arXiv subjects

Jun Pan

Publications and source records attributed to Jun Pan.

At least 19 recordsLinked to original sources

arXiv2026

Approximating the peculiar velocity distribution of dark matter halos with Tsallis statistics

Dark matter halos, which host galaxies and galaxy clusters, have peculiar velocities far from thermal equilibrium. Characterizing the nonlinear and non-Gaussian features of this velocity distribution improves our understanding of the gravitational evolution of the cosmic web and supports related cosmological applications. We endeavor to establish a connection between the peculiar velocity distribution of halos and nonequilibrium statistical mechanics, with the objective of obtaining a model that is both concise and accurate for practical applications. We extracted halo samples from large N-body simulations and performed maximum-likelihood fits to the peculiar velocity distributions using a two-parameter Tsallis model, derived from non-extensive statistical mechanics. On the theoretical side, we reformulated the halo distribution in the superstatistics framework by means of a generalized Gram-Charlier expansion based on the gamma distribution. For halo peculiar velocities below 1000 km/s, the Tsallis model achieves 5 percent accuracy over z=0-2, with performance improving toward lower redshifts. Our results show that the halo velocity distribution becomes increasingly non-Gaussian and departs further from equilibrium over time. The best-fit parameters depend only weakly on mass, though low-mass halos exhibit slightly stronger non-Gaussianity. The two parameters, especially the velocity dispersion, offer promising probes of cosmological parameters. Theoretically, we find that in general the halo peculiar velocity distribution function is expressible as a superposition of a series of Tsallis distribution functions, while simulation results demonstrate that the zeroth-order approximation, namely a single Tsallis function, already achieves sufficient accuracy.

astro-ph.CO↗

arXiv2026

Halo Mass of ULIRGs at Cosmic Noon

We present a clustering analysis of $\sim 3000$ ultraluminous infrared galaxies (ULIRGs) at $z\sim 2$, uniformly selected by $24\mu {\rm m}$ flux and IRAC colors in the COSMOS and BOOTES fields. We measure the angular correlation functions of ULIRGs in both fields and fit them with galaxy clustering models. Linear theory modeling shows that these ULIRGs reside in dark matter halos with characteristic masses of $\log M_{\rm h} /({h^{-1}\rm M_{\odot}}) = 12.64\pm 0.50$ in COSMOS and $12.72\pm 0.13$ in BOOTES. The halo occupation distribution (HOD) modeling yields occupation-weighted effective halo masses of $\log M_{\rm eff} /({h^{-1}\rm M_{\odot}}) =12.50^{+0.26}_{-0.28}$ for COSMOS and $12.89^{+0.11}_{-0.12}$ for BOOTES. These host halos are expected to evolve into halos with masses of $\sim 10^{13.6-13.9}\ {h^{-1}\rm M_{\odot}}$ at $z=0$. The HOD fits allow for a non-negligible satellite contribution to the clustering of galaxies in the BOOTES field, but the satellite fraction derived in the COSMOS field appears nearly zero.

astro-ph.CO↗

arXiv2026

Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset

Optical remote sensing imagery is frequently degraded by cloud and cloud-shadow contamination, which limits its reliability for near-real-time land use and land cover (LULC) mapping. Although synthetic aperture radar (SAR) can provide cloud-penetrating structural information, existing SAR-optical fusion methods often assume reliable optical observations and insufficiently address the semantic uncertainty introduced by cloud contamination. To address this issue, we propose CloudLULC-Net, an end-to-end heterogeneous SAR-optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 imagery and temporally adjacent Sentinel-1 SAR observations. The proposed network incorporates optical reliability modulation to suppress unreliable optical responses, heterogeneous information adaptive aggregation to model high-order spatial-channel interactions between optical and SAR representations, and a unified semantic mapping transformer to organize fused features in a LULC-oriented latent space. A semantic anchor-guided optimization strategy is further introduced to improve the consistency of intermediate semantic representations. To support this task, we construct CloudLULC-Set, a large-scale benchmark dataset containing 40,223 curated SAR-optical-label triplets with pixel-level LULC annotations across diverse geographic regions and cloud conditions. Experimental results show that CloudLULC-Net achieves an OA of 86.60%, an F1-score of 83.29%, and an mIoU of 73.51%, outperforming representative heterogeneous reconstruction-first and end-to-end SAR-optical mapping methods. Comparisons with existing global LULC products and analyses under different cloud-cover levels further demonstrate the robustness and practical value of CloudLULC-Net for target-date LULC mapping in cloud-prone regions.The project is publicly available at: https://github.com/RSIIPAC/CloudLULC

cs.CV↗

arXiv2026

The FAST Hundred-Deg$^2$ HI Deep (HD$^2$) Survey: Early Results from the Pilot Survey

The Hundred-deg$^2$ HI Deep (HD$^2$) survey carried out with the Five-hundred-meter Aperture Spherical Telescope (FAST) is planned to map a contiguous region within the DESI DR1 footprint, achieving an effective integration time of 20 minutes for each pointing and a uniform detection sensitivity of 0.28 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We present early results from the pilot HD$^2$ survey: a 10 deg$^2$ field overlapping with HSC-SSP and the DESI EDR SV3, observed with an integration time of 7.3 minutes per beam and the rms of 0.45 mJy beam$^{-1}$ at 4.8 km s$^{-1}$ resolution. We identify 339 HI sources at $z<0.09$, corresponding to $\sim$34 detections per deg$^2$, nearly six times higher than the detection rate of the wide-field surveys. Optical counterparts are primarily identified using DESI redshifts, yielding a matching rate and correctness exceeding 90% for galaxies with $r<19.5$ mag, a substantial improvement over SDSS. Under the constraint of $r < 17.8$ mag and $0.01 < z < 0.05$, nearly 50% of galaxies in the DESI BGS samples have HI detections in this pilot survey. The optical properties of these HI-detected galaxies span nearly the entire parameter range of the DESI sample. The gas fraction scaling relations versus stellar mass, stellar mass surface density, NUV-r, and specific star formation rate are consistent with previous surveys, e.g., ALFALFA, DINGO, and xGASS. These results justify the feasibility of the full HD$^2$ survey, which will build a high-completeness HI census over a contiguous area to probe the cold gas scaling relations of galaxies over different scales.

astro-ph.GA↗

arXiv2026

DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos

Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially under unstabilized conditions where platform jitter and the weak appearance of tiny objects jointly degrade tracking performance. To address this problem, we propose DeTracker, a joint-detection-and-tracking framework tailored for unstabilized satellite videos. DeTracker introduces a task-driven Global-Local Motion Decoupling (GLMD) module to address the motion imbalance between dominant platform motion and weak target motion. It suppresses background-dominated motion via global semantic alignment at the feature level and captures target-specific motion through local refinement, improving trajectory stability and identity consistency. In addition, a Temporal Dependency Feature Pyramid (TDFP) module is developed to perform cross-frame temporal feature fusion, enhancing the continuity and discriminability of tiny-object representations. We further construct a new benchmark dataset, SDM-Car-SU, which simulates multi-directional and multi-speed platform motions to enable systematic evaluation of tracking robustness under varying motion perturbations. Extensive experiments on both simulated and real unstabilized satellite videos demonstrate that DeTracker significantly outperforms existing methods, achieving 61.1% MOTA on SDM-Car-SU and 45.3% MOTA on real satellite video data. The code and dataset will be publicly available at https://github.com/alex-chenjiajun/DeTracker.

cs.CV↗

arXiv2024

Pair Counting without Binning -- A New Approach to Correlation Functions in Clustering Statistics

This paper presents a novel perspective on correlation functions in the clustering analysis of the large-scale structure of the universe. We first recognise that pair counting in bins of radial separation is equivalent to evaluating counts-in-cells (CIC), which can be modelled using a filtered density field with a binning-window function. This insight leads to an in situ expression for the two-point correlation function (2PCF). Essentially, the core idea underlying our method is to introduce a window function to define the binning scheme, enabling pair-counting without binning. This approach develops a concept of generalised 2PCF, which extends beyond conventional discrete pair counting by accommodating non-sharp-edged window functions. To extend this framework to N-point correlation functions (NPCF) using current optimal edge-corrected estimators, we developed a binning scheme independent of the specific parameterisation of polyhedral configurations. In particular, we demonstrate a fast algorithm for the three-point correlation function (3PCF), where triplet counting is accomplished by assigning either a spherical tophat or a Gaussian filter to each vertex of triangles. Additionally, we derive analytical expressions for the 3PCF using a multipole expansion in Legendre polynomials, accounting for filtered field (binning) corrections. Numerical tests using several suites of N-body simulation samples show that our approach aligns remarkably well with the theoretical predictions. Our method provides an exact solution for quantifying binning effects in practical measurements and offers a high-speed algorithm, enabling high-order clustering analysis in extremely large datasets from ongoing and upcoming surveys such as Euclid, LSST, and DESI.

astro-ph.CO↗

arXiv2024

HiFAST: an HI data calibration and imaging pipeline for FAST

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has the largest aperture and a 19-beam L-band receiver, making it powerful for investigating the neutral hydrogen atomic gas (HI) in the universe. We present HiFAST (https://hifast.readthedocs.io), a dedicated, modular, and self-contained calibration and imaging pipeline for processing the HI data of FAST. The pipeline consists of frequency-dependent noise diode calibration, baseline fitting, standing wave removal using an FFT-based method, flux density calibration, stray radiation correction, and gridding to produce data cubes. These modules can be combined as needed to process the data from most FAST observation modes: tracking, drift scanning, On-The-Fly mapping, and most of their variants. With HiFAST, the RMS noises of the calibrated spectra from all 19 beams were only slightly (~ 5%) higher than the theoretical expectation. The results for the extended source M33 and the point sources are consistent with the results from Arecibo. The moment maps (0,1 and 2) of M33 agree well with the results from the Arecibo Galaxy Environment Survey (AGES) with a fractional difference of less than 10%. For a common sample of 221 sources with signal-to-noise ratio S/N >10 from the Arecibo Legacy Fast ALFA (ALFALFA) survey, the mean value of fractional difference in the integrated flux density, $S_{\mathrm{int}}$, between the two datasets is approximately 0.005 %, with a dispersion of 15.4%. Further checks on the integrated flux density of 23 sources with seven observations indicate that the variance in the flux density of the source with luminous objects ($S_\mathrm{int}$ $ > 2.5$ Jy km s$^{-1}$) is less than 5%. Our tests suggest that the FAST telescope, with the efficient, precise, and user-friendly pipeline HiFAST, will yield numerous significant scientific findings in the investigation of the HI in the universe.

astro-ph.GA↗

arXiv2023

Performance of FAST with an Ultra-Wide Bandwidth Receiver at 500-3300 MHz

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has been running for several years. A new Ultra-Wide Bandwidth (UWB) receiver, simultaneously covering 500-3300 MHz, has been mounted in the FAST feed cabin and passed a series of observational tests. The whole UWB band is separated into four independent bands. Each band has 1048576 channels in total, resulted in a spectral resolution of 1 kHz. At 500-3300 MHz, the antenna gain is around 14.3-7.7 K/Jy, the aperture efficiency is around 0.56-0.30, the system temperature is around 88-130 K, and the HPBW is around 7.6-1.6 arcmin. The measured standard deviation of pointing accuracy is better than ~7.9 arcsec, when zenith angle (ZA) is within 26.4deg. The sensitivity and stability of the UWB receiver are confirmed to satisfy expectation by spectral observations, e.g., HI and OH. The FAST UWB receiver already has a good performance for taking sensitive observations in various scientific goals.

astro-ph.IM↗

arXiv2021

About One-point Statistics of the Ratio of Two Fourier-transformed Cosmic Fields and an Application

The Fourier transformation is an effective and efficient operation of Gaussianization at the one-point level. Using a set of N-body simulation data, we verified that the one-point distribution functions of the dark matter momentum divergence and density fields closely follow complex Gaussian distributions. The one-point distribution function of the quotient of two complex Gaussian variables is introduced and studied. Statistical theories are then applied to model one-point statistics about the growth of individual Fourier mode of the dark matter density field, which can be obtained by the ratio of two Fourier transformed cosmic fields. Our simulation results proved that the models based on the Gaussian approximation are impressively accurate, and our analysis revealed many interesting aspects about the growth of dark matter's density fluctuation in Fourier space.

astro-ph.CO↗

arXiv2021

Numerical investigation of non-Gaussianities in the phase and modulus of density Fourier modes

We numerically investigate non-Gaussianities in the late-time cosmological density field in Fourier space. We explore various statistics, including the two-point and three-point probability distribution function (PDF) of phase and modulus, and two \& three-point correlation function of of phase and modulus. We detect significant non-Gaussianity for certain configurations. We compare the simulation results with the theoretical expansion series of \citet{2007ApJS..170....1M}. We find that the $\mathcal{O}(V^{-1/2})$ order term alone is sufficiently accurate to describe all the measured non-Gaussianities in not only the PDFs, but also the correlations. We also numerically find that the phase-modulus cross-correlation contributes $\sim 50\%$ to the bispectrum, further verifying the accuracy of the $\mathcal{O}(V^{-1/2})$ order prediction. This work demonstrates that non-Gaussianity of the cosmic density field is simpler in Fourier space, and may facilitate the data analysis in the era of precision cosmology.

astro-ph.CO↗

arXiv2020

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization

In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow are not only sparse but also smooth along the horizontal and vertical direction in images while the clean images are smooth along the temporal direction between images. Therefore, the sparsity norm is used to boost the sparsity of the cloud and cloud shadow, and unidirectional total variation (UTV) regularizers are applied to ensure the unidirectional smoothness. This paper utilizes alternation direction method of multipliers to solve the presented model and generate the cloud and cloud shadow element as well as the clean element. The cloud and cloud shadow element is purified to get the cloud area and cloud shadow area. Then, the clean area of the original cloud-contaminated images is replaced to the corresponding area of the clean element. Finally, the reference image is selected to reconstruct details of the cloud area and cloud shadow area using the information cloning method. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints.

eess.IV↗

arXiv2020

Estimating power spectrum of discrete cosmic momentum field with fast Fourier transform

Fast Fourier transform based estimators are formulated for measuring momentum power spectra, including the auto power spectra of the momentum, the momentum divergence, and the cross spectrum of density fluctuation and momentum divergence. Algorithms using the third order Bettle-Lemari\'{e} scaling function to assign discrete objects to regular grids for fast Fourier transform are proposed to clean alias effects. Numerical experiments prove that the implementation can achieve sub-percent precision till close to the Nyquist frequency. Impact of removing bulk flow on estimation of momentum power spectra are derived theoretically and verified numerically, subtracting bulk flow has little effects at large scales but might induce meaningful differences in nonlinear regime, and probably it is not necessary to subtract bulk flow for samples which peculiar velocities are exact or sufficiently accurate. Momentum power spectra of dark matter samples from N-body simulation are measured and discussed. As expected, prediction of the one loop Eulerian perturbation theory agrees with simulation only slightly better than the linear theory at z=0, but can be applied to higher redshift with improved accuracy. Measurements of simulation data and the one loop Eulerian theory both reveal that the momentum field contains strong rotational part, and there is a large stochastic component in the divergence of momentum which is not correlated with the density field. The three kinds of momentum power spectra have their own characteristics.

astro-ph.CO↗

arXiv2020

Simulating Kilonovae in the {\Lambda}CDM Universe

Kilonovae are optical flashes produced in the aftermath of neutron star-neutron star mergers (NNMs) or neutron star-black hole mergers (NBMs). In this work, we use the Millennium Simulation, combined with a semi-analytic galaxy formation model--GABE (Galaxy Assembly with Binary Evolution) which adopts binary stellar population synthesis models, to explore the cosmic event rate of kilonovae, and the properties of their host galaxies in a cosmological context. We find that model with supernova kick velocity of 0 km/s fits the observation best, in agreement with the exception of some formation channels of binary neutron star. This indicates that NNMs prefer to originate from binary systems with low kick velocities. With V$_{\rm kick}$=0 km/s, the cosmic event rate of NNMs and NBMs at z=0 are 283 Gpc$^{-3}$yr$^{-1}$ and 91 Gpc$^{-3}$yr$^{-1}$, respectively, marginally consistent with the constraint from LVC GWTC-1. For Milky Way-mass galaxies, we predict the NNM rate is $25.7^{+59.6}_{-7.1}$ Myr$^{-1}$, which is also in good agreement with the observed properties of binary neutron stars in the Milky Way. Taking all the NNMs into account in the history of Milky Way-mass galaxies, we find that the averaged r-process elements yield with A>79 in a NNM and NBM event should be 0.01 M$_{\odot}$ to be consistent with observation. We conclude that NGC 4993, the host galaxy of GW170817, is a typical host galaxy for NNMs. However, generally NNMs and NBMs tend to reside in young, blue, star-forming, late-type galaxies, with stellar mass and gaseous metallicity distribution peaking at $10^{10.65}$ M$_{\odot}$ and 8.72-8.85, respectively. By studying kilonovae host galaxies in the cosmological background, it is promising to constrain model details better when we have more events in the forthcoming future. (abridged)

astro-ph.GA↗

arXiv2019

Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks

Recent studies have shown convolution neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel-level perturbations into the whole or specific part of images. In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR), which adds predetermined perturbations to specific regions of license plate images, simulating some sort of naturally formed spots (such as sludge, etc.). Therefore, the problem is modeled as an optimization process searching for optimal perturbation positions, which is different from previous work that consider pixel values as decision variables. Notice that this is a complex nonlinear optimization problem, and we use a genetic-algorithm based approach to obtain optimal perturbation positions. In experiments, we use the proposed algorithm to generate various adversarial examples in the form of rectangle, circle, ellipse and spots cluster. Experimental results show that these adversarial examples are almost ignored by human eyes, but can fool HyperLPR with high attack success rate over 93%. Therefore, we believe that this kind of spot evasion attacks would pose a great threat to current LPR systems, and needs to be investigated further by the security community.

cs.CV↗

arXiv2019

GABE: Galaxy Assembly with Binary Evolution

We developed a new semi-analytic galaxy formation model: Galaxy Assembly with Binary Evolution (GABE). For the first time we introduce binary evolution into semi-analytic models of galaxy formation by using Yunnan-II stellar population synthesis model, which includes various binary interactions. When implementing our galaxy formation model onto the merger trees extracted from the Millennium simulation, it can reproduce a large body of observational results. We find that in the local universe the model including binary evolution reduces the luminosity at optical and infrared wavelengths slightly, while increases the luminosity at ultraviolet wavelength significantly, especially in $F_{\rm UV}$ band. The resulting luminosity function does not change very much over SDSS optical bands and infrared band, but the predicted colors are bluer, especially when $F_{\rm UV}$ band is under consideration. The new model allows us to explore the physics of various high energy events related to the remnants of binary stars, e.g. type Ia supernovae, short gamma-ray bursts and gravitational wave events, and their relation with host galaxies in a cosmological context.

astro-ph.GA↗

arXiv2018

Comparing galaxy morphology in hydrodynamical simulation and in semi-analytic model

We compare galaxy morphology predicted by the Illustris hydrodynamical simulation and a semi-analytic model (SAM) grafted in the halo merger trees from the Illustris-Dark matter simulation. Morphology is classified according to the luminous profile and the kinematic bulge-to-total ratio for Illustris galaxy, and the bulge-to-total stellar mass ratio for SAM galaxy. For late-type galaxies in the Illustris catalogue, most of their counterparts in the SAM model have the same type, and the consistency between two models is higher for lower mass galaxies. For early-type (ET) galaxies in Illustris, the consistency is quite low for the counterparts except for most massive galaxies. By comparing in detail the growth histories of some matched galaxy pairs of Milky Way mass in Illustris and the SAM model, we notice two aspects of differences in determining galaxy morphology between the two galaxy formation implementations. First, in the SAM, major merger and frequent minor mergers result in the growth of bulges and turn the galaxy into ET, while bulge formation is not connected to mergers as tightly as in SAM for the Illustris galaxies. In addition, the satellite stellar mass can decrease significantly due to tidal stripping before merging into the central galaxy in Illustris, while it does not decrease in the SAM model. This results in less mergers with large (stellar) mass ratios in the Illustris simulation, and less effect of mergers on shaping galaxy morphology.

astro-ph.GA↗

arXiv2018

The Large-scale Effect of Environment on Galactic Conformity

We use a volume-limited galaxy sample from the SDSS Data Release 7 to explore the dependence of galactic conformity on the large-scale environment, measured on $\sim$ 4 Mpc scales. We find that the star formation activity of neighbour galaxies depends more strongly on the environment than on the activity of their primary galaxies. In under-dense regions most neighbour galaxies tend to be active, while in over-dense regions neighbour galaxies are mostly passive, regardless of the activity of their primary galaxies. At a given stellar mass, passive primary galaxies reside in higher density regions than active primary galaxies, leading to the apparently strong conformity signal. The dependence of the activity of neighbour galaxies on environment can be explained by the corresponding dependence of the fraction of satellite galaxies. Similar results are found for galaxies in a semi-analytical model, suggesting that no new physics is required to explain the observed large-scale conformity.

astro-ph.GA↗

arXiv2017

SDSS-IV MaNGA: A Distinct Mass Distribution Explored in Slow-Rotating Early-type Galaxies

We study the radial acceleration relation (RAR) for early-type galaxies (ETGs) in the SDSS MaNGA MPL5 dataset. The complete ETG sample show a slightly offset RAR from the relation reported by McGaugh et al. (2016) at the low-acceleration end; we find that the deviation is due to the fact that the slow rotators show a systematically higher acceleration relation than the McGaugh's RAR, while the fast rotators show a consistent acceleration relation to McGaugh's RAR. There is a 1\sigma significant difference between the acceleration relations of the fast and slow rotators, suggesting that the acceleration relation correlates with the galactic spins, and that the slow rotators may have a different mass distribution compared with fast rotators and late-type galaxies. We suspect that the acceleration relation deviation of slow rotators may be attributed to more galaxy merger events, which would disrupt the original spins and correlated distributions of baryons and dark matter orbits in galaxies.

astro-ph.GA↗