arXiv subjects

Yani Guo

Publications and source records attributed to Yani Guo.

3 recordsLinked to original sources

arXiv2026

A Photometric and Spectroscopic investigation of 11 TESS eclipsing contact binaries

By cross-matching the eclipsing binary catalog provided by Prsa et al. (2022) with LAMOST medium resolution spectra, we obtained 11 targets. Combining light and radial velocity curves analysis, we have derived accurate physical parameters for these 11 targets. The results indicate that there are 3 deep contact binaries, 3 moderate ones, and 5 shallow ones. Among them, 3 targets exhibit the O'Connell effect, which is attributed to the presence of star-spot on the component's surface. One target is a low-mass ratio deep contact binary and may be contact binary merging candidates. The evolutionary status of these 11 targets was studied using the mass-luminosity and mass-radius relation diagrams. Based on the O-C (Observed minus Calculated) analysis of 10 targets, we found that the orbital periods of 5 contact binaries show a long-term decreasing trend, likely due to the combined effects of mass transfer between the two components and loss of angular momentum. Meanwhile, the orbital periods of the other 4 stars are continuously increasing, which is attributed to mass transfer. Besides, the O-C curves of 3 targets show clear periodic changes, which might result from the Applegate mechanism or the light travel time effect.

astro-ph.SR↗

arXiv2025

GRETEL: A Goal-driven Retrieval and Execution-based Trial Framework for LLM Tool Selection Enhancing

Despite remarkable advances in Large Language Model capabilities, tool retrieval for agent-based systems remains fundamentally limited by reliance on semantic similarity, which fails to capture functional viability. Current methods often retrieve textually relevant but functionally inoperative tools due to parameter mismatches, authentication failures, and execution constraints--a phenomenon we term the semantic-functional gap. We introduce GRETEL, to address this gap through systematic empirical validation. GRETEL implements an agentic workflow that processes semantically retrieved candidates through sandboxed plan-execute-evaluate cycles, generating execution-grounded evidence to distinguish truly functional tools from merely descriptive matches. Our comprehensive evaluation on the ToolBench benchmark demonstrates substantial improvements across all metrics: Pass Rate (at 10) increases from 0.690 to 0.826, Recall (at 10) improves from 0.841 to 0.867, and NDCG (at 10) rises from 0.807 to 0.857.. These results establish that execution-based validation provides a more reliable foundation for tool selection than semantic similarity alone, enabling more robust agent performance in real-world applications.

cs.LG↗

arXiv2025

The investigation of 84 TESS totally eclipsing contact binaries

Based on the eclipsing binary catalog provided by \cite{2022ApJS..258...16P}, 84 totally eclipsing contact binaries with stable light curves were selected. The TESS light curves of these 84 targets were studied using the Physics Of Eclipsing Binaries code. The results indicate that there are 18 deep contact binaries, 39 moderate contact binaries, and 27 shallow contact binaries. Among them, 43 targets exhibit the O'Connell effect, which is attributed to the presence of star-spot on the component's surface. 15 targets are low-mass ratio deep contact binaries and may be contact binary merging candidates. Based on the relationship between the period and semi-major axis of contact binaries, their absolute physical parameters such as mass, radius, and luminosity were derived. The evolutionary status of these 84 targets was studied using the mass-luminosity and mass-radius relation diagrams. Their initial masses were also estimated. Our results are compared with those of targets that have been historically studied. Among the 84 targets, 44 targets have been studied before, and 21 of these have mass ratios $q$ that are consistent with historical values within a 10\% difference. For the inconsistent targets, we conducted a detailed investigation and found that the main reasons are poor quality of historical data, or the fact that the machine learning methods used in historical studies might not accurately determine the physical parameters for individual targets.

astro-ph.SR↗