arXiv subjects

Yongxing Zhang

Publications and source records attributed to Yongxing Zhang.

6 recordsLinked to original sources

arXiv2026

PMO Polaris CO survey. II. Where is the dust?

Dust plays critical chemical and dynamical roles in the interstellar medium (ISM), but its specific association with molecular and atomic gas remains difficult to isolate. Combining the PMO Polaris CO Survey (PPCOS), EBHIS \ion{H}{I} data, and \textit{Planck} dust maps, this study investigates dust distributions across multiple gas components in the Polaris Flare. We employ multi-technique linear decomposition -- including full-spectrum fitting and a regularization approach -- to reconstruct the dust distribution from multi-component gas emissions. This framework quantifies dust contributions from CO-associated, \ion{H}{I}-associated, and CO-dark molecular gas phases. CO-associated dust accounts for 20--40\% of the total dust mass, whereas dust in the broad \ion{H}{I} (warm neutral medium, WNM) component is negligible. Instead, \ion{H}{I}-associated dust concentrates primarily within the narrow cold neutral medium (CNM) and a distinct, ultra-narrow component with a velocity width comparable to the \ion{H}{I} spectral resolution. Residual dust at atomic-to-molecular (\ion{H}{I}--CO) interfaces contributes 4--10\% to the global dust mass, but exceeds 25\% at molecular cloud boundaries, confirming a substantial presence of CO-dark molecular gas. Furthermore, the velocity fields of dust-associated \ion{H}{I} closely match those of CO, indicating active dynamical coupling between CO-emitting gas and the surrounding CNM. Guided by these results, we present a stepwise schematic cartoon illustrating the coupling between multi-phase gas structures, molecular formation, and dust growth.

astro-ph.GA↗

arXiv2026

PMO Polaris CO survey. I. A 100 deg$^2$ view of the Polaris Flare

Large-area CO surveys are essential for studying molecular cloud dynamics and evolution; however, most have focused on the Galactic plane, leaving high-latitude clouds less explored. We present the PMO Polaris CO Survey (PPCOS), which maps a 100~deg$^2$ region of the Polaris Flare in the $J=1-0$ transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O using the Delingha 13.7~m telescope. As the first large-area CO survey at high Galactic latitude ($|b| > 20^{\circ}$) with sub-arcminute resolution, PPCOS achieves sensitivities of $\sim$0.46~K for $^{12}$CO and $\sim$0.23~K for $^{13}$CO and C$^{18}$O at a spectral resolution of 0.16~km~s$^{-1}$ and an angular resolution of 50\arcsec. The $^{12}$CO emission reveals seven distinct complexes, where only $\sim$10\% of pixels display multiple velocity components, alongside a global velocity gradient of 0.18~km~s$^{-1}$~pc$^{-1}$. Typical line widths are $1.2 \pm 0.6$~\mbox{km~s$^{-1}$} for $^{12}$CO, while $^{13}$CO components are systematically narrower ($\lesssim 0.7\,ΔV_{\rm ^{12}CO}$). The $^{12}$CO/$^{13}$CO intensity ratios (5--25) indicate widespread $^{12}$CO optical thickness, resembling conditions found in giant molecular clouds (GMCs). Globally, the CO emission divides into two groups: a major group aligned with the velocity gradient and a secondary group elongated perpendicular to it, possibly regulated by large-scale coherent dynamics. We propose a three-layer hierarchy: a dynamically assembling and dispersing periphery traced by $^{12}$CO, a more stable intermediate kernel traced by $^{13}$CO, and gravitationally bound compact cores traced by C$^{18}$O. No young stellar objects are firmly associated with the molecular gas. PPCOS provides an ideal laboratory for studying turbulence, hierarchical structure, and early cloud evolution in a nearby, relatively simple molecular cloud.

astro-ph.GA↗

arXiv2026

Stable Velocity: A Variance Perspective on Flow Matching

While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimization and slow convergence. By explicitly characterizing this variance, we identify 1) a high-variance regime near the prior, where optimization is challenging, and 2) a low-variance regime near the data distribution, where conditional and marginal velocities nearly coincide. Leveraging this insight, we propose Stable Velocity, a unified framework that improves both training and sampling. For training, we introduce Stable Velocity Matching (StableVM), an unbiased variance-reduction objective, along with Variance-Aware Representation Alignment (VA-REPA), which adaptively strengthen auxiliary supervision in the low-variance regime. For inference, we show that dynamics in the low-variance regime admit closed-form simplifications, enabling Stable Velocity Sampling (StableVS), a finetuning-free acceleration. Extensive experiments on ImageNet $256\times256$ and large pretrained text-to-image and text-to-video models, including SD3.5, Flux, Qwen-Image, and Wan2.2, demonstrate consistent improvements in training efficiency and more than $2\times$ faster sampling within the low-variance regime without degrading sample quality. Our code is available at https://github.com/linYDTHU/StableVelocity.

cs.CV↗

arXiv2025

The Milky Way Imaging Scroll Painting Survey: Data Release 1

We present the first data release (DR1) of the Milky Way Imaging Scroll Painting (MWISP) survey, a mapping in the J=(1-0) transition lines of 12CO, 13CO, and C18O toward the northern Galactic plane during 2011-2022. The MWISP survey was conducted using the PMO 13.7 m telescope at a spatial resolution of approximately 50" and a velocity resolution of 0.16 km/s at 115 GHz. DR1 fully covered 2310 square degrees within the Galactic longitude (l) and latitude (b) range of 9.75 deg =< l =< 229.75 deg and |b| =< 5.25 deg. The surveyed area was divided into cell units of 30'x30' for practical purposes and On-The-Fly (OTF) mapping was performed toward each target cell unit. The data were regridded into a regular 3D datacube in l-b-V_LSR with a pixel size of 30" in l-b axes and 0.16 km/s in theV_LSR axis. The median rms noise is 0.47 K, 0.25 K, and 0.25 K for 12CO, 13CO, and C18O, respectively. The equivalent 3 sigma sensitivity in 12CO luminosity is approximately 0.23 K km/s, making MWISP the most sensitive survey of its kind. In this paper, we describe the survey data, including the calibration, data cleaning, data mosaic processes, and the data products. The final mosaicked data cubes contain about 3.33x10^7 spectra (pixels) for each CO isotopologue line. Color composite images, made from the intensities of the isotopologue lines, and some concise descriptions are provided. We constructed a molecular cloud catalog based on the mosaicked 12CO data cube using the clustering algorithm DBSCAN, detecting 103,517 molecular clouds, 10,790 of which exhibit 13CO emission and 304 of which show C18O emission. Based on the histogram of voxel brightness temperature, we estimated a total 12CO flux of 7.69+/-0.38x10^7 K km/s arcmin^2, 82% of which is captured by the DBSCAN algorithm. The data, together with the cloud sample, provide unique information on molecular gas in the northern Milky Way.

astro-ph.GA↗

arXiv2025

e-basis Coefficients of Chromatic Symmetric Functions

A well-known result of Stanley's shows that given a graph $G$ with chromatic symmetric function expanded into the basis of elementary symmetric functions as $X_G = \sum c_λe_λ$, the sum of the coefficients $c_λ$ for $λ$ with $λ_1' = k$ (equivalently those $λ$ with exactly $k$ parts) is equal to the number of acyclic orientations of $G$ with exactly $k$ sinks. However, more is known. The sink sequence of an acyclic orientation of $G$ is a tuple $(s_1,\dots,s_k)$ such that $s_1$ is the number of sinks of the orientation, and recursively each $s_i$ with $i > 1$ is the number of sinks remaining after deleting the sinks contributing to $s_1,\dots,s_{i-1}$. Equivalently, the sink sequence gives the number of vertices at each level of the poset induced by the acyclic orientation. A lesser-known follow-up result of Stanley's determines certain cases in which we can find a sum of $e$-basis coefficients that gives the number of acyclic orientations of $G$ with a given partial sink sequence. Of interest in its own right, this result also admits as a corollary a simple proof of the $e$-positivity of $X_G$ when the stability number of $G$ is $2$. In this paper, we prove a vertex-weighted generalization of this follow-up result, and conjecture a stronger version that admits a similar combinatorial interpretation for a much larger set of $e$-coefficient sums of chromatic symmetric functions. In particular, the conjectured formula would give a combinatorial interpretation for the sum of the coefficients $c_λ$ with prescribed values of $λ_1'$ and $λ_2'$ for any unweighted claw-free graph (not necessarily an incomparability graph, as in the setting of the Stanley-Stembridge conjecture).

math.CO↗

arXiv2025

SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups

Finite symmetric groups $S_n$ are essential in fields such as combinatorics, physics, and chemistry. However, learning a probability distribution over $S_n$ poses significant challenges due to its intractable size and discrete nature. In this paper, we introduce SymmetricDiffusers, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over $S_n$ by decomposing it into learning simpler transitions of the reverse diffusion using deep neural networks. We identify the riffle shuffle as an effective forward transition and provide empirical guidelines for selecting the diffusion length based on the theory of random walks on finite groups. Additionally, we propose a generalized Plackett-Luce (PL) distribution for the reverse transition, which is provably more expressive than the PL distribution. We further introduce a theoretically grounded "denoising schedule" to improve sampling and learning efficiency. Extensive experiments show that our model achieves state-of-the-art or comparable performances on solving tasks including sorting 4-digit MNIST images, jigsaw puzzles, and traveling salesman problems. Our code is released at https://github.com/DSL-Lab/SymmetricDiffusers.

cs.LG↗