Large samples of Am and Ap stars are helpful in studying the interplay between phenomena like atomic diffusion, magnetic fields, and stellar rotation in stellar astrophysics. Existing samples of Am and Ap stars, mostly obtained from spectral data with a signal-to-noise ratio in the g band (S/Ng) greater than 50, can benefit from expansion by exploring spectra with lower S/Ng. Therefore, this paper proposes an ensemble regression model applicable to spectra with a minimum S/Ng of 30. Using the model, we identify 21,361 Am candidates, of which 11,614 are new, and 6182 Ap candidates, of which 4978 are new, from LAMOST DR10. The Am sample size has increased by 60% and the Ap sample size has increased by 180% compared to the previous sample. In terms of effective temperature (T _eff), the Am candidates range mainly from 6000 to 8500 K, while the Ap candidates range from 6000 to 11,700 K. The surface gravity ( log g ) distributions for Am and Ap candidates differ in the range of 3.25–4.75 dex. The number of Am candidates increases stepwise, in contrast to the relatively uniform distribution of Ap candidates across the entire surface gravity range. Regarding metallicity ([Fe/H]), Am candidates typically range from −0.75 to 0.38 dex, peaking near 0 dex, while Ap candidates are distributed from −1.38 to 0.38 dex, with a peak near −0.5 dex.

Galaxy pairs hold significant importance in understanding the evolution of galaxies, and the extensive search for a large sample of galaxy pairs is meaningful. In this article, we develop a deep learning-based approach for the search of galaxy pairs and conduct a comprehensive search on Sloan Digital Sky Survey (SDSS) images. In nine million photometric images, 17,965 physical galaxy pairs with spectral or photometric redshifts are detected. Four sets of results are provided, including physical pairs determined by two spectral redshifts, two photometric redshifts, one spectral redshift, and one photometric redshift, and visual irregular pairs that have no precise redshift information but can be inferred as physical galaxy pairs based on the morphological changes. Then their morphological and physical characteristics are explored, the redshifts of most targets are around 0.1, and as the redshift difference between two galaxies increases, the number of galaxy pairs gradually reduces. The distributions of star formation rate (SFR) are not the same for different morphologies of galaxy pairs, irregular pairs have higher SFR than the other three types, and statistics indicate that the SFR of galaxies depends on both nearby galaxies and internal properties. Color and stellar mass are also key properties of galaxies which can reflect the status of galaxy pairs. Compared to other surveys, a greater number of galaxy pair targets are detected, and this is also the first extensive detection of galaxy pairs in SDSS images using photometric redshifts. These galaxy pair samples can greatly aid in the study of galaxy evolution.

Prototypes help to explain the predictions of deep classification models for time series. However, most models learn prototypes by randomly initializing an uncertain number of low-discriminative prototypes, which may lead to unstable models and unreliable results. To address these issues, we propose a new class D iscriminative P rototype L earning Net work (DPL-Net), which learns an appropriate number of class-discriminative prototypes, thus improving classification performance. Specifically, the proposed P rototype I nitialization M echanism (PIM) introduces a new proximity metric based on the silhouette coefficient and statistical metrics. It facilitates the automatic determination of the class-discriminative prototypes for each class. Then, the encoder layer encodes the prototypes derived from PIM and the input series using one-dimensional convolutional neural networks (1D-CNN). Finally, the prototype classification layer optimizes the prototypes according to the regularization terms, while simultaneously classifying the input sequence based on its similarity to the updated prototypes. The comparison experiments are conducted on 26 UCR datasets compared with 10 baselines. The results show that our proposed approach achieves the best accuracy on 11 datasets. Specifically, our method outperforms PIP, CSSL, and LSS by an average of 16.33%, 9.77% and 5.96% on 22, 14 and 16 datasets, respectively. The interpretability experimental results and the application analysis on spectral data indicate that the learned prototypes can provide reasonable explanations for the classification results of the model.

The cluster number can directly affect the clustering effect and its application in real-world scenarios. Its determination is one of the key issues in cluster analysis. According to singular value decomposition (SVD), the characteristic directions of larger singular values likely represent the primary data patterns, trends, or structures corresponding to the main information. In clustering analysis, the main information and structure are likely related to the cluster structure itself. The number of larger singular values may correspond to the number of clusters, and their main information may correspond to different clusters. Based on this, a singular-value-based cluster number detection method is proposed. First, the transferred K-nearest neighbors (TKNN) density formula is proposed to address the limitation of the DPC algorithm in failing to identify centroids in sparse clusters of unbalanced datasets. Second, core data are selected by the DPC algorithm with a modified density formula to better capture the data distribution. Third, based on the selected core data, a sparse similarity matrix is constructed to further highlight the relationships between data and enhance the distribution of data features. Finally, SVD is performed on the sparse similarity matrix to obtain singular values, the cumulative contribution rate is introduced to determine the number of relatively large singular values (i.e., the cluster number). Experimental results show that our method is superior in determining the cluster number for datasets with complex shapes.

Carbon stars are critical for understanding stellar evolution and exploring cosmic chemistry. This paper provides 7809 carbon star candidates identified from the Large-Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) DR11 and Sloan Digital Sky Survey (SDSS) DR18, using a parallel carbon star feature recognition method. This method deploys a parallel multi-interval feature representation model and a k-nearest neighbor classification model, effectively characterizing local and global molecular bands of carbon stars and enhancing feature discrimination, especially for weak features that are highly susceptible to noise interference. The recognition performance F1-score can exceed 96%. Moreover, with parallel strategies for interval representation, the search efficiency has improved. And we construct a new catalog of carbon stars subtypes, including 1616 barium (Ba) stars, 1720 C-H stars, 1621 C-N stars, 1205 C-R stars, and 1647 stars labeled as “Unknown” due to their low signal-to-noise ratios (S/Ns). Compared with the LAMOST DR11 pipeline, carbon stars by L. Li et al., and the SDSS DR18 pipeline, we identify 1403 and 1644 new candidates from LAMOST, and 661 new candidates from SDSS DR18, respectively. In our catalog, the majority of C-N, C-R, and Ba stars from LAMOST are located at low Galactic latitudes with b < = 30 °, while most candidates from SDSS are located at high Galactic latitudes. And the vast majority of Ba, C-H, C-R, and Unknown stars show relatively high effective temperatures. Some carbon stars exhibit stellar activity, potentially influenced by dense interstellar materials, internal magnetic fields, and rotation effects. In the g, r, and i bands, C-N and Unknown stars from LAMOST exhibit lower S/Ns, influenced by high interstellar density at low Galactic latitudes, while Unknown stars from SDSS show higher S/Ns. Among the newly discovered carbon stars, Ba from LAMOST, Unknown from SDSS, and C-R stars have higher S/Ns than other subclasses.