发表机构
I. Physikalisches Institut, Universität zu Köln; Institut de Ciències de l’Espai (ICE), CSIC; Institut d’Estudis Espacials de Catalunya (IEEC); Center for Data and Simulation Science, University of Cologne; Jodrell Bank Centre for Astrophysics, Department of Physics & Astronomy, The University of Manchester（科隆大学第一物理研究所; 空间科学研究所，西班牙高等科学研究委员会; 加泰罗尼亚空间研究学院; 科隆大学数据与模拟科学中心; 曼彻斯特大学天体物理学乔德雷尔银行研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过合成观测量化ALMA类似数据推断恒星形成区核质量与温度的可靠性，发现投影效应和通量重建导致质量误差达3-5倍，提出含局部核信息的温度估算可减少偏差。
AI 中文摘要
致密核和团块在大质量恒星形成区中通过（亚）毫米连续谱观测进行研究，但推断出的质量和温度可能受到有限分辨率、干涉滤波、投影效应以及不确定的尘埃温度的影响。我们利用这些团块的合成观测，量化了从ALMA类似连续谱数据中推断核质量和温度的可靠性。我们对坍缩的1000 M$_\odot$ 团块进行了辐射流体动力学模拟的后处理，使用RADMC-3D和CASA生成了合成的ALMAGAL类似图像。核围绕吸积粒子定义，从而能够比较来自3D模拟、理想化RADMC-3D图和合成ALMA图像的质量。我们量化了分辨率、投影和视角对核质量的影响，并测试了三种尘埃温度估算器（团块尺度L/M、L/M+通量模型以及监督机器学习估算）。投影效应导致1500 au内视线积分核质量超过球形质量的2至3倍。经验L/M温度关系遵循整体热演化，但遗漏了核与核之间的离散性，导致核质量误差达到数倍。包含局部通量改善了温度和质量的估算，而梯度提升机器学习模型则显示出进一步的改进。合成的ALMAGAL类似图像恢复了整体核结构，但通量重建的不确定性引入了高达3倍的质量误差，并将可靠的核质量恢复限制在$\gtrsim 0.3$ M$_\odot$，与ALMAGAL的完备性极限相当。单个核质量通常存在3至5倍的不确定性，主要源于尘埃温度假设和通量重建。减少这些偏差需要包含局部核信息的温度估算，而非单一的团块尺度L/M关系。
英文摘要
Dense cores and clumps in massive star-forming regions are studied through (sub-)millimetre continuum observations, but inferred masses and temperatures can be biased by limited resolution, interferometric filtering, projection, and uncertain dust temperatures. We quantify how reliably core masses and temperatures can be inferred from ALMA-like continuum data using synthetic observations of such clumps. We post-processed radiation-hydrodynamic simulations of collapsing 1000 M$_\odot$ clumps with RADMC-3D and CASA to generate synthetic ALMAGAL-like images. Cores were defined around sink particles, enabling comparison of masses from the 3D simulations, idealised RADMC-3D maps, and synthetic ALMA images. We quantified how resolution, projection, and viewing angle affect core masses, and tested three dust-temperature estimators (clump-scale L/M, a L/M+flux model, and a supervised machine-learning estimate). Projection effects cause line-of-sight integrated core masses within 1500 au to exceed spherical masses by factors of two to three. The empirical L/M temperature prescription follows the global thermal evolution but misses the core-to-core scatter, producing core-mass errors of a factor of a few. Including local flux improves temperature and mass estimates, while a gradient-boosting machine-learning model suggests further improvement. Synthetic ALMAGAL-like images recover the global core structure, but flux-reconstruction uncertainties introduce mass errors up to a factor of three and limit reliable core-mass recovery to $\gtrsim 0.3$ M$_\odot$, comparable to the ALMAGAL completeness limit. Individual core masses are typically uncertain by a factor of three to five, mainly due to dust-temperature assumptions and flux reconstruction. Reducing these biases requires temperature estimates that include local core information rather than a single clump-scale L/M-based prescription.
Journal refAstronomy & Astrophysics, 712, A238 (2026), 18 pp
DOI：10.1051/0004-6361/202659518
