攀登悬崖：使用概率随机森林对宇宙悬崖 JWST 数据中的 YSO 进行分类
Climbing the Cliffs: Classifying YSOs in the Cosmic Cliffs JWST Data using a Probabilistic Random Forest
AI总结：
本文基于 JWST“宇宙悬崖”测光星表，采用概率随机森林识别出 450 个候选年轻恒星体，并用 Monte Carlo 方法揭示其与尘埃柱密度的空间关联及大量亚恒星 YSO。
摘要：
詹姆斯·韦布空间望远镜（JWST）最早向公众发布的观测之一，是恒星形成区 NGC 3324 的一个区域，俗称“宇宙悬崖”。我们构建了该区域的测光星表，并测试使用 Probabilistic Random Forest（概率随机森林）机器学习方法识别其中 Young Stellar Objects（YSO，年轻恒星体）的能力。在视场内 19 497 个总天体中，我们发现 450 个候选 YSO（cYSO），其中 413 个是以往工作未发现的 cYSO。这些分类通过包括召回率和精确率在内的若干不同指标进行了验证。利用所得天体为 YSO 的概率，我们采用 Monte Carlo 方法确定宇宙悬崖中 cYSO 的面密度；结果发现，在柱密度最高至 1.37 × 10^22 cm^-2 的范围内，该面密度与由 Herschel 数据导出的柱密度大体一致。新确定的宇宙悬崖中 YSO 的数量和空间分布表明，JWST 在尘埃区域探测 YSO 的能力远强于 Spitzer。将暗弱 cYSO 的观测颜色和亮度与主序前模型进行比较，表明 JWST 已在宇宙悬崖中探测到大量亚恒星 YSO 群体。这一群体的规模进一步表明，以往对分子云恒星形成效率的估计存在系统性偏低。
英文摘要：
Among the first observations released to the public from the James Webb Space Telescope (JWST) was a section of the star-forming region NGC 3324 known colloquially as the "Cosmic Cliffs." We build a photometric catalog of the region and test the ability of using the Probabilistic Random Forest machine learning method to identify its Young Stellar Objects (YSOs). We find 450 candidate YSOs (cYSOs) out of 19~497 total objects within the field, 413 of which are cYSOs not found in previous works. These classifications are verified with several different metrics, including recall and precision. Using the obtained probabilities of objects being YSOs, we employ a Monte Carlo approach to determine the surface density of cYSOs in the Cosmic Cliffs, which we find to be largely coincident with column densities derived from Herschel data, up to a column density of 1.37 $\times$ 10$^{22}$ cm$^{-2}$. The newly determined number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that JWST is far more capable of detecting YSOs in dusty regions than Spitzer. Comparisons of the observed colors and brightness of faint cYSOs with those of pre-main-sequence models suggest JWST has detected a significant population of sub-stellar YSOs in the Cosmic Cliffs. The size of this population further suggests previous estimates of star formation efficiencies in molecular clouds have been systematically low.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
