Wanyan Yuan | Search arXiv

arXiv2026

Tracing Winds through the Fog: A Comprehensive Survey of LMC's Galactic Outflows with ULLYSES

We investigate nearside stellar-driven outflows from the Large Magellanic Cloud (LMC) using UV absorption-line spectroscopy of 170 OB stars, complemented by HI 21-cm emission and H$_α$ emission. Using Voigt-profile fitting and AOD analysis of SiII, OI, and SII transitions, we map the velocity-dependent structure of foreground gas. The SiII column densities for the Voigt-fitted components decline smoothly from the LMC disk and reach a minimum at $v_{\rm LMCSR} \approx -100$ to $-200~{km~s^{-1}}$, marking the transition from a denser, slower wind to more diffuse high-velocity material. Comparisons with local star-formation rate surface densities reveal a modest positive correlation for the slower wind component, linking it to recent massive-stellar feedback. Photoionization modeling of 13 absorbers in the $+100\lesssim v_{\rm LSR} \lesssim +150~{km~s^{-1}}$ range reveals wide diversity in metallicity, dust depletion, and ionization conditions, consistent with multiple possible origins. While a substantial fraction of this predominately ionized gas is consistent with the high-velocity extension of the LMC wind, part of it may arise from contamination by Milky Way (MW) high-velocity clouds (HVCs) and Magellanic circumgalactic medium (CGM). We estimate a nearside cool-gas outflow mass of ${\sim}1.8\times10^{7}\,M_{\odot}$, implying $\dot{M}_{\rm out}\approx0.15$-$0.33\,M_{\odot}\,\mathrm{yr^{-1}}$ and a mass-loading factor of $η\approx0.6$-1.3. On regional scales, 30 Doradus contributes $\sim10\%$ and N11 contributes $\sim3\%$ of the total outflow mass, while the trailing side contains more wind material than the leading side, consistent with ram-pressure stripping. These results provide the comprehensive kinematic and physical characterization of how stellar feedback, galactic environment, and foreground contamination shape the multiphase wind emerging from the LMC.

astro-ph.GA↗

arXiv2026

Grading Scale Impact on LLM-as-a-Judge: Human-LLM Alignment Is Highest on 0-5 Grading Scale

Large language models (LLMs) are increasingly used as automated evaluators, yet prior works demonstrate that these LLM judges often lack consistency in scoring when the prompt is altered. However, the effect of the grading scale itself remains underexplored. We study the LLM-as-a-judge problem by comparing two kinds of raters: humans and LLMs. We collect ratings from both groups on three scales and across six benchmarks that include objective, open-ended subjective, and mixed tasks. Using intraclass correlation coefficients (ICC) to measure absolute agreement, we find that LLM judgments are not perfectly consistent across scales on subjective benchmarks, and that the choice of scale substantially shifts human-LLM agreement, even when within-group panel reliability is high. Aggregated over tasks, the grading scale of 0-5 yields the strongest human-LLM alignment. We further demonstrate that pooled reliability can mask benchmark heterogeneity and reveal systematic subgroup differences in alignment across gender groups, strengthening the importance of scale design and sub-level diagnostics as essential components of LLM-as-a-judge protocols.

cs.CL↗