Automated Telescope-Paper Linkage via Multi-Model Ensemble Learning

AbstractAutomated linkage between scientific publications and telescope datasets is a cornerstone for scalable bibliometric analyses and ensuring scientific reproducibility in astrophysics. We propose a multi-model ensemble architecture integrating transformer models DeBERTa, RoBERTa, and TF-IDF logistic regression, tailored to the WASP-2025 shared task on telescope-paper classification. Our approach achieves a macro F1 score approaching 0.78 after extensive multi-seed ensembling and per-label threshold tuning, significantly outperforming baseline models. This paper presents comprehensive methodology, ablation studies, and an in-depth discussion of challenges, establishing a robust benchmark for scientific bibliometric task automation.