arXiv subjects

Ziming Yan

Publications and source records attributed to Ziming Yan.

3 recordsLinked to original sources

arXiv2025

A search of periodic variable stars in the LMC by JWST photometry

Based on high-resolution near-infrared photometric data from the James Webb Space Telescope (JWST) targeting the Large Magellanic Cloud (LMC), this study attempts to evaluate the feasibility and sensitivity limits of variable star detection in crowded stellar fields. Through light curve analysis, we identified a total of 304 periodic variable stars, including 71 EW-type eclipsing binaries, 7 EA-type eclipsing binaries, 177 rotational variables, 38 $\delta$ Scuti (DSCT) stars, and 12 RR Lyrae stars. Period--luminosity relations (PLRs) were derived for EW-type eclipsing binaries, DSCT stars, and RR Lyrae stars. The PLRs for EW-type and RR Lyrae stars are in good agreement with previous studies, while the PLR zero point for DSCT stars appears systematically fainter by approximately 0.15--0.30 mag. Our PLRs exhibit low dispersion and are minimally affected by crowding. We analyzed the capability of JWST archival data to detect low-amplitude variables and found that only stars with amplitudes greater than approximately 0.05 mag can be reliably detected. Through simulations, we quantified how increasing the number of photometric epochs improves the detectability of low-amplitude, low signal-to-noise ratio variables. Despite current limitations in observational cadence, JWST demonstrates unique advantages in detecting short-period eclipsing binaries, rotational variables, and high-amplitude pulsators. Its exceptional spatial resolution enables high-precision PLR calibrations, offering new opportunities for future studies in variable star astrophysics and extragalactic distance measurements.

astro-ph.SR↗

arXiv2025

The Tip of Red Giant Branch Distances to Nearby Dwarf Galaxies WLM and Sextans A with JWST

Distance measurements to extragalactic systems that are both accurate and precise are cornerstones of modern astrophysics, underpinning the calibration of standard candles and the determination of the Hubble constant. Dwarf galaxies, such as Wolf-Lundmark-Melotte (WLM) and Sextans A, provide valuable laboratories for testing distance scales across different stellar populations. In this work, we utilize the high sensitivity and spatial resolution of the James Webb Space Telescope (JWST) to measure the distances to WLM and Sextans A using the tip of the red giant branch (TRGB) method. Adopting the TRGB absolute magnitude calibrated by NGC 4258, we determine distance moduli of $\mu_{\mathrm{0,WLM}} = 24.977 \pm 0.018 (\mathrm{stat}) \pm 0.056 (\mathrm{sys})$ mag for WLM and $\mu_{\mathrm{0,SexA}} = 25.740 \pm 0.011 (\mathrm{stat}) \pm 0.057 (\mathrm{sys})$ mag for Sextans A. Our results are consistent within a 3% distance uncertainty with previous measurements based on TRGB, Cepheids, and J-Region Asymptotic Giant Branch (JAGB) methods. With improved distance measurements in the future, these two galaxies have the potential to serve as additional anchor points for TRGB calibration, aiming to reduce the TRGB-based distance uncertainty to below 2%.

astro-ph.GA↗

arXiv2024

A Pretraining-Finetuning Computational Framework for Material Homogenization

Homogenization is a fundamental tool for studying multiscale physical phenomena. Traditional numerical homogenization methods, heavily reliant on finite element analysis, demand significant computational resources, especially for complex geometries, materials, and high-resolution problems. To address these challenges, we propose PreFine-Homo, a novel numerical homogenization framework comprising two phases: pretraining and fine-tuning. In the pretraining phase, a Fourier Neural Operator (FNO) is trained on large datasets to learn the mapping from input geometries and material properties to displacement fields. In the fine-tuning phase, the pretrained predictions serve as initial solutions for iterative algorithms, drastically reducing the number of iterations needed for convergence. The pretraining phase of PreFine-Homo delivers homogenization results up to 1000 times faster than conventional methods, while the fine-tuning phase further enhances accuracy. Moreover, the fine-tuning phase grants PreFine-Homo unlimited generalization capabilities, enabling continuous learning and improvement as data availability increases. We validate PreFine-Homo by predicting the effective elastic tensor for 3D periodic materials, specifically Triply Periodic Minimal Surfaces (TPMS). The results demonstrate that PreFine-Homo achieves high precision, exceptional efficiency, robust learning capabilities, and strong extrapolation ability, establishing it as a powerful tool for multiscale homogenization tasks.

cs.CE↗