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要工作。恒星形成于横跨银河系的巨大分子云深处，这是气体密度和运动、局部磁场以及来自附近不同尺度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，特别是那些质量更高的恒星，实际上是在嵌入的星团中形成的，这些星团由30-1000多颗原恒星组成，它们相互之间以及它们的伴生气体受到引力的束缚。这样的环境由于附近有如此多的原恒星会影响周围的环境而变得复杂。然而，对恒星形成的一般理解需要对这种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群集的环境中形成的。我们提出了两个项目，利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·韦伯太空望远镜的中红外数据和阿塔卡马大型毫米波阵列的亚毫米波数据，对离太阳最近的分子云中发现的嵌入星团进行全面普查，这些数据将清楚地揭示最年轻的原恒星群。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。得到的数据将与赫歇尔空间天文台在远红外波段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，之前对这些数量的研究表明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。其次，我们将研究致密气体的运动和同一星团中的磁场，特别是云中较大的丝状结构和其中较小的丝状结构。来自氨气的发射线——密集气体运动的重要示踪剂——已经被格林班克望远镜绘制出来，而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——磁场方向的重要示踪剂——也将很快可用。分析将使用隔离细丝内气流的新技术进行。事实上，这些数据集的结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。考虑到它们的共同目标，这两个项目是互补的，它们将共同为理解恒星形成做出重大贡献。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乃至更长时间内保持加拿大在该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.