Context. Blob detection is a common problem in astronomy. One example is in stellar population modelling, where the distribution of stellar ages and metallicities in a galaxy is inferred from observations. In this context, blobs may correspond to stars born in-situ versus those accreted from satellites, and the task of blob detection is to disentangle these components. A difficulty arises when the distributions come with significant uncertainties, as is the case for stellar population recoveries inferred from modelling spectra of unresolved stellar systems. There is currently no satisfactory method for blob detection with uncertainties. Aims. We introduce a method for uncertainty-aware blob detection developed in the context of stellar population modelling of integrated-light spectra of stellar systems. Methods. We develop theory and computational tools for an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. This identifies significant blobs considering a variety of scales. As a prerequisite to apply ULoG to stellar population modelling, we introduce a method for efficient computation of uncertainties for spectral modelling. This method is based on the truncated Singular Value Decomposition and Markov Chain Monte Carlo sampling (SVD-MCMC). Results. We apply the methods to data of the star cluster M54. We show that the SVD-MCMC inferences match those from standard MCMC, but are a factor 5-10 faster to compute. We apply ULoG to the inferred M54 age/metallicity distributions, identifying between 2 or 3 significant, distinct populations amongst its stars.

翻译：背景。斑点检测是天文学中的一个常见问题。一个例子是在恒星种群建模中，通过观测推断星系中恒星年龄和金属丰度的分布。在此背景下，斑点可能对应于原位形成的恒星与从卫星星系吸积的恒星，而斑点检测的任务是区分这些成分。当分布存在显著不确定性时（例如，从无解析恒星系统的光谱建模推断出的恒星种群恢复），问题变得困难。目前尚缺乏令人满意的方法来应对带有不确定性的斑点检测。目的。我们提出一种在集成光光谱恒星系统建模背景下开发的不确定性感知斑点检测方法。方法。我们为经典的高斯拉普拉斯斑点检测方法开发了理论及计算工具，引入其不确定性感知版本，称之为ULoG。该方法在考虑多种尺度的同时识别显著斑点。作为将ULoG应用于恒星种群建模的前提，我们提出了一种高效计算光谱建模不确定性的方法。该方法基于截断奇异值分解和马尔可夫链蒙特卡洛采样（SVD-MCMC）。结果。我们将上述方法应用于星团M54的数据。结果表明，SVD-MCMC推断与标准MCMC结果相匹配，但计算速度提升了5-10倍。我们应用ULoG分析M54推断的年龄/金属丰度分布，在其恒星中识别出2-3个显著的、不同的种群。