arXiv subjects

Tianmin Wu

Publications and source records attributed to Tianmin Wu.

4 recordsLinked to original sources

arXiv2026

PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space

Unlocking the full physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and optimizing astrophysical parameters in high-dimensional space, as a consequence of line blending. Here we present PhDLspec -- a deep learning framework embedded with physical priors for stellar spectra modeling and analysis. By imposing differential spectra derived from ab initio stellar atmospheric model calculation on a transformer framework, PhDLspec can rigorously and precisely model stellar spectra by simultaneously taking into account more than 30 physical parameters, at a computational speed hundreds of times faster than ab initio model calculation. With such a flexible stellar modeling approach, PhDLspec can effectively derive ~30 stellar labels from a low-resolution spectrum using affordable optimization techniques. Application to LAMOST spectra (R~1800) yields stellar elemental abundances in good agreement with high-resolution spectroscopic surveys, following essential calibrations to correct systematic biases in elemental abundance estimates using wide binaries and reference high-resolution datasets. We provide a catalog of 25 elemental abundances for 116,611 subgiant stars with precise age estimates. The successful application of PhDLspec to LAMOST spectra for high-dimensional parameter determination sheds light on similar challenges faced by other surveys and disciplines.

astro-ph.GA↗

arXiv2025

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne

A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars released in LAMOST DR9, and abundances for 22 elements, namely, C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Ni, Sr, Y, Zr, Ba, La, Ce, Nd, Sm, and Eu, for nearly 3.6 million stars with spectral signal-to-noise ratio (SNR) higher than 20. The [Fe/H] is valid down to $\sim$-4.0, while elemental abundance ratios [X/Fe] are mostly valid for stars with [Fe/H] $\gtrsim-2.0$. Measurement errors in these labels are sensitive to and almost inversely proportional with SNR. For stars with S/N>50, we achieved a typical error of 30 K in Teff, 0.07 dex in $\log g$, $\sim0.05$ dex in abundances for most elements with atomic number smaller than Sr, and 0.1--0.2 dex for heavier elements. Homogenization to the label estimates is carried out via dedicated internal and external calibration. In particular, the non-local thermal equilibrium effect is corrected for the [Fe/H] estimates, the Teff is calibrated to the infrared flux method scale, and the $\log~g$ is validated with asteroseismic measurements. The elemental abundances are internally calibrated using wide binaries, eliminating systematic trend with effective temperature. The catalog is publicly available.

astro-ph.SR↗

arXiv2024

Determining Stellar Elemental Abundances from DESI Spectra with the Data-Driven Payne

Stellar abundances for a large number of stars are key information for the study of Galactic formation history. Large spectroscopic surveys such as DESI and LAMOST take median-to-low resolution ($R\lesssim5000$) spectra in the full optical wavelength range for millions of stars. However, line blending effect in these spectra causes great challenges for the elemental abundances determination. Here we employ the DD-PAYNE, a data-driven method regularised by differential spectra from stellar physical models, to the DESI EDR spectra for stellar abundance determination. Our implementation delivers 15 labels, including effective temperature $T_{\rm eff}$, surface gravity $\log g$, microturbulence velocity $v_{\rm mic}$, and abundances for 12 individual elements, namely C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Ni. Given a spectral signal-to-noise ratio of 100 per pixel, internal precision of the label estimates are about 20 K for $T_{\rm eff}$, 0.05 dex for $\log~g$, and 0.05 dex for most elemental abundances. These results are agree with theoretical limits from the Crámer-Rao bound calculation within a factor of two. The Gaia-Enceladus-Sausage that contributes the majority of the accreted halo stars are discernible from the disk and in-situ halo populations in the resultant [Mg/Fe]-[Fe/H] and [Al/Fe]-[Fe/H] abundance spaces. We also provide distance and orbital parameters for the sample stars, which spread a distance out to $\sim$100 kpc. The DESI sample has a significant higher fraction of distant (or metal-poor) stars than other existed spectroscopic surveys, making it a powerful data set to study the Galactic outskirts. The catalog is publicly available.

astro-ph.GA↗

arXiv2023

Estimating Stellar Parameters and Identifying Very Metal-poor Stars Using Convolutional Neural Networks for Low-resolution Spectra (R~200)

Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Space Station Telescope (CSST) will provide us with a large amount of spectroscopic data that may contain plenty of VMP stars, and thus it is crucial to determine the stellar atmospheric parameters ($T_{eff}$, $\log g$, and [Fe/H]) for low-resolution spectra similar to the CSST spectra (R~200). In this paper, a two-dimensional Convolutional Neural Network (CNN) model with three convolutional layers and two fully connected layers is constructed. The principal aim of this work is to measure the ability of this model to estimate stellar parameters on low-resolution (R~200) spectra and to identify VMP stars so that we can better search for VMP stars in the spectra observed by CSST.We mainly use 10,008 observed spectra of VMP stars from LAMOST DR3, and 16,638 spectra of common stars ([Fe/H]>-2.0) from LAMOST DR8 for the experiment and make comparisons. All spectra are reduced to R~200 to match the resolution of the CSST and are preprocessed and collapsed into two-dimensional spectra for input to the CNN model. The results show that the MAE values are 99.40 K for $T_{eff}$, 0.22 dex for $\log g$, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe], respectively. Besides, the CNN model efficiently identifies VMP stars with a precision of 94.77%. The validation and practicality of this model are also tested on the MARCS synthetic spectra. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra (R~200) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.

astro-ph.SR↗