arXiv subjects

Yuchen Xing

Publications and source records attributed to Yuchen Xing.

6 recordsLinked to original sources

arXiv2026

ALOHA IRDCs Molecular Line Follow-up: I. Gas properties and kinematics

Infrared Dark Clouds are ideal sites for investigating the initial conditions of massive star and cluster formation. The A Lei Of the Habitat and Assembly of Infrared Dark Clouds (ALOHA IRDCs), a James Clerk Maxwell Telescope (JCMT) Large Program, has mapped nearby IRDCs with SCUBA-2. Complementary molecular line observations are needed to characterise the physical, kinematic, and chemical properties of the dense gas. We aim to determine the thermal, kinematic, and chemical properties of clumps identified in the ALOHA IRDCs, and to assess their evolutionary status and level of star-forming activity. We performed single-pointing K-band and W-band observations towards 56 ALOHA IRDCs clumps using the Effelsberg 100-m and Yebes 40-m telescopes, respectively. We derived NH3 kinetic temperatures using the hyperfine group ratio (HFGR) method and identified infall and shock signatures from HCO+, H13CO+, SiO, and HNCO profiles. Water masers and NH2D emission were used as complementary tracers of chemical evolution and star formation. The clumps exhibit kinetic temperatures of 15-29 K. We detect NH2D emission towards 18 sources, with NH2D centroid velocities consistent with NH3, indicating both species trace the same dense gas component. More than half of the clumps display blue-asymmetric HCO+ profiles, identifying them as infall candidates. Water masers are detected in 22 sources, with prominent velocity ranges and variability. Broad SiO emission (>~20 km/s) indicates strong shocks, while narrower extents (<~6km/s) likely trace large-scale interactions or low-velocity shocks. The widespread infall signatures, shock tracers, masers, and NH2D emission suggest that relatively quiescent, chemically young material can coexist with dynamically active gas affected by early protostellar feedback, providing insight into the coupled physical and chemical evolution of massive IRDC clumps.

astro-ph.GA↗

arXiv2026

CARPP: Parametric Radiative-Transfer Fitting of Molecular Cores from Dust Continuum Data

The density profiles of dense molecular cores are important indicators of their physical and evolutionary states. Multi-wavelength dust continuum data offers excellent constraints on the density profile of cores. Here we introduce CARPP (Core Analysis via Radiative Transfer and Profile Parameters), a publicly available fitting package that generates optimized core density and temperature profiles based on parameterized radiative transfer calculations. CARPP assumes spherical symmetry and adopts physically motivated parametric forms for the density and temperature profiles, and uses dust continuum data for fitting. Tests on synthetic data show that CARPP achieves high accuracy, namely averaged relative errors of CARPP's seven parameters being $<20\%$, when the data quality satisfies $\frac{\rm RMS \,\, noise}{[\rm peak \,\, flux]} < 0.025\times \frac{[r_0]}{\rm resolution} +0.05$, where $r_0$ is the core's characteristic radius. We select the low-mass core TMC-1C and the high-mass core Ori2-2 to demonstrate CARPP's performance on real data. It classifies TMC-1C as a Bonnor-Ebert sphere in near-hydrostatic equilibrium, while Ori2-2 exhibits a power-law-dominated profile indicative of a collapsing envelope. This capability establishes CARPP as a powerful and versatile tool to classify the dynamical states of individual cores. It offers an optimal balance between physical fidelity and computational efficiency, serving as a practical, standardized alternative to both over-simplified SED analyses and complex, time-intensive 3D radiative-transfer modeling.

astro-ph.GA↗

arXiv2026

A tool of Hierarchical cOre ideNtification and Kinematic property AssIgnment (HONKAI) for Dense Cores

Infrared dark clouds (IRDCs) contains cold dense gas at the earliest stage of massive star and cluster formation. In studying the IRDCs, a universal and fundamental task is to resolve their internal hierarchical structures. Various packages and algorithms were developed for this purpose, but with most of them mainly focused on certain individual steps in data processing. In this work, we build a more automatic procedure for multi-band structure measurement HONKAI (Hierarchical cOre ideNtification and Kinematic property AssIgnment), which can resolve the elemental components including cores and clumps, disentangle the velocity components in spectral data, measure their physical properties, and generate a catalogue for all the measured properties. We use {\sc honkai} for a joint study towards three IRDCs observed in 850 $\mu$m dust continuum with James Clerk Maxwell Telescope (JCMT) and the $^{13}CO$ $(1-0)$ data cube with the Purple Mount Observatory 14-m telescope. 193 dense cores in 16 clumps are identified. As major dynamical properties, a large amount of the cores (136 out of 193) are measured to have large virial ratio of $R_{\rm vir}>1$, but their mass-size relation is bellow the threshold for massive star formation. Meanwhile, core mass function (CMF) also exhibits a steeper slope towards high-mass end compared to more evolved core samples. These three properties in accordance suggest that although many IRDC cores are self-gravitating, only a small fraction are seemingly possible to form high-mass stars. In subsequent core evolution, some further mass assembly trend may be involved to facilitate the high-mass star formation.

astro-ph.GA↗

arXiv2026

Progressive Video Condensation with MLLM Agent for Long-form Video Understanding

Understanding long videos requires extracting query-relevant information from long sequences under tight compute budgets. Existing text-then-LLM pipelines lose fine-grained visual cues, while video-based multimodal large language models (MLLMs) can keep visual details but are too frame-hungry and computationally expensive. In this work, we aim to harness MLLMs for efficient video understanding. We propose ProVCA, a progressive video condensation agent that iteratively locates key video frames at multiple granularities. ProVCA first adopts a segment localization module to identify the video segment relevant to the query, then a snippet selection module to select important snippets based on similarity, and finally a keyframe refinement module to pinpoint specific keyframes in those snippets. By progressively narrowing the scope from coarse segments to fine frames, ProVCA identifies a small set of keyframes for MLLM-based reasoning. ProVCA achieves state-of-the-art zero-shot accuracies of 69.3\% on EgoSchema, 80.5\% on NExT-QA, and 77.7\% on IntentQA, while using fewer frames than previous training-free methods.

cs.CV↗

arXiv2026

The Column Density Probability Density Function of Cygnus-X

The density distribution within molecular clouds offers critical insights into their underlying physical processes, which are essential for understanding star formation. As a statistical measure of column density on the cloud scale, the shape and evolution of the column density probability density function (N-PDF) serve as important tools for understanding the dynamics between turbulence and gravity. Here we investigate the N-PDFs of Cygnus-X using the column density map obtained from Herschel, supplemented by HI and Young Stellar Objects (YSO) data. We find that the N-PDFs of Cygnus-X and four sub-regions display log-normal + power-law shapes, indicating the combined effects of turbulence and gravity in sculpting the density structure. We find evidence that the power-law segment of the N-PDFs flattens over time, and the transitional column density can be seen as a unique and stable star formation threshold specific to each molecular cloud. These results not only clarify the physical state of Cygnus-X but also emphasize the utility of the N-PDF as a statistical diagnostic tool, as it is an accessible indicator of evolutionary stages and star formation thresholds in molecular clouds.

astro-ph.GA↗

arXiv2022

Revisiting The Mass-Size Relation Of Structures In Molecular Clouds

We revisit the mass-size relation of molecular cloud structures based on the column density map of the Cygnus-X molecular cloud complex. We extract 135 column density peaks in Cygnus-X and analyze the column density distributions around these peaks. The averaged column density profiles, $N(R)$, around all the peaks can be well fitted with broken power-laws, which are described by an inner power-law index $n$, outer power-law index $m$, and the radius $R_{\rm TP}$ and column density $N_{\rm TP}$ at the transition point. We then explore the $M-R$ relation with different samples of cloud structures by varying the $N(R)$ parameters and the column density threshold, $N_0$, which determines the boundary of a cloud structure. We find that only when $N_0$ has a wide range of values, the $M - R$ relation may largely probe the density distribution, and the fitted power-law index of the $M-R$ relation is related to the power-law index of $N(R)$. On the contrary, with a constant $N_0$, the $M - R$ relation has no direct connection with the density distribution; in this case, the fitted power-law index of the $M - R$ relation is equal to 2 (when $N_0\ge N_{\rm TP}$ and $n$ has a narrow range of values), larger than 2 (when $N_0\ge N_{\rm TP}$ and $n$ has a wide range of values), or slightly less than 2 (when $N_0< N_{\rm TP}$).

astro-ph.GA↗