same number of vertices, the graphs whose vertices have the same degree, are the structures with the minimal GMFRT; and the graphs whose vertex degrees have the biggest difference, are the structures with the maximal GMFRT. We also present the methods for constructing the graphs with the maximal GMFRT (or the minimal GMFRT), among all connected graphs with the same number of vertices and edges.

math.PR↗

arXiv2019

Exact calculations of first-passage properties on the pseudofractal scale-free web

In this paper, we consider discrete time random walks on the pseudofractal scale-free web (PSFW) and we study analytically the related first passage properties. First, we classify the nodes of the PSFW into different levels and propose a method to derive the generation function of the first passage probability from an arbitrary starting node to the absorbing domain, which is located at one or more nodes of low-level (i.e., nodes with large degree). Then, we calculate exactly the first passage probability, the survival probability, the mean and the variance of first passage time by using the generating functions as a tool. Finally, for some illustrative examples corresponding to given choices of starting node and absorbing domain, we derive exact and explicit results for such first passage properties. The method we propose can as well address the cases where the absorbing domain is located at one or more nodes of high-level on the PSFW, and it can also be used to calculate the first passage properties on other networks with self-similar structure, such as $(u, v)$ flowers and recursive scale-free trees.

cond-mat.stat-mech↗

arXiv2019

Scaling laws for diffusion on (trans)fractal scale-free networks

Fractal (or transfractal) features are common in real-life networks and are known to influence the dynamic processes taking place in the network itself. Here we consider a class of scale-free deterministic networks, called $(u,v)$-flowers, whose topological properties can be controlled by tuning the parameters $u$ and $v$; in particular, for $u>1$, they are fractals endowed with a fractal dimension $d_f$, while for $u=1$, they are transfractal endowed with a transfractal dimension $\tilde{d}_f$. In this work we investigate dynamic processes (i.e., random walks) and topological properties (i.e., the Laplacian spectrum) and we show that, under proper conditions, the same scalings (ruled by the related dimensions), emerge for both fractal and transfractal.

cond-mat.stat-mech↗

arXiv2019

Exact results for the first-passage properties in a class of fractal networks

In this work we consider a class of recursively-grown fractal networks $G_n(t)$, whose topology is controlled by two integer parameters $t$ and $n$. We first analyse the structural properties of $G_n(t)$ (including fractal dimension, modularity and clustering coefficient) and then we move to its transport properties. The latter are studied in terms of first-passage quantities (including the mean trapping time, the global mean first-passage time and the Kemeny's constant) and we highlight that their asymptotic behavior is controlled by network's size and diameter. Remarkably, if we tune $n$ (or, analogously, $t$) while keeping the network size fixed, as $n$ increases ($t$ decreases) the network gets more and more clustered and modular, while its diameter is reduced, implying, ultimately, a better transport performance. The connection between this class of networks and models for polymer architectures is also discussed.

cond-mat.stat-mech↗

arXiv2018

Analysis of fluctuations in the first return times of random walks on regular branched networks

The first return time (FRT) is the time it takes a random walker to first return to its original site, and the global first passage time (GFPT) is the first passage time for a random walker to move from a randomly selected site to a given site. We find that in finite networks the variance of FRT, Var(FRT), can be expressed Var(FRT)~$=2\langle$FRT$ \rangle \langle $GFPT$ \rangle -\langle $FRT$ \rangle^2-\langle $FRT$ \rangle$, where $\langle \cdot \rangle$ is the mean of the random variable. Therefore a method of calculating the variance of FRT on general finite networks is presented. We then calculate Var(FRT) and analyze the fluctuation of FRT on regular branched networks (i.e., Cayley tree) by using Var(FRT) and its variant as the metric. We find that the results differ from those in such other networks as Sierpinski gaskets, Vicsek fractals, T-graphs, pseudofractal scale-free webs, ($u,v$) flowers, and fractal and non-fractal scale-free trees.

cond-mat.stat-mech↗

arXiv2016

Scaling properties of first-passage quantities on the fractal and transfractal scale free networks

In this paper, we consider the random walk process on a kind of fractal (or transfractal) scale free networks, which also called as $(u,v)$ flowers, and we focus on the global first passage time (GFPT) and first return time (FRT). Here, we present method to derive exactly the probability generation function, mean and variance of the GFPT and FRT for a given hub (i.e., node with the highest degree) and then the scaling properties of the mean and the variance of the GFPT and FRT are disclosed. Our results show that, for the case of $u>1$, while the networks are fractals, the mean of the GFPT scales with the volume of the network as $\langle GMFPT_t\rangle \sim N_t^{{2}/{d_s}}$, where $\langle * \rangle$ denotes the mean of random variable $*$, $N_t$ is the volume of the network with generation $t$ and $d_s$ is the spectral dimension of the network; but, for the case of $u=1$, while the networks are nonfractals, the mean of the GFPT scales as $\langle GMFPT_t\rangle \sim N_t^{{2}/{\tilde{d}_s}}$, where $\tilde{d}_s$ is the transspectral dimension of the network, which is introduced in this paper. Results also show that, the variance of the GFPT scales as $Var(GFPT_t) \sim \langle GFPT_{t}\rangle^2$, where $Var(*)$ denotes variance of of random variable $*$; whereas the variance of the FRT scales as $Var(FRT_t) \sim \langle FRT_{t} \rangle \langle GFPT_{t}\rangle$. %$Var(GFPT_t) \sim \langle GFPT_{t}\rangle^2$ and $Var(FRT_t) \sim \langle FRT_{t} \rangle \langle GFPT_{t}\rangle$, where $Var(GFPT_t)$ and $Var(FRT_t)$ denote the variance of the GFPT and the FRT, $\langle GFPT_{t}\rangle$ and $\langle FRT_{t}\rangle$ denote the mean of the GFPT and the FRT respectively.

cond-mat.stat-mech↗

arXiv2016

Mean trapping time for an arbitrary node on regular hyperbranched polymers

The regular hyperbranched polymers (RHPs), also known as Vicsek fractals, are an important family of hyperbranched structures which have attracted a wide spread attention during the past several years. In this paper, we study the first-passage properties for random walks on the RHPs. Firstly, we propose a way to label all the different nodes of the RHPs and derive exact formulas to calculate the mean first-passage time (MFPT) between any two nodes and the mean trapping time (MTT) for any trap node. Then, we compare the trapping efficiency between any two nodes of the RHPs by using the MTT as the measures of trapping efficiency. We find that the central node of the RHPs is the best trapping site and the nodes which are the farthest nodes from the central node are the worst trapping sites. Furthermore, we find that the maximum of the MTT is about $4$ times more than the minimum of the MTT. The result is similar to the results in the recursive fractal scale-free trees and T-fractal, but it is quite different from that in the recursive non-fractal scale-free trees. These results can help understanding the influences of the topological properties and trap location on the trapping efficiency.

cond-mat.stat-mech↗