Xianqi Liu | Searcharxiv

arXiv2026

A Catalog of 1,408 Carbon-Enhanced Metal-Poor Stars from LAMOST DR11

Metal-poor (MP) stars are important targets for investigating the chemical evolution of the early universe. Among them, Carbon-Enhanced Metal-Poor (CEMP) stars have attracted extensive attention due to their rarity and astrophysical significance. Owing to their low occurrence rate, the identification of MP stars and CEMP stars remains a task of considerable scientific value. In this study, we investigate the search for CEMP stars based on the low-resolution stellar spectra from LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) DR11 and propose a deep-learning-based approach for this purpose. By analyzing the LAMOST DR11 spectral library, we identify 1,408 CEMP star candidates. For ease of reference and further use, we provide the estimated stellar parameters for these objects, including $T_\texttt{eff}$, $\log~g$, [Fe/H], and [C/H].

astro-ph.SR↗

arXiv2025

A Weak Signal Learning Dataset and Its Baseline Method

Weak signal learning (WSL) is a common challenge in many fields like fault diagnosis, medical imaging, and autonomous driving, where critical information is often masked by noise and interference, making feature identification difficult. Even in tasks with abundant strong signals, the key to improving model performance often lies in effectively extracting weak signals. However, the lack of dedicated datasets has long constrained research. To address this, we construct the first specialized dataset for weak signal feature learning, containing 13,158 spectral samples. It features low SNR dominance (over 55% samples with SNR below 50) and extreme class imbalance (class ratio up to 29:1), providing a challenging benchmark for classification and regression in weak signal scenarios. We also propose a dual-view representation (vector + time-frequency map) and a PDVFN model tailored to low SNR, distribution skew, and dual imbalance. PDVFN extracts local sequential features and global frequency-domain structures in parallel, following principles of local enhancement, sequential modeling, noise suppression, multi-scale capture, frequency extraction, and global perception. This multi-source complementarity enhances representation for low-SNR and imbalanced data, offering a novel solution for WSL tasks like astronomical spectroscopy. Experiments show our method achieves higher accuracy and robustness in handling weak signals, high noise, and extreme class imbalance, especially in low SNR and imbalanced scenarios. This study provides a dedicated dataset, a baseline model, and establishes a foundation for future WSL research.

cs.LG↗