How do Density, Feedback, Gas Dynamics, and Magnetic Fields affect Star Formation within Clusters?

了解恒星的起源是现代天体物理学的一项重大任务。恒星形成于银河系中巨大的分子云深处，这是它们的气体密度和运动、局部磁场以及来自附近不同尺度的年轻恒星的能量反馈之间复杂相互作用的结果。特别是，大多数恒星，尤其是那些质量较大的恒星，实际上是在嵌入的星团中形成的，这些星团是由30-1000多颗原恒星组成的，它们相互之间以及它们的伴生气体受到引力的束缚。这样的环境是复杂的，因为附近有如此多的原恒星，它们会影响周围的环境。然而，对星星形成的一般理解需要对这些环境中的物理因素进行彻底的分析，因为它们导致了所有已知质量的恒星。事实上，我们的太阳和它的行星系统可以说是在这样一个集群环境中形成的。我们提出了两个项目，使用令人兴奋的新观测和技术深入研究星团内恒星的起源。首先，我们将利用即将发射的詹姆斯·韦伯空间望远镜的中红外数据和阿塔卡马大型毫米阵列的亚毫米数据，对在离太阳最近的分子云中发现的嵌入式星团进行全面普查，这些数据将清楚地揭示它们最年轻的原恒星种群。为了帮助我们找到这些对象，我们还将开发一种新的基于机器学习的分类算法。由此产生的数字将与使用赫歇尔空间天文台远红外波长数据获得的附近云层中气体分布的高分辨率地图进行比较。事实上，之前对这些量的检查显示它们之间存在很强的相关性，但必须在嵌入式集群的最极端环境中进行测试。第二，我们将研究稠密气体和磁场在同一星团中的运动，特别是它们所在的云中较大的对流结构和其中较小的对流结构。氨是稠密气体运动的一个很好的示踪物，它向许多邻近星系团的线发射已经由绿色班克望远镜绘制出来，偏振数据是磁场方向的一个很好的示踪物，来自同一地点的詹姆斯·克拉克麦克斯韦望远镜的偏振数据也将很快得到。分析将使用新技术进行，该技术将气体流隔离在灯丝内。事实上，这些数据集的组合将使气体速度梯度与磁场方向和强度之间建立新的联系。鉴于它们的共同目标，这两个项目是互补的，将共同为理解星星的形成做出重大贡献。这些项目将成为两名研究生博士论文的基础，并将有助于在未来五年及以后保持加拿大在这一领域的全球领导地位。

Understanding the origins of stars is a major undertaking in modern astrophysics. Stars form deep within vast molecular clouds across the Galaxy, as the result of a complex interplay between their gas densities and motions, the local magnetic fields, and energy feedback from nearby young stars on different scales. In particular, most stars, and especially those of higher mass, actually form within embedded clusters, groups of 30-1000+ protostars gravitationally bound to each other and their associated gas. Such environments are complicated by the close proximity of so many nearby protostars that impact their surroundings. Nevertheless, a general understanding of star formation requires a thorough analysis of the physical factors within such environments since they lead to stars of all known mass. Indeed, our Sun and its planetary system arguably formed within such a clustered environment. We propose two projects that delve into the origins of stars within clusters using exciting new observations and techniques. First, we will obtain a full census of embedded clusters found in the molecular clouds nearest to the Sun, using mid-infrared data from the soon-to-be-launched James Webb Space Telescope and submillimetre data from the Atacama Large Millimetre Array that together will reveal clearly their youngest protostar populations. To help us find these objects, we will also develop a new machine learning-based classification algorithm. The resulting numbers will be compared to high-resolution maps of the distributions of gas across nearby clouds obtained using data from the Herschel Space Observatory at far-infrared wavelengths. Indeed, a previous examination of these quantities revealed a strong correlation between them but it must be tested within the most extreme environments of embedded clusters. Second, we will examine the motions of the dense gas and the magnetic fields in the same clusters, specifically the larger filamentary structures in clouds that host them and the smaller ones within them. The line emission from ammonia, a great tracer of dense gas motions, toward many nearby clusters has been already mapped from the Green Bank Telescope, and polarization data, a great tracer of magnetic field direction, from the James Clerk Maxwell Telescope in the same locations will be soon also available. The analysis will be carried out using new techniques that isolate gas flows within filaments. Indeed, the combination of these datasets will enable new connections to be made between gas velocity gradients and magnetic field directions and strengths. Given their common targets, the two projects are complementary and together will make significant contributions to understanding star formation in general. These projects will be the basis of the PhD dissertations of two graduate students, and will help maintain Canada's global leadership in this field over the next five years and beyond.