Zhenping Yi

Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galaxy images, making traditional manual classification impractical. Although supervised deep learning can achieve high accuracy, it requires large labeled datasets that are time-consuming to construct. In contrast, unsupervised methods often show limited classification performance. To address this limitation, we propose a label-efficient self-supervised learning framework for galaxy morphology classification. Our method first learns robust morphological representations from 305,583 unlabeled KiDS galaxy images through contrastive learning, and then trains a classifier using only 5000 human-labeled images. The classifier separates galaxies into five categories: elliptical, spiral, lenticular-disk, irregular, and “other.” Using a ResNet-50 model with a crop size of 64 × 64 pixels, our approach achieves an overall test accuracy of up to 91.0% (90.5% ± 0.2% on average) on the human-classified catalog. The corresponding F1 scores for elliptical, spiral, irregular, lenticular-disk, and “other” galaxies are 0.96, 0.86, 0.86, 0.95, and 0.92, respectively. We apply this pipeline to the Kilo-Degree Survey Data Release 5 and produce a publicly available morphology catalog of 310,583 galaxies. This is the first morphology catalog for KiDS galaxies and provides a valuable resource for future studies of galaxy evolution. Our results show that self-supervised learning can substantially reduce the need for manual labels while maintaining high classification accuracy, making it a promising and scalable approach for automated galaxy morphology classification in the era of large-scale surveys.

Low-surface-brightness galaxies (LSBGs) play an important role in studies of galaxy formation and evolution, yet accurate measurements of their structural and photometric parameters remain challenging for conventional analysis pipelines due to their diffuse light distributions and low signal-to-noise ratios. In this work, we present an automated deep learning framework, LSBGPENet , for the robust estimation of structural and photometric parameters of LSBGs from wide-field imaging data. The framework directly operates on galaxy image cutouts and simultaneously infers key parameters, including total magnitude ( m ), effective radius ( R _eff ), ellipticity ( ϵ ), and Sérsic index ( n ), together with associated uncertainty estimates. Mean and central surface brightnesses ( μ _eff , μ ₀ ) are subsequently derived from the inferred parameters. We assess the accuracy and reliability of the inferred parameters through comparisons with traditional profile-fitting measurements. On both simulated data and observational data from the Dark Energy Survey, the framework achieves high predictive accuracy, with mean coefficients of determination of 0.91 and 0.94, respectively, and well-calibrated uncertainty estimates, characterized by mean uncertainty calibration errors of 0.004 and 0.009. The inferred parameters are statistically consistent with those obtained from GALFIT . The proposed framework provides a scalable and reproducible solution for structural and photometric parameter estimation of LSBGs and is well suited for application to current and forthcoming wide-field surveys, including the China Space Station Telescope.

We present a homogeneous catalog of stellar atmospheric parameters and chemical abundances for 20,032 members across 800 open clusters, derived from Gaia DR3 XP sampled spectra. To extract detailed chemical information from low-resolution spectrophotometry, we developed a deep learning framework incorporating a novel boosting multihead attention mechanism. This architecture iteratively reweights attention heads to enhance sensitivity to subtle spectral features often masked by noise. We constructed a robust training set using APOGEE DR17 labels, employing a histogram distribution matching strategy to strictly align the training sample with the target cluster population in the color–magnitude diagram. This approach mitigates domain shift and ensures reliable inference for red giant branch, red clump, and lower main-sequence stars. The final catalog provides estimates of T _eff , log g , [M/H], and individual abundance ratios for [ α /M], C, N, O, Mg, Si, Ca, and Ti, along with epistemic uncertainties. Validation against independent datasets demonstrates high precision, with typical mean absolute errors of ∼37 K for T _eff , ∼0.10 dex for log g , and ∼0.05 dex for [M/H]. This work highlights the potential of physics-aware deep learning in the Gaia era and offers a valuable resource for Galactic archeology.

Context . Hot subdwarf binaries provide stringent constraints on envelope stripping and binary mass transfer. However, robust identification of these systems in large spectroscopic surveys is hindered by variable data quality and the subtle signatures of their companions. Aims . We aim to develop a robust automated framework for identifying binaries comprising a hot subdwarf and a main-sequence star within the diverse datasets of the LAMOST low-resolution survey, utilizing deep learning to overcome challenges posed by complex noise and class imbalance. Methods . We propose a Bayesian hybrid model that combines convolutional neural networks for local feature extraction with a Bayesian transformer encoder to model long-range dependencies. This architecture incorporates variational inference to improve classification accuracy and facilitate robust candidate identification. Results . The framework attains an accuracy of 95.3% on the test set, with a precision of 97.2% for the binary class. Applying the model to the LAMOST dataset yields 1161 binary candidates. Further certification via spectral energy distribution fitting confirms 968 binaries among the 1001 objects with reliable fits. Multi-epoch radial velocity measurements of 352 candidates identified 119 systems with significant variability. Conclusions . A subset of the candidates show no significant radial velocity changes over baselines exceeding 1000 days despite exhibiting infrared excess, consistent with the behavior of long-period binaries. The proposed Bayesian framework effectively quantifies predictive uncertainty to filter out data artifacts, yielding a sample suitable for constraining evolutionary pathways such as common envelope ejection and stable Roche lobe overflow.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has collected tens of millions of spectra, providing an unprecedented resource for large-scale spectroscopic studies. Efficient retrieval techniques are therefore essential for exploring such massive datasets. Existing approaches often rely on predefined templates or manually labeled training samples, which can limit their applicability in large and diverse spectral archives. In this work, we present a general similarity-retrieval framework that combines self-supervised contrastive learning based on a convolutional neural network with Facebook AI Similarity Search (FAISS) for efficient large-scale spectral retrieval. The framework learns spectral representations directly from unlabeled data and enables flexible retrieval from user-defined wavelength regions based on feature similarity. We evaluate the framework on several stellar populations in LAMOST DR8. For late-type M8-star retrieval, 90.5% of the top 1000 retrieved spectra are later than M6. For M0–M5 giants, the mean retrieval accuracy across six subtypes reaches 94.8%. Using a C-H star spectrum as the query spectrum, 90.8% of the top 1000 retrieved candidates are classified as carbon stars by the LAMOST pipeline. Cross-matching with SIMBAD further confirms 255 C-H stars and 47 C-R stars among the retrieved candidates. These results demonstrate that the proposed framework can efficiently identify spectrally similar objects across large spectroscopic databases and can serve as a useful tool for searching for rare or spectrally distinctive stellar populations.

Hot subdwarf stars are important tracers of stellar structure and evolution, while their binary systems provide key constraints on their formation channels. Nevertheless, the number of confirmed hot subdwarf binaries is still limited. We aim to identify hot subdwarf binaries efficiently and reliably in large candidate samples by combining heterogeneous observational information. We developed a two-stage classification framework. A Bayesian neural network was first used to perform preliminary classification and uncertainty quantification from Gaia photometric and astrometric data. We then constructed a multimodal deep learning model, HsdB-FusionNet, which combines Gaia photometric and astrometric features with SDSS spectra through a cross-attention mechanism. We applied this framework to a catalog of 61,585 hot subdwarf candidates. The resulting candidates were further examined through spectral energy distribution fitting, and atmospheric parameters were derived for high-quality systems. We identified 1,369 hot subdwarf binary candidates from the parent sample. Among them, 1,057 objects were strongly supported as binaries by spectral energy distribution fitting. Atmospheric parameters were obtained for 579 high-quality binary systems. The interpretability analysis further indicates that the model decisions are consistent with known physical priors. This work provides a substantially enlarged sample of hot subdwarf binary candidates and demonstrates the potential of multimodal deep learning for binary identification. The released catalog offers a useful basis for future studies on the formation and evolution of hot subdwarf binaries.

Ultra-diffuse galaxies (UDGs) are a subset of low-surface-brightness galaxies (LSBGs), showing mean effective surface brightness fainter than 24 mag arcsec ⁻² and a diffuse morphology, with effective radii larger than 1.5 kpc. Due to their elusiveness, using traditional methods over large sky areas is challenging. Here we present a catalog of UDG candidates identified in the full 1350 deg ² area of the Kilo-Degree Survey (KiDS) using deep learning. In particular, we used a previously developed network for the detection of low-surface-brightness systems in the Sloan Digital Sky Survey (SBGnet) and optimized for UDG detection. We trained this new UDG detection network for KiDS (UDGnet-K), with an iterative approach, starting from a small-scale training sample. After training and validation, the UGDnet-K has been able to identify ∼3300 UDG candidates, of which, after visual inspection, we selected 545 high-quality ones. The catalog contains the independent rediscovery of previously confirmed UDGs in local groups and clusters (e.g., NGC 5846 and Fornax), and new discovered candidates in about 15 local systems, for a total of 67 bona fide associations. Besides the value of the catalog per se for future studies of UDG properties, this work shows the effectiveness of an iterative approach to training deep learning tools in presence of poor training samples, due to the paucity of confirmed UDG examples, which we expect to replicate for upcoming all-sky surveys such as those by the Rubin Observatory, Euclid, and the China Space Station Telescope.

Ultra-diffuse galaxies (UDGs) are a class of galaxies characterized by an extremely low surface brightness and large effective radius. The significance of UDGs lies in their unique properties, such as their diverse dark-matter content, which collectively challenge existing theories of galaxy formation and evolution. However, their low surface brightness and diffuse stellar distributions make UDGs particularly difficult to detect. To address this challenge, this study introduces a deep learning-based object detection model for the Dark Energy Survey (DES), named UDGnet-DES. Combined with an iterative training strategy, this model is designed to conduct a large-scale search for UDG candidates in the DES Data Release 2 (DES DR2) imaging data. Using our model, we searched UDGs from more than 500 000 DES images and further filtered the results based on surface brightness and visual inspection. As a result, we obtained a catalog of 2991 UDG samples, 39 of which have spectroscopic redshifts. An analysis of our UDG samples reveals that most objects exhibit nearly circular morphologies. Among them, blue UDGs tend to have higher surface brightnesses, while red UDGs show significant spatial clustering, in contrast to the more uniformly distributed blue UDGs. The method developed in this study can improve the efficiency of UDG searches and will be applied to the search for UDGs in the China Space Station Telescope (CSST) survey project.

Metal-poor stars (MP, [Fe/H]<−1.0) retain the chemical signatures of the early Universe, making their α-element invaluable for tracing the Galactic chemical evolution. Traditional optical spectroscopic methods and machine learning approaches often struggle at low metallicities and neglect the diagnostic power of ultraviolet (UV) spectral features (e.g., OH bands, Mg II/Mg I lines) that are accessible to the forthcoming Chinese Space Station Telescope (CSST). Using over 1.8 × 10 ⁵ simulated CSST spectra, we developed the spectral transformer (SPT)-GloCal model to quantify the impact of UV (2550−4000 Å) low-resolution ( R ≈ 200) spectra on the precision of [O/Fe] and [Mg/Fe] estimates in MP stars. The model improves local attention through score-aware competitive filtering and integrates it with global attention via a learnable gating mechanism. We compared models trained on full spectra versus optical-only spectra. Incorporating UV spectra halved the mean absolute error (MAE) of [O/Fe] predictions from 0.0785 to 0.0367 dex and reduced the scatter ( σ ) from 0.135 to 0.063 dex. For [Mg/Fe], MAE decreased from 0.0010 to 0.0006 dex and σ from 0.054 to 0.0068 dex. Error analyses demonstrated that UV data lead to more stable and accurate estimates, especially at a high log g and low Teff. SPT-GloCal outperforms both its predecessor (SPT) and tree-based regressors, confirming the effectiveness of its global-local attention design. Furthermore, tests on spectra with simulated noise show that the model maintains consistent performance across a range of signal-to-noise ratios (S/N =30−50), with marginal gains over the SPT model at the lower S/N end. UV spectral features, even at low resolution ( R ≈ 200), enhance α-element abundance determinations. The SPT-GloCal framework provides a scalable solution for upcoming space-based surveys. Future work will apply this model to real CSST data, thus extending it to other elements.

White dwarfs represent the end stage for 97% of stars, making precise parameter measurement crucial for understanding stellar evolution. Traditional estimation methods involve fitting spectra or photometry, which require high-quality data. In recent years, machine learning has played a crucial role in processing spectral data due to its speed, automation, and accuracy. However, two common issues have been identified. First, most studies rely on data with high signal-to-noise ratios (S/N > 10), leaving many poor-quality datasets underutilized. Second, existing machine learning models, primarily based on convolutional and recurrent networks and their variants, fail to simultaneously capture both local spatial and long-range sequential spectral information. To address these challenges, we designed the estimator network (EstNet), an advanced algorithm integrating multiple techniques, including residual networks, squeeze and excitation attention, gated recurrent units, adaptive loss, and Monte Carlo dropout. A total of 5965 poor-quality white dwarf spectra (R~1800, S/N~1.17) were used for the experiments. EstNet achieved average percentage errors of 13.81% for effective temperature and 3.92% for surface gravity on the test set. These results are superior to other mainstream algorithms and consistent with the outcomes of traditional theoretical spectrum fitting methods. In the future, our algorithm will be applied to large-scale parameter estimations on data from the China Space Station Survey Telescope and the Dark Energy Spectroscopic Instrument.