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arXiv · 2504.10931

A Highly Efficient Cross-matching Scheme using Learned Index Structure

Phu-Minh Lam·Dongwei Fan·Hongbo Wei·Jun Wang·Yu Zhou·Qi Ma·Baolong Zhang·Xiazhao Zhang·Yongheng Wang
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Abstract

Spatial data fusion is a bottleneck when it meets the scale of 10 billion records. Cross-matching celestial catalogs is just one example of this. To challenge this, we present a framework that enables efficient cross-matching using Learned Index Structures. Our approach involves a data transformation method to map multi-dimensional data into easily learnable distributions, coupled with a novel search algorithm that leverages the advantages of model pairs, significantly enhancing the efficiency of nearest-neighbor search. In this study, we utilized celestial catalog data derived from astronomical surveys to construct the index and evaluated the speed of the cross-matching process. Using the HEALPix segmentation scheme, we built an independent model object for each tile and developed an end-to-end pipeline to construct a framework with semantic guarantees for record retrieval in query and range search. Our results show that the proposed method improves cross-matching speed by more than four times compared to KD-trees for a radius range between 1 milli-arcseconds and 100 arcseconds.

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Phu-Minh Lam, Dongwei Fan, Hongbo Wei, Jun Wang, Yu Zhou, Qi Ma, Baolong Zhang, Xiazhao Zhang, Yongheng Wang. 2025-04-15. A Highly Efficient Cross-matching Scheme using Learned Index Structure. https://arxiv.org/abs/2504.10931

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