Active Measurement of Two-Point Correlations
This work addresses the time-consuming task of labeling sources for 2PCF estimation in astronomy, offering a scalable solution for domain-specific applications.
The paper tackles the problem of efficiently measuring two-point correlation functions (2PCF) for specific subsets of points, such as star clusters in astronomy, by developing a human-in-the-loop framework that uses a pre-trained classifier to guide sampling for annotation. It achieves substantially lower variance and reduces annotation effort compared to Monte Carlo approaches.
Two-point correlation functions (2PCF) are widely used to characterize how points cluster in space. In this work, we study the problem of measuring the 2PCF over a large set of points, restricted to a subset satisfying a property of interest. An example comes from astronomy, where scientists measure the 2PCF of star clusters, which make up only a tiny subset of possible sources within a galaxy. This task typically requires careful labeling of sources to construct catalogs, which is time-consuming. We present a human-in-the-loop framework for efficient estimation of 2PCF of target sources. By leveraging a pre-trained classifier to guide sampling, our approach adaptively selects the most informative points for human annotation. After each annotation, it produces unbiased estimates of pair counts across multiple distance bins simultaneously. Compared to simple Monte Carlo approaches, our method achieves substantially lower variance while significantly reducing annotation effort. We introduce a novel unbiased estimator, sampling strategy, and confidence interval construction that together enable scalable and statistically grounded measurement of two-point correlations in astronomy datasets.