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.

Deep clustering aims to discover meaningful data groups by jointly learning representations and cluster probability distributions. Yet existing methods rarely consider the underlying information characteristics of these distributions, causing ambiguity and redundancy in cluster assignments, particularly when different augmented views are used. To address this issue, this paper proposes a novel information-principled deep clustering framework for learning invariant, redundancy-reduced, and discriminative cluster probability distributions, termed ICIRD. Specifically, ICIRD is built upon three complementary modules for cluster probability distributions: (i) conditional entropy minimization, which increases assignment certainty and discriminability; (ii) inter-cluster mutual information minimization, which reduces redundancy among cluster distributions and sharpens separability; and (iii) cross-view mutual information maximization, which enforces semantic consistency across augmented views. Additionally, a contrastive representation mechanism is incorporated to provide stable and reliable feature inputs for the cluster probability distributions. Together, these components enable ICIRD to jointly optimize both representations and cluster probability distributions in an information-regularized manner. Extensive experiments on five image benchmark datasets demonstrate that ICIRD outperforms most existing deep clustering methods, particularly on fine-grained datasets such as CIFAR-100 and ImageNet-Dogs.