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The Chinese Space Station Telescope (CSST) is scheduled to launch soon, which is expected to provide a vast amount of image potentially containing low-surface brightness galaxies (LSBGs). However, detecting and characterizing LSBGs is known to be challenging due to their faint surface brightness, posing a significant hurdle for traditional detection methods. In this paper, we propose LSBGnet, a deep neural network specifically designed for automatic detection of LSBGs. We established LSBGnet-SDSS model using data set from the Sloan Digital Sky Survey (SDSS). The results demonstrate a significant improvement compared to our previous work, achieving a recall of 97.22 per cent and a precision of 97.27 per cent on the SDSS test set. Furthermore, we use the LSBGnet-SDSS model as a pre-training model, employing transfer learning to retrain the model with LSBGs from Dark Energy Survey (DES), and establish the LSBGnet-DES model. Remarkably, after retraining the model on a small DES sample, it achieves over 90 per cent precision and recall. To validate the model’s capabilities, we utilize the trained LSBGnet-DES model to detect LSBG candidates within a selected 5 sq. deg area in the DES footprint. Our analysis reveals the detection of 204 LSBG candidates, characterized by a mean surface brightness range of $23.5\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}\le \bar{\mu }_{\text{eff}}(g)\le 26.8\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}$ and a half-light radius range of 1.4 arcsec ≤ r1/2 ≤ 8.3 arcsec. Notably, 116 LSBG candidates exhibit a half-light radius ≥2.5 arcsec. These results affirm the remarkable performance of our model in detecting LSBGs, making it a promising tool for the upcoming CSST.

 

Ceratozamia Brongn. is one of the species-rich genera of Cycadales comprising 38 species that are mainly distributed in Mexico, with a few species reported from neighboring regions. Phylogenetic relationships within the genus need detailed investigation based on extensive datasets and reliable systematic approaches. Therefore, we used 30 of the known 38 species to reconstruct the phylogeny based on transcriptome data of 3954 single-copy nuclear genes (SCGs) via coalescent and concatenated approaches and three comparative datasets (nt/nt12/aa). Based on all these methods, Ceratozamia is divided into six phylogenetic subclades within three major clades. There were a few discrepancies regarding phylogenetic position of some species within these subclades. Using these phylogenetic trees, biogeographic history and morphological diversity of the genus are explored. Ceratozamia originated from ancestors in southern Mexico since the mid-Miocene. There is a distinct distribution pattern of species through the Trans-Mexican Volcanic Belt (TMVB), that act as a barrier for the species dispersal at TMVB and its southern and northern part. Limited dispersal events occurred during the late Miocene, and maximum diversification happened during the Pliocene epoch. Our study provides a new insight into phylogenetic relationships, the origin and dispersal routes, and morphological diversity of the genus Ceratozamia. We also explain how past climatic changes affected the diversification of this Mesoamerica-native genus. 

Abstract Background and Aims Cycads are regarded as an ancient lineage of living seed plants, and hold important clues to understand the early evolutionary trends of seed plants. The molecular phylogeny and spatio-temporal diversification of one of the species-rich genera of cycads, Macrozamia, have not been well reconstructed. Methods We analysed a transcriptome dataset of 4740 single-copy nuclear genes (SCGs) of 39 Macrozamia species and two outgroup taxa. Based on concatenated (maximum parsimony, maximum likelihood) and multispecies coalescent analyses, we first establish a well-resolved phylogenetic tree of Macrozamia. To identify cyto-nuclear incongruence, the plastid protein coding genes (PCGs) from transcriptome data are extracted using the software HybPiper. Furthermore, we explore the biogeographical history of the genus and shed light on the pattern of floristic exchange between three distinct areas of Australia. Six key diagnostic characters are traced on the phylogenetic framework using two comparative methods, and infra-generic classification is investigated. Key Results The tree topologies of concatenated and multi-species coalescent analyses of SCGs are mostly congruent with a few conflicting nodes, while those from plastid PCGs show poorly supported relationships. The genus contains three major clades that correspond to their distinct distributional areas in Australia. The crown group of Macrozamia is estimated to around 11.80 Ma, with a major expansion in the last 5–6 Myr. Six morphological characters show homoplasy, and the traditional phenetic sectional division of the genus is inconsistent with this current phylogeny. Conclusions This first detailed phylogenetic investigation of Macrozamia demonstrates promising prospects of SCGs in resolving phylogenetic relationships within cycads. Our study suggests that Macrozamia, once widely distributed in Australia, underwent major extinctions because of fluctuating climatic conditions such as cooling and mesic biome disappearance in the past. The current close placement of morphologically distinct species in the phylogenetic tree may be related to neotenic events that occurred in the genus. 

Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $fracDev\_r$ < 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next-generation observatories.