发表机构
University of Melbourne; RMIT University（墨尔本大学; 皇家墨尔本理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出时空自传播对数高斯Cox-Hawkes连续点过程模型，结合自发形成、激发、抑制与较差自转，无需预设螺旋几何即可生成絮状螺旋图案，为恒星形成观测的统计推断奠定基础。
AI 中文摘要
随机自传播恒星形成模型通过局部触发和反馈描述星系结构，但通常使用离散空间单元和时间步长来表述。我们将时空对数高斯Cox-Hawkes框架进行扩展，得到时空自传播对数高斯Cox-Hawkes过程，这是一个用于恒星形成事件位置和时间的连续点过程模型。该模型结合了自发形成、相关环境效应、向外传播的激发、局部抑制、较差自转和饱和。一个观测层将条件事件强度转换为瞬时恒星形成旋臂发射率和近期年轻恒星表面亮度的理想化图谱。对于所选参数设置，所展示的实现呈现出瞬态的絮状螺旋状图案，而无需施加任何确定性螺旋几何。蒙特卡洛实验发现，有自转和无自转情况下的事件产生量相似，而静态前沿情形产生的事件较少。所展示的图谱进一步表明，较差自转有助于活动的缠绕和空间排列。所提出的框架为未来从恒星形成的空间和时间分辨观测中进行统计推断提供了基础。
英文摘要
Stochastic self-propagating star formation models describe galactic structure through local triggering and feedback but are commonly formulated using discrete spatial cells and time steps. We extend the spatio-temporal log-Gaussian Cox-Hawkes framework to obtain the Spatio-Temporal Self-Propagating Log-Gaussian Cox-Hawkes Process, a continuous point process model for the locations and times of star-forming events. The model combines spontaneous formation, correlated environmental effects, outwardly propagating excitation, local inhibition, differential rotation, and saturation. An observation layer transforms the conditional event intensity into idealised maps of instantaneous star-forming arm emissivity and recent young stellar surface brightness. For the selected parameter setting, the displayed realisation exhibits transient flocculent spiral-like patterns without any deterministic spiral geometry being imposed. The Monte Carlo experiment finds similar event production with and without rotation, whereas the stationary-front case produces fewer events. The displayed maps further suggest that differential rotation contributes to the winding and spatial arrangement of activity. The proposed framework provides a basis for future statistical inference from spatially and temporally resolved observations of star formation.
