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Stellar classification is a central topic in astronomical research that relies mostly on the use of spectra. However, with the development of large sky surveys, spectra are becoming increasingly scarce compared to photometric images. Numerous observed stars lack spectral types. In Sloan Digital Sky Survey (SDSS), there are more than hundreds of millions of such stars. In this paper, we propose a convolutional neural network-based stellar classification network (SCNet) in an attempt to solve the stellar classification task from photometric images alone, distinguishing between seven classes, i.e. O, B, A, F, G, K, and M. A total of 46 245 identified stellar objects were collected from the SDSS as the training samples for our network. Compared to many typical classification networks in deep learning, SCNet achieves the best classification accuracy of 0.861. When we allow an error to be within three neighbouring subtypes for SCNet, the accuracy even reaches 0.907. We apply the final SCNet model to 50 245 638 SDSS stars without corresponding spectra and present a new star classification catalogue, containing 7438 O-type stars, 31 433 B-type stars, 201 189 A-type stars, 910 007 F-type stars, 10 986 055 G-type stars, 18 941 155 K-type stars, and 19 168 361 M-type stars.

The Wide-Field Infrared Explorer (WISE) survey captured massive amounts of infrared astronomical object data, and different astronomical objects will show different characteristics. Especially spiral galaxies that are richer in colour than other astronomical objects. In addition, the Sloan Digital Sky Survey (SDSS) has obtained a large number of visible light images and their parameter. So this paper mainly explores the colour characteristics of WISE spiral galaxies, and uses SDSS, 2MASS, WISE, and MANGA parameters to analyse the differences between different classes and the commonality of the same class, so that provides help for astronomers to study infrared spiral galaxies. First, the RA and Dec. of the spiral galaxy samples were crossed in GalaxyZoo2. The flux data of w1, w2, and w3 bands in WISE were crossed for image synthesis. The Bootstrap Your Own Latent contrastive learning framework and K-means clustering were used to unsupervised classify the infrared spiral galaxy images. After multiple experiments, five classes of images were selected by referring to the best clustering results. Finally, the parameter of galaxies in SDSS, 2MASS, WISE, and MANGA catalogues were crossed, including redshift, 12 magnitudes that from visible light band to mid-infrared band, stellar formation rate, stellar metallicity, stellar velocity dispersion, etc. These parameters were qualitatively and quantitatively analyzed. The effectiveness of unsupervised clustering algorithms for handling unlabeled data is demonstrated and two special classes of galaxies are found. The analysis result shows that the distribution characteristics of different parameters of different classes of infrared spiral galaxies are different.

In this paper, we propose a convolutional neural network (CNN)-based photometric pipeline for the Sloan Digital Sky Survey (SDSS) images. The pipeline includes three main parts: the target source detection, the target source classification, and the photometric parameter measurement. The last part is completed using traditional methods. The paper mainly focuses on the first two parts and does not present the last. In the 1st part, a network named TSD-YOLOv4 is proposed to detect new sources missed by the SDSS photometric pipeline according to the PhotoObjAll catalogue of SDSS. In the second part, a target source classification network named TSCNet is constructed to classify sources into galaxies, quasars, and stars directly from photometric images. Experiments show that TSD-YOLOv4 outperforms other networks (Faster-RCNN, YOLOv4, YOLOX, etc.) in all metrics, with an accuracy of 0.988, a recall of 0.997, and an F1-score of 0.992, and TSCNet has good performance with a classification accuracy of 0.944 on the test set with 23 265 sources, and precision rates of 0.98, 0.908, and 0.918 for galaxies, quasars, and stars, respectively. On the other hand, the recall rates are 0.982, 0.903, and 0.921 for galaxies, quasars, and stars, respectively. The TSCNet has higher accuracy, fewer parameters, and faster inference speed than the leading astronomical photometric source classification network, the APSCNet model. In addition, the effect of magnitude distribution on the classification results is discussed in the experiments. The experiments prove that the proposed pipeline can be used as a powerful tool to supplement the SDSS photometric catalogue.

Diffuse interstellar bands (DIBs) are absorption features arising from the interstellar medium, for which the carriers remain unclear. In this work, a total of 3115 stars in the regions of NGC 2264, the Rosette Nebula, and S147 are selected from LAMOST-MRS to study the spatial and velocity distributions of the DIB λ6614 carrier. From the spatial distribution, we find that DIB λ6614 carrier clouds appear to be widely and inhomogeneously distributed in the three target regions. They are either closer or further in distance, or much wider than the three nebulae, and there are gaps within them. The radial velocities of the DIB λ6614 carrier clouds are on average about 13, 20, and 10 km s−1 slower than the radial velocities of the nebulae in the NGC 2264, Rosette Nebula, and S147 regions, respectively. Furthermore, there is probably a high-extinction region from 0.6 to 0.8 kpc in the NGC 2264 direction, where the DIB λ6614 carrier vanishes. In the S147 region, the DIB λ6614 cloud is possibly discontinuous, and the tomographic evidence suggests that there are two DIB λ6614 clouds. The first cloud is at 0.3–0.9 kpc, which is in or near the Local Arm, while the second one emerges at about 1.8 kpc from the Sun and may be associated with S147 and in the Perseus Arm.

Most astronomical source classification algorithms based on photometric data struggle to classify sources as quasars, stars, and galaxies reliably. To achieve this goal and build a new Sloan Digital Sky Survey photometric catalogue in the future, we apply a deep learning source detection network built on YOLO v4 object detection framework to detect sources and design a new deep learning classification network named APSCnet (astronomy photometric source classification network) to classify sources. In addition, a photometric background image generation network is applied to generate background images in the process of data sets synthesis. Our detection network obtains a mean average precision score of 88.02 when IOU = 0.5. As for APSCnet, in a magnitude range with 14–25, we achieve a precision of 84.1 ${{\ \rm per\ cent}}$ at 93.2 ${{\ \rm per\ cent}}$ recall for quasars, a precision of 94.5 ${{\ \rm per\ cent}}$ at 84.6 ${{\ \rm per\ cent}}$ recall for stars, and a precision of 95.8 ${{\ \rm per\ cent}}$ at 95.1 ${{\ \rm per\ cent}}$ recall for galaxies; and in a magnitude range with less than 20, we achieve a precision of 96.6 ${{\ \rm per\ cent}}$ at 94.7${{\ \rm per\ cent}}$ recall for quasars, a precision of 95.7${{\ \rm per\ cent}}$ at 97.4${{\ \rm per\ cent}}$ recall for stars, and a precision of 98.9 ${{\ \rm per\ cent}}$ at 99.2 ${{\ \rm per\ cent}}$ recall for galaxies. We have proved the superiority of our algorithm in the classification of astronomical sources through comparative experiments between multiple sets of methods. In addition, we also analysed the impact of point spread function on the classification results. These technologies may be applied to data mining of the next generation sky surveys, such as LSST, WFIRST, and CSST etc.

The classification of stars is a long-standing topic in the field of astronomy. Traditionally, the most popular way to classify stars is to use spectra. However, spectra are scarce compared to photometric images. In this paper, we present a machine-learning method to classify stars based on photometric images. This method proposes a new data-driven model based on convolutional feature and support vector machine algorithm (CFSVM). At first, the model uses convolution neural network to extract features from photometric images which are synthesized from photometric data of SDSS and support vector machine (SVM) algorithm to classify the extracted features. The model uses about 38 120 photometric images as the training set and it has good performance in classifying stars. 6823 photometric images are used to test the model and its accuracy reaches 79.7 per cent. When extending the range of error to the adjacent subtypes, the model can reach an accuracy of 91.7 per cent. And the classification results are very close to those from the spectra. Meanwhile the test proves that CFSVM is not sensitive to the signal to noise ratio (SNR) of stars.The model can give accurate classification results even if the SNR < 10. The experiments show that the CFSVM is feasible to classify the spectral types of stars only with photometric images.

We present a new method of DEEM, the direct energy encircling method, for characterising the performance of fibres in most astronomical spectroscopic applications. It's a versatile platform to measure focal ratio degradation (FRD), throughput, and point spread function (PSF). The principle of DEEM and the relation between the encircled energy (EE) and the spot size were derived and simulated based on the power distribution model (PDM). We analysed the errors of DEEM and pointed out the major error source for better understanding and optimisation. The validation of DEEM has been confirmed by comparing the results with conventional method which shows that DEEM has good robustness with high accuracy in both stable and complex experiment environments. Applications on the integral field unit (IFU) show that the FRD of 50$\mu$m core fibre is substandard for the requirement which requires the output focal ratio to be slower than 4.5. The homogeneity of throughput is acceptable and higher than 85 per cent. The prototype IFU of the first generation helps to find out the imperfections to optimise the new design of the next generation based on the staggered structure with 35$\mu$m core fibres of $N.A.$=0.12, which can improve the FRD performance. The FRD dependence on wavelength and core size is revealed that higher output focal ratio occurs at shorter wavelengths for large core fibres, which is in agreement with the prediction of PDM. But the dependence of the observed data is weaker than the prediction., Comment: 39 pages, 49 figures, accepted for publication in MNRAS in Feb. 2018

We study the classification and composite spectra of galaxy in the fourth data release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). We select 40,182 spectra of galaxies from LAMOST DR4, which have photometric in- formation but no spectroscopic observations in the Sloan Digital Sky Survey(SDSS). These newly observed spectra are re-calibrated and classified into six classes, i.e. pas- sive, H{\alpha}-weak, star-forming, composite, LINER and Seyfert using the line intensity (H\b{eta}, [OIII]{\lambda}5007, H{\alpha} and [NII]{\lambda}6585). We also study the correlation between spectral classes and morphological types through three parameters: concentration index, (u - r) color, and D4000n index. We calculate composite spectra of high signal-to-noise ra- tio(S/N) for six spectral classes, and using these composites we pick out some features that can differentiate the classes effectively, including H\b{eta}, Fe5015, H{\gamma}A, HK, and Mg2 band etc. In addition, we compare our composite spectra with the SDSS ones and analyse their difference. A galaxy catalogue of 40,182 newly observed spectra (36,601 targets) and the composite spectra of the six classes are available online., Comment: 14 pages, 11 figures

Focal ratio degradation (FRD) is a major contributor to throughput and light loss in a fibre spectroscopic telescope system. We combine the guided mode theory in geometric optics and a well-known model, power distribution model (PDM), to predict and explain the FRD dependence properties. We present a robust method by modifying the energy distribution method (EDM) with \emph{f-intercept} to control the input condition. This method provides a way to determine the proper position of the fibre end on the focal plane to improve energy utilization and FRD performance, which lifts the relative throughput up to 95\% with variation of output focal ratio less than 2\%. And this method can also help to optimize the arrangement of the position of focal-plane plate to enhance the coupling efficiency in a telescope. To investigate length properties, we modified PDM by introducing a new parameter, focal distance \emph{f}, into the original model to make it available for multi-position measurement system. The results show that the modified model is robust and feasible for measuring the key parameter \emph{d}$_0$ to simulate the transmission characteristics. The output focal ratio in the experiment does not follow the prediction trend but shows an interesting phenomenon that the output focal ratio increases at first to the peak, then decreases and remains stable finally with increasing fibre length longer than 15m, which provides a reference for choosing appropriate length of fibre to improve the FRD performance for the design of the fibre system in a telescope.

Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters -- effective temperature T_{\rm eff}, surface gravity log g and metallicity [Fe/H], absolute magnitudes M_V and M_{Ks}, {\alpha}-element to metal (and iron) abundance ratio [{\alpha}/M] (and [{\alpha}/Fe]), as well as carbon and nitrogen abundances [C/H] and [N/H] from the LAMOST spectra with amultivariate regressionmethod based on kernel-based principal component analysis, using stars in common with other surveys (Hipparcos, Kepler, APOGEE) as training data sets. Both internal and external examinations indicate that given a spectral signal-to-noise ratio (SNR) better than 50, our method is capable of delivering stellar parameters with a precision of ~100K for Teff, ~0.1 dex for log g, 0.3 -- 0.4mag for M_V and M_{Ks}, 0.1 dex for [Fe/H], [C/H] and [N/H], and better than 0.05 dex for [{\alpha}/M] ([{\alpha}/Fe]). The results are satisfactory even for a spectral SNR of 20. The work presents first determinations of [C/H] and [N/H] abundances from a vast data set of LAMOST, and, to our knowledge, the first reported implementation of absolute magnitude estimation directly based on the observed spectra. The derived stellar parameters for millions of stars from the LAMOST surveys will be publicly available in the form of value-added catalogues., Comment: 23 pages, 23 figures; MNRAS accepted

We present a sample of metallic-line star (Am) candidates from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) Data Release One (DR1). According to the characteristic of underabundance of calcium and overabundance of iron element of Am stars, we propose an empirical separation curve derived from line indices of Ca ii K line and iron lines we choose for low-resolution spectra. Ultimately, 3537 Am candidates are selected from more than 30 000 stars, which are classified as A-type or early F-type stars by both the LAMOST pipeline and visual inspection. Then, we carry out some analysis on this sample and finally provide a list of the Am candidates with 10 relevant parameters. Compared with other catalogues, Am candidates selected from the LAMOST DR1 are much fainter on the whole. Obviously, our list is an important complement to already known bright Am catalogues, and it offers valuable material for research on this type of chemically peculiar star.