Zongheng Li | Search arXiv

arXiv2026

Metal-Poor Stars as Metal-Rich Impostors via Planet Engulfment

Planet engulfment leads to planetary mass accretion and angular momentum deposition into the stellar envelope. This study uses MESA models to investigate the long-term impact of engulfment on stellar metallicity and rotational velocity, as well as the effect of rotational mixing on metal enrichment. Our models show that engulfment occurring during the late main-sequence (MS) stage yields significantly higher surface enrichment than at the ZAMS. Rotational mixing generally suppresses enrichment; however, metal-poor stars can sustain super-solar abundances for several Gyr, whereas high-metallicity stars remain largely unaffected. Although engulfment causes an instantaneous spin-up, this angular momentum injection is transient, and the star quickly re-converges to its standard rotational evolution. Thus, MS spin-up cannot serve as a long-term observational indicator. Because convective envelope thickness and MS lifetime dictate the magnitude and duration of enrichment, identifying engulfment signatures requires balancing the long-lived but diluted signals in 0.7 M_sun stars against the strong yet short-lived enrichment in 1.2 M_sun stars. Consequently, 1.0 M_sun stars offering both substantial enrichment and a relatively long MS lifetime represent optimal targets for observational searches.

astro-ph.SR↗

arXiv2025

Marine Chlorophyll Prediction and Driver Analysis based on LSTM-RF Hybrid Models

Marine chlorophyll concentration is an important indicator of ecosystem health and carbon cycle strength, and its accurate prediction is crucial for red tide warning and ecological response. In this paper, we propose a LSTM-RF hybrid model that combines the advantages of LSTM and RF, which solves the deficiencies of a single model in time-series modelling and nonlinear feature portrayal. Trained with multi-source ocean data(temperature, salinity, dissolved oxygen, etc.), the experimental results show that the LSTM-RF model has an R^2 of 0.5386, an MSE of 0.005806, and an MAE of 0.057147 on the test set, which is significantly better than using LSTM (R^2 = 0.0208) and RF (R^2 =0.4934) alone , respectively. The standardised treatment and sliding window approach improved the prediction accuracy of the model and provided an innovative solution for high-frequency prediction of marine ecological variables.

cs.LG↗