arXiv subjects

Yuetong Wang

Publications and source records attributed to Yuetong Wang.

3 recordsLinked to original sources

arXiv2026

Exploring the Small-scale Magnetic Fields in the Atmosphere of HD 49385 by Asteroseismic Analysis

Recent asteroseismic studies have shown convincing evidences that magnetic fields may exist in the interior of some pulsating red giants. Inspired by this breakthrough, we explored the effect of small-scale magnetic fields on the p-mode oscillations in an evolved star, HD 49385. {\bf We incorporate a modified Eddington $T$-$\tau$ equation that phenomenologically mimics the effect of the magnetic fields in the atmosphere of HD 49385,} and calculate the frequencies of p-modes with $l=0$, 1, and 2. By comparing the calculated frequencies with the observed ones, we select two best-fit models with either GS98 or A09 chemical composition. Our best-fit models not only fit satisfactorily the observed frequencies, but also well reproduce some spectroscopically observed stellar parameters such as effective temperature and log\,$g$. Based on the two best-fit models, we have estimated that the small-scale magnetic fields possess a strength of approximately 80\,G and spread concentratively at approximately a height of 1850 km in the atmosphere. By selecting the best-fit models with special requirement on the avoided-crossing mode, we have confirmed that the frequency of the avoided-crossing mode is tightly related to the helium core of the star, and determined the size of the helium core as 0.117${\rm M}_\odot$ in mass and 0.078${\rm R}_\odot$ in radius. Based on the improvements of previous two sides, we can accurately determine the mass of HD 49385 to be $1.25\pm 0.02\,{\rm M}_\odot$ with an age of 4.1\,Gyr for GS98 composition and 4.5\,Gyr for A09 composition.

astro-ph.SR↗

arXiv2026

Asteroseismic Analysis of a Red Giant KIC 9145955 by Including the Small-scale Magnetic Fields in the Atmosphere

Recent convincing evidence is found within asteroseismology that suggests the magnetic fields exist in three red giants. Research on small-scale magnetic fields in the Sun and HD 49385 has shown that they have a certain corrective effect on the systematic discrepancies between observed and theoretical frequencies. Here we apply a similar method applied for the Sun to a red giant, KIC 9145955, to explore the impact of small-scale magnetic fields in the photosphere on its frequencies. We find that the calculated frequencies of our best-fit model, which simulates the effect of the magnetic fields by artificially modifying the Eddington $T-\tau$ relation, perfectly match those of the observed l = 0, 1, and 2 modes, indicating the existence of small-scale magnetic fields with an upper strength limit of 65 G and concentrating at a height 13,100 km in the photosphere. Based on the best-fit model, we revise the stellar parameters of KIC 9145955 as: $M = 1.23\pm0.04\,M_\odot$, $R = 5.57\pm0.06\,R_\odot$, $L = 19.85\pm0.5\,L_\odot$, $Age = 3.83\pm0.5$\,Gyr, $M_{\rm He} = 0.2108\pm0.0005M_\odot$, and $R_{\rm He} = 0.0306\pm0.0001R_\odot$.

astro-ph.SR↗

arXiv2022

COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users' cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions.

cs.LG↗