Ming Yang

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and >10 ⁵ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose a hierarchical, two-stage deep learning model for adaptive data reduction. The first stage (“classification”) employs a ResNet-34 model to classify images into six discrete density categories, achieving 98.83% in global accuracy. This classification acts as a critical decision gate, ensuring high calibration accuracy in the crowded fields. In the second stage (“regression”), a ResNet-50 regression model predicts the bright stars (<23.5 mag) in the field, which is essential for astrometric calibration, achieving a mean absolute error of 0.0824 dex. By decoupling density characterization from source extraction, our model ensures that photometric and astrometric algorithms are optimally matched to the stellar density environment, thereby enhancing the fidelity and homogeneity of CSST as well as future large sky survey data products.

The availability of large data sets containing stellar parameters, distances, and extinctions for stars in the Milky Way, particularly within the Galactic disk, is essential for advancing our understanding of the Galaxy’s stellar populations, structure, kinematics, and chemical evolution. In this study, we present a catalog of stellar parameters, including effective temperature (T _eff), metallicity ([Fe/H]), absolute magnitudes (M _G ), distances (d), and reddening values (E(G _BP − G _RP)), for a sample of 141 million stars from the SkyMapper Southern Survey. These parameters are derived using the SPar algorithm, which employs a fitting procedure to match multi-band photometric observations and estimate the stellar properties (T _eff, [Fe/H], M _G , d, and E(G _BP − G _RP)) on an individual star basis, following the methodology outlined in our previous work. This study successfully determines stellar parameters and extinction values simultaneously for stars located in high and low Galactic latitudes. The resulting stellar parameters and extinction values are in good agreement with results from other studies, demonstrating the robustness of our method. We derive a temperature dispersion of 195 K and a metallicity dispersion of 0.31 dex when comparing our results with spectroscopic data. The catalog produced in this work provides a valuable resource for future research on the Galactic metallicity distribution function, the structure of the Galaxy, three-dimensional extinction mapping, and other related astrophysical topics.

Low-surface-density streams are important tracers to study the formation and evolution of the Milky Way. Using the accurate astrometric measurements from Gaia mission, we discover a low-surface-density stream in the northern hemisphere, with a length of ∼110 degree and width of 1.23 kpc. The vertical velocity dispersion perpendicular to the stream is around 22.4 km s⁻¹. The spectral data of member candidate stars from LAMOST and DESI shows a large metallicity range from −1.8 to −0.7. Based on those properties, we claim that the stream originated from a dwarf galaxy. The median metallicity of [Fe/H] = −1.3 indicates a massive dwarf galaxy origination with a stellar mass of around 2.0 × 10⁷ M_⊙, which is comparable with the Fornax dwarf galaxy and smaller than Large and Small Magellanic Clouds (LMC and SMC) and Sagittarius. We also find the globular cluster Pyxis is highly associated with the stream in the phase space E − L_Z and in its metallicity. The massive progenitor also suggests that many dwarf galaxies, including massive ones, have been disrupted during their evolution orbiting the Milky Way and left with very low-surface-density structures. This is important for our understanding of the ‘missing satellites’ problem. The orbit information of the stream shows a tight association between its progenitor and the Vast POlar Structure (VPOS), which indicates that the satellites fell into the Milky Way in groups, bringing many globular clusters into the Milky Way.

Red supergiants (RSGs) are essential to understanding the evolution and the contribution to the interstellar medium of massive stars. However, the number of identified RSGs within the Milky Way is still limited, mainly due to the difficulty of measuring stellar extinction and distance. The release of approximately one million RVS spectra in Gaia DR3 presents a new opportunity for identifying Galactic RSGs because the equivalent width of the calcium triplet lines (EW(CaT)) in the spectra is an excellent indicator of stellar surface gravity. We used RVS spectra with a signal-to-noise ratio (S/N) greater than 100 to search for Galactic RSGs. We removed dwarf stars and red giants and selected RSG candidates based on their location in the EW(CaT) versus BP − RP diagram. Early-type RSG candidates (K0-M2) were then identified using the criteria BP − RP > 1.584 and EW(CaT) > 1.1 nm. The criteria of the average equivalent widths of TiO in the XP spectra (EW(TiO)) > 10 nm, the color index K − W3 < 0.5, and the period-amplitude sequence from the Gaia DR3 LPV catalog were further applied to remove late-type red giants (after M2) and asymptotic giant branch stars. This method yielded 30 early-type (K0-M2) and 6196 late-type (after M2) RSG candidates, which is a significant increase compared to the previous Galactic RSG sample. The application of this approach to spectra with S/N > 50 resulted in 48 early-type and 11 491 late-type RSG candidates. This preliminary analysis paves the way for more extensive research with Gaia DR4, when larger spectral datasets are expected to significantly enhance our understanding of Galactic RSG populations.

Reproducing color–magnitude diagrams (CMDs) of star-resolved galaxies is one of the most precise methods for measuring the star formation history (SFH) of nearby galaxies back to the earliest time. The upcoming big data era poses challenges to the traditional numerical technique in its capacity to deal with vast amounts of data, which motivates us to explore the feasibility of employing machine learning networks in this field. In this study, we refine the synthetic CMD method with a state-of-the-art theoretical stellar evolution model to simulate the properties of stellar populations, incorporate the convolutional neural network in the fitting process to enhance the efficiency, and innovate the initial stellar mass estimation to improve the flexibility. The fine-tuned deep learning network, named SFHNet, has been tested with synthetic data and further validated with photometric data collected from the Hubble Space Telescope. The derived SFHs are largely in accordance with those reported in the literature. Furthermore, the network provides detailed insights into the distribution of stellar density, initial stellar mass, and star formation rate over the age–metallicity map. The application of the deep learning network not only measures the SFH accurately but also enhances the synthetic CMD method’s efficiency and flexibility, thereby facilitating a more comprehensive and in-depth understanding of nearby galaxies.

Reproducing color-magnitude diagrams (CMDs) of star-resolved galaxies is one of the most precise methods for measuring the star formation history (SFH) of nearby galaxies back to the earliest time. The upcoming big data era poses challenges to the traditional numerical technique in its capacity to deal with vast amounts of data, which motivates us to explore the feasibility of employing machine learning networks in this field. In this study, we refine the synthetic CMD method with a state-of-the-art theoretical stellar evolution model to simulate the properties of stellar populations, incorporate the convolutional neural network (CNN) in the fitting process to enhance the efficiency, and innovate the initial stellar mass estimation to improve the flexibility. The fine-tuned deep learning network, named \texttt{SFHNet}, has been tested with synthetic data and further validated with photometric data collected from the Hubble Space Telescope (\textit{HST}). The derived SFHs are largely in accordance with those reported in the literature. Furthermore, the network provides detailed insights into the distribution of stellar density, initial stellar mass, and star formation rate (SFR) over the age-metallicity map. The application of the deep learning network not only measures the SFH accurately but also enhances the synthetic CMD method's efficiency and flexibility, thereby facilitating a more comprehensive and in-depth understanding of nearby galaxies.