Sloan Digital Sky Survey Data Release Research Articles

Morphologies of galaxies within voids

Among the largest structures in which matter is distributed in the Universe, we find cosmic voids, which are large, under-dense regions almost devoid of galaxies. The study of these structures and the galaxies that inhabit them, the void galaxies, provides key information for understanding galaxy evolution. In this work we investigate the effects of the environment on the evolution of void galaxies. In particular, we study their morphology and explore its dependence on the location within the void where the galaxies reside, as well as on the properties of the void, such as its size and the galaxy number density. The sample of void galaxies that we use in this study is based on the catalogue of cosmic voids and void galaxies in the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). As we are interested in studying the morphology of void galaxies, we select galaxies in the redshift range of 0.005\,leq \,z\,leq \,0.080, and use the public galaxy morphologies of the SDSS sample together with deep learning algorithms to divide the sample into early- and late-type void galaxies. We analyse the fractions of galaxies of each morphological type as a function of the void-centric distance, the size of the voids, and the density of galaxies in each void. There is a higher abundance of late-type galaxies with respect to early-type galaxies within voids, which remains nearly constant from the inner to the outer part of the voids. We do not find any dependence of the fraction of early- and late-type galaxies on void size or on the number-density of galaxies in the voids. Galaxies in voids follow the morphology--density relation, in the sense that the majority of the galaxies in voids (the most under-dense large-scale environments) are late-type galaxies. However, we find no difference between voids with lower or higher volume number-density of galaxies: the fractions of early- and late-type galaxies do not depend on the density of the voids. The physical processes responsible for the evolution from late towards earlier types (such as external environmental quenching) are not sufficiently effective in voids or are so slow (internal secular quenching) that their contributions do not appear in the morphology--density relation.

YOLOX-CS: An Automatic Search Algorithm for Low Surface Brightness Galaxies

The characteristics of Low Surface Brightness Galaxies (LSBGs) are very important for understanding the overall characteristics of galaxies. It is of great significance to search and expand the samples of Low Surface Brightness Galaxies by modern machine learning, especially deep learning algorithm. LSBGs are difficult to discern automatically and accurately with traditional methods because of their obscure features. However, deep learning does have the advantage of automatically identifying complex and effective features. To solve this problem, an algorithm named You Only Look Once version X-CS (YOLOX-CS) is proposed to search LSBG in large sample sky survey. Firstly, five classical target detection algorithms are compared through experiments and the optimal YOLOX algorithm is selected as the basic algorithm. Then, the YOLOX-CS framework is constructed by combining different attention mechanisms and different optimizers. The data set uses images from the Sloan Digital Sky Survey (SDSS), labelled from LSBG in the α.40-SDSS DR7 (the cross coverage area of 40% HI Arecibo Legacy Fast ALFA Survey and SDSS Data Release7) survey. Due to the small number of samples in this data set, Deep Convolutional Generative Adversarial Networks (DCGAN) model is used to expand the experimental test data. After comparing with a series of target detection algorithms, YOLOX-CS has a good test result in searching LSBG recall rate and Average Precision (AP) value in two data sets before and after expansion. The recall rate and AP value in the test set without expansion data set reach 97.75% and 97.83%, respectively. In the expanded data set of DCGAN model, under the same test set, the recall rate reaches 99.10% and the AP value reaches 98.94%, which proves that the algorithm has excellent performance in LSBG search. Finally, the algorithm is applied to SDSS photometric data, and 765 LSBG candidates are obtained.

Peculiar Velocity Reconstruction from Simulations and Observations Using Deep Learning Algorithms

In this paper, we introduce a U-Net model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the U-Net model with simulation data under more realistic conditions, including the redshift space distortion effect and halo mass threshold. Our results show that the U-Net model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient, and 27% in average coherence. The deep learning algorithm exhibits exceptional capacities to capture velocity features in nonlinear regions and substantially improve reconstruction precision in boundary regions. We then apply the U-Net model trained under Sloan Digital Sky Survey (SDSS) observational conditions to the SDSS Data Release 7 data for observational 3D peculiar velocity reconstructions.

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Edge-on Low-surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO

Low-surface-brightness galaxies (LSBGs), fainter members of the galaxy population, are thought to be numerous. However, due to their low surface brightness, the search for a wide-area sample of LSBGs is difficult, which in turn limits our ability to fully understand the formation and evolution of galaxies as well as galaxy relationships. Edge-on LSBGs, due to their unique orientation, offer an excellent opportunity to study galaxy structure and galaxy components. In this work, we utilize the You Only Look Once object detection algorithm to construct an edge-on LSBG detection model by training on 281 edge-on LSBGs in Sloan Digital Sky Survey (SDSS) gri-band composite images. This model achieved a recall of 94.64% and a purity of 95.38% on the test set. We searched across 938,046 gri-band images from SDSS Data Release 16 and found 52,293 candidate LSBGs. To enhance the purity of the candidate LSBGs and reduce contamination, we employed the Deep Support Vector Data Description algorithm to identify anomalies within the candidate samples. Ultimately, we compiled a catalog containing 40,759 edge-on LSBG candidates. This sample has similar characteristics to the training data set, mainly composed of blue edge-on LSBG candidates. The catalog is available online at https://github.com/worldoutside/Edge-on_LSBG.

Critical assessment of the recent report on the gigaparsec-scale correlation of the orientations of large quasar groups

ABSTRACT Recently, it was reported that large quasar groups (LQGs) identified from the Sloan Digital Sky Survey (SDSS) data release seven catalogue are not randomly oriented but preferentially aligned or orthogonal over scales 1–2 Gpc. To confirm this claim, I reproduced the same LQG sample and performed Sobolev tests of uniformity on the LQG orientation axes in the redshift space. Contrary to the original report based on the bimodal distribution of the LQG position angles in the sky, I found no departure from uniformity in the distribution of the LQG orientation axes. I also examined whether the LQGs are physical structures using a statistically more reliable data set constructed from the SDSS data release 16 (DR16) large-scale structure (LSS) quasar catalogue. Considering the Gaussian primordial density fluctuations and shot noise, I estimated the mass density contrasts of the LQGs from the number counts of the DR16 LSS quasars and found that most of the LQGs do not trace statistically significant high-density regions. I conclude that the LQG sample is a collection of unphysical chance associations and should not be used for any cosmological studies.

Star Photometry for DECam Legacy Survey and Sloan Digital Sky Survey Images Based on Convolutional Neural Networks

Flux is one of the most fundamental parameters in astrophysics, and aperture photometry and point-spread function (PSF) photometry are commonly used methods to obtain the flux. With the continuous development of astronomical equipment that has generated massive data, researchers have to find more effective methods to obtain stellar fluxes. However, current photometric software such as SExtractor are very sensitive to the configuration parameters and are difficult to configure. We propose a new photometric model based on deep learning called sf-convolutional neural network (CNN) to extract aperture fluxes and PSF fluxes. For the simulated data including 5727 stars, the experimental results show that sf-CNN can predict fluxes better than SExtractor. The mean absolute error (MAE) values of sf-CNN and SExtractor for predicting PSF fluxes are 0.0034 and 0.0134, respectively. On the 6293 mixed stars in DECam Legacy Survey Data Release (DR) 9, the MAE values of the predicted PSF fluxes are 0.0075 and 0.0177, respectively. The PSF accuracy of the sf-CNN model is significantly higher than that of SExtractor. Additionally, the MAE values of the predicted aperture fluxes on 6215 mixed stars and 1341 blends of stars in Sloan Digital Sky Survey DR 12 illustrate that the accuracy of sf-CNN is still the highest. Meanwhile, the results indicate that sf-CNN outperforms VGG16 and ResNet50. Furthermore, sf-CNN is 100–200 times faster than Photutils on RTX 3070 GPU and 20–40 times faster than Photutils on I7 12700 CPU. sf-CNN can calculate fluxes efficiently and accurately only by setting a few parameters and may thus become a fundamental tool for the era of big data in astronomy.

Cosmological measurements from void-galaxy and galaxy-galaxy clustering in the Sloan Digital Sky Survey

ABSTRACT We present the cosmological implications of measurements of void-galaxy and galaxy-galaxy clustering from the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample (MGS), Baryon Oscillation Spectroscopic Survey (BOSS), and extended BOSS (eBOSS) luminous red galaxy catalogues from SDSS Data Release 7, 12, and 16, covering the redshift range 0.07 < $z$ < 1.0. We fit a standard ΛCDM cosmological model as well as various extensions, including a constant dark energy equation of state not equal to −1, a time-varying dark energy equation of state, and these same models allowing for spatial curvature. Results on key parameters of these models are reported for void-galaxy and galaxy-galaxy clustering alone, both of these combined, and all these combined with measurements from the cosmic microwave background (CMB) and supernovae (SN). For the combination of void-galaxy and galaxy-galaxy clustering, we find tight constraints of Ωm = 0.356 ± 0.024 for a base ΛCDM cosmology, $\Omega _\mathrm{m} = 0.391^{+0.028}_{-0.021}, w = -1.50^{+0.43}_{-0.28}$ additionally allowing the dark energy equation of state $w$ to vary, and $\Omega _\mathrm{m} = 0.331^{+0.067}_{-0.094}, w=-1.41^{+0.70}_{-0.31},\ \mathrm{and}\ \Omega _\mathrm{k} = 0.06^{+0.18}_{-0.13}$ further extending to non-flat models. The combined SDSS results from void-galaxy and galaxy-galaxy clustering in combination with CMB+SN provide a 30 per cent improvement in parameter Ωm over CMB+SN for ΛCDM, a 5 per cent improvement in parameter Ωm when $w$ is allowed to vary, and a 32 per cent and 68 per cent improvement in parameters Ωm and Ωk when allowing for spatial curvature.

ALMA High-Level Data Products: submillimetre counterparts of SDSS quasars in the ALMA footprint

ABSTRACT The Atacama Large Millimetre/submillimetre Array (ALMA) is the world’s most advanced radio interferometric facility, producing science data with an average rate of about 1 TB per day. After a process of calibration, imaging and quality assurance, the scientific data are stored in the ALMA Science Archive (ASA), along with the corresponding raw data, making the ASA an invaluable resource for original astronomical research. Due to their complexity, each ALMA data set has the potential for scientific results that go well beyond the ideas behind the original proposal that led to each observation. For this reason, the European ALMA Regional Centre initiated the High-Level Data Products initiative to develop science-oriented data products derived from data sets publicly available in the ASA, that go beyond the formal ALMA deliverables. The first instance of this initiative is the creation of a catalogue of submillimetre (submm) detections of Sloan Digital Sky Survey (SDSS) quasars from the SDSS Data Release 14 that lie in the aggregate ALMA footprint observed since ALMA Cycle 0. The ALMA fluxes are extracted in an automatic fashion, using the ALMA Data Mining Toolkit. All extractions above a signal-to-noise cut of 3.5 are considered, they have been visually inspected and the reliable detections are presented in a catalogue of 376 entries, corresponding to 275 unique quasars. Interesting targets found in the process, i.e. lensed or jetted quasars as well as quasars with nearby submm counterparts are highlighted, to facilitate further studies or potential follow up observations.

Data-driven selection and spectral classification of white dwarf stars

ABSTRACT The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ${\gtrsim}90\ \hbox{per cent}$ accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

A catalogue of white dwarfs in Gaia EDR3

ABSTRACT We present a catalogue of white dwarf candidates selected from Gaia Early Data Release 3 (EDR3). We applied several selection criteria in absolute magnitude, colour, and Gaia quality flags to remove objects with unreliable measurements while preserving most stars compatible with the white dwarf locus in the Hertzsprung–Russell diagram. We then used a sample of over 30 000 spectroscopically confirmed white dwarfs and contaminants from the Sloan Digital Sky Survey (SDSS) to map the distribution of these objects in the Gaia absolute magnitude–colour space. Finally, we adopt the same method presented in our previous work on Gaia Data Release 2 (DR2) to calculate a probability of being a white dwarf (PWD) for ≃1.3 million sources that passed our quality selection. The PWD values can be used to select a sample of ${\simeq} 359\,000$ high-confidence white dwarf candidates. We calculated stellar parameters (effective temperature, surface gravity, and mass) for all these stars by fitting Gaia astrometry and photometry with synthetic pure-H, pure-He, and mixed H–He atmospheric models. We estimate an upper limit of 93 per cent for the overall completeness of our catalogue for white dwarfs with G ≤ 20 mag and effective temperature (Teff) > 7000 K, at high Galactic latitudes (|b| > 20°). Alongside the main catalogue we include a reduced proper motion extension containing ${\simeq} 10\,200$ white dwarf candidates with unreliable parallax measurements that could, however, be identified on the basis of their proper motion. We also performed a cross-match of our catalogues with SDSS Data Release 16 (DR16) spectroscopy and provide spectral classification based on visual inspection for all resulting matches.

Appearance versus disappearance of broad absorption line troughs in quasars

ABSTRACT We present a new set of 84 broad absorption line (BAL) quasars (1.7 < zem < 4.4) exhibiting an appearance of C iv BAL troughs over 0.3–4.8 rest-frame years by comparing the Sloan Digital Sky Survey Data Release (SDSSDR)-7, SDSSDR-12, and SDSSDR-14 quasar catalogues. We contrast the nature of BAL variability in this appearing BAL quasar sample with a disappearing BAL quasar sample studied in the literature by comparing the quasar’s intrinsic, BAL trough, and continuum parameters between the two samples. We find that appearing BAL quasars have relatively higher redshift and smaller probed time-scales as compared to the disappearing BAL quasars. To mitigate the effect of any redshift bias, we created control samples of appearing and disappearing BAL quasars that have similar redshift distribution. We find that the appearing BAL quasars are relatively brighter and have shallower and wider BAL troughs compared to the disappearing BAL sample. The distribution of quasar continuum variability parameters between the two samples is clearly separated, with the appearance of the BAL troughs being accompanied by the dimming of the continuum and vice versa. Spectral index variations in the two samples also point to the anticorrelation between the BAL trough and continuum variations consistent with the ‘bluer when brighter’ trend in quasars. We show that the intrinsic dust model is less likely to be a favourable scenario in explaining BAL appearance/disappearance. Our analysis suggests that the extreme variations of BAL troughs like BAL appearance/disappearance are mainly driven by changes in the ionization conditions of the absorbing gas.

Matched Photometric Catalogs of GALEX UV Sources with Gaia DR2 and SDSS DR14 Databases (GUVmatch)

Abstract We have matched the ultraviolet (UV) sources in GUVcat_AIS with optical databases that have similar depth and wide sky coverage. GUVcat_AIS has Galaxy Evolution Explorer (GALEX) far-UV (FUV, λ eff ∼ 1528 Å) and near-UV (NUV, λ eff ∼ 2310 Å) photometry of ≈83 million sources, covering 24,788 square degrees of the sky, with a typical depth of FUV = 19.9 and NUV = 20.8 AB mag. Matches with Gaia and the Sloan Digital Sky Survey (SDSS) databases are presented here. Gaia data release 2 (DR2), covering the entire GUVcat footprint, detected about one-third of the GUVcat_AIS sources. We found 31,925,294 Gaia DR2 counterparts to 30,024,791 GUVcat_AIS unique sources, with photometry in the Gaia G band and often also in Gaia BP and RP bands; 26,275,572 matches have a parallax measurement, 21,084,628, 18,588,140, and 16,357,505 have a parallax error less than 50%, 30%, and 20%, respectively. The match with SDSS data release 14 (DR14) yields 23,310,532 counterparts to 22,207,563 unique GUVcat_AIS sources, 10,167,460 of which are pointlike, over a total overlap area of ≈11,100 square degrees (Bianchi et al. 2019). SDSS adds five optical magnitudes to the UV photometry : u, g, r, i, z, and optical spectra of 860,224 matched sources. We used a match radius of 3″, consistent with previous works, although the positions agree to ≲15 for the majority of (pointlike) matched sources, in order to identify possible multiple matches whose UV flux could be unresolved in GALEX imaging. The catalogs can be trimmed to a tighter match radius using the provided separation. The multiband photometry is used to identify classes of astrophysical objects that are prominent in UV, to characterize the content of the GUVmatch catalogs, where stars in different evolutionary stages, quasi-stellar objects, and galaxies can be separated.

APOGEE Data and Spectral Analysis from SDSS Data Release 16: Seven Years of Observations Including First Results from APOGEE-South

Abstract The spectral analysis and data products in Data Release 16 (DR16; 2019 December) from the high-resolution near-infrared Apache Point Observatory Galactic Evolution Experiment (APOGEE)-2/Sloan Digital Sky Survey (SDSS)-IV survey are described. Compared to the previous APOGEE data release (DR14; 2017 July), APOGEE DR16 includes about 200,000 new stellar spectra, of which 100,000 are from a new southern APOGEE instrument mounted on the 2.5 m du Pont telescope at Las Campanas Observatory in Chile. DR16 includes all data taken up to 2018 August, including data released in previous data releases. All of the data have been re-reduced and re-analyzed using the latest pipelines, resulting in a total of 473,307 spectra of 437,445 stars. Changes to the analysis methods for this release include, but are not limited to, the use of MARCS model atmospheres for calculation of the entire main grid of synthetic spectra used in the analysis, a new method for filling “holes” in the grids due to unconverged model atmospheres, and a new scheme for continuum normalization. Abundances of the neutron-capture element Ce are included for the first time. A new scheme for estimating uncertainties of the derived quantities using stars with multiple observations has been applied, and calibrated values of surface gravities for dwarf stars are now supplied. Compared to DR14, the radial velocities derived for this release more closely match those in the Gaia DR2 database, and a clear improvement in the spectral analysis of the coolest giants can be seen. The reduced spectra as well as the result of the analysis can be downloaded using links provided on the SDSS DR16 web page.

X-ray properties of the X-CLASS-redMaPPer galaxy cluster sample: the luminosity–temperature relation

ABSTRACT This article presents the results of a spectroscopic analysis of the X-CLASS-redMaPPer (XC1-RM) galaxy cluster sample. X-CLASS is a serendipitous search for clusters in X-ray wavebands based on the XMM–Newton archive, whereas redMaPPer is an optical cluster catalogue derived from the Sloan Digital Sky Survey (SDSS). The present sample comprises 92 X-ray extended sources identified in optical images within 1 arcmin separation. The area covered by the cluster sample is ∼ 27 deg2. The clusters span a wide redshift range (0.05 < z < 0.6) and 88 clusters benefit from spectrosopically confirmed redshifts using data from SDSS Data Release 14. We present an automated pipeline to derive the X-ray properties of the clusters in three distinct apertures: R500 (at fixed mass overdensity), Rfit (at fixed signal-to-noise ratio) and ${R}_{300\, {\rm kpc}}$ (fixed physical radius). The sample extends over wide temperature and luminosity ranges: from 1–10 keV and from 6 × 1042 to 11 × 1044 erg s−1, respectively. We investigate the luminosity–temperature (L–T) relation of the XC1-RM sample and find a slope equal to 3.03 ± 0.26. It is steeper than predicted by self-similar assumptions, in agreement with independent studies. A simplified approach is developed to estimate the amount and impact of selection biases that might be affecting our recoveredL–Tparameters. The result of this simulation process suggests that the measuredL–Trelation is biased to a steeper slope and higher normalization.

Direct Effects of the Environment on AGN Triggering in SDSS Spiral Galaxies: Merger-AGN connection

Abstract We examine whether galaxy environments directly affect triggering nuclear activity in Sloan Digital Sky Survey (SDSS) local spiral galaxies using a volume-limited sample with the r-band absolute magnitude Mr < −19.0 and 0.02 < z < 0.055 selected from the SDSS Data Release 7. To avoid incompleteness of the central velocity dispersion σ of the volume-limited sample and to fix the black hole mass affecting AGN activity, we limit the sample to a narrow σ range of 130 < σ < 200 km s−1. We define a variety of environments as a combination of neighbour interactions and local density on a galaxy. After the central star formation rate (which is closely related to AGN activity level) is additionally restricted, the direct impact of the environment is unveiled. In the outskirts of rich clusters, red spiral galaxies show a significant excess of the AGN fraction despite the lack of central gas. We argue that they have been pre-processed before entering the rich clusters, and due to mergers or strong encounters in the in-fall region, their remaining gases efficiently lose angular momentum. Furthermore, we find many star-forming galaxies and only a few starburst-AGN composite hosts with the highest [OIII] luminosity. We claim that they are a gas-rich merger product in groups or are group galaxies in-falling into clusters, indicating that many AGN signatures may be obscured during the merger events.

Constraints on running of non-Gaussianity from large-scale structure probes

ABSTRACT In this letter, we present constraints on the scale-dependent ‘local’-type primordial non-Gaussianity, which is described by non-Gaussianity’s spectral index nNG, from the NRAO VLA Sky Survey and the quasar catalogue of the Sloan Digital Sky Survey (SDSS) Data Release 6, together with the SDSS Data Release 12 photo-z sample. Here, we use the autocorrelation analyses of these three probes and their cross-correlation analyses with the cosmic microwave background (CMB) temperature map, and obtain the tight constraint on the spectral index: $n_{\rm NG}=0.2 ^{+0.7}_{-1.0}$ ($1\sigma$ C.L.), which shows the first competitive constraint on the running of non-Gaussianity from current large-scale structure clustering data. Furthermore, we also perform the forecast calculations and improve the limit of nNG using the future Euclid mission, and obtain the standard deviation at a 68 per cent confidence level: ΔnNG = 1.74 when considering the fiducial value fNL = 3, which provides the complementary constraining power to those from the CMB bispectrum information.

Machine and Deep Learning applied to galaxy morphology - A comparative study

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.

Stellar population analysis on the stacked spectra of double-peaked emission-line galaxies

Double-peaked emission-line galaxies have long been perceived as objects related to merging galaxies or other phenomenawith disturbed dynamical activities, such as outflows and disk rotation. In order to find the connection between the unique activities happening in these objects and their stellar population physics, we study the stellar populations of the stacked spectra drawn from double-peaked emission-line galaxies in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 4 (DR4) and the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) databases. We group the selected double-peaked emission-line objects into 10 different types of pairs based on the Baldwin-Phillips-Terlevich (BPT) diagnosis for each pair of blueshifted and redshifted components, and then stack the spectra of each group for analysis. The software STARLIGHT is employed to fit each stacked spectrum, and the contributions of stars at different ages and metallicities are quantified for subsequent comparative study and analysis. To highlight the commonality and uniqueness in these double-peaked emitting objects, we compare the population synthesis results of the stacked spectra of double-peaked emission-line galaxies with those of their counterpart reference samples displaying single-peaked emission features. The reference samples are also selected from the LAMOST DR4 and SDSS DR7 databases. From the comparison results, we confirm the strong correlations between stellar populations and their spectral classes, and find that the double-peaked emitting phenomenon is more likely to occur in an ‘older’ stellar environment and the subgroups hosting different BPT components will show an obvious heterogeneous star formation history.

Detection of intercluster gas in superclusters using the thermal Sunyaev–Zel’dovich effect

Using a thermal Sunyaev–Zel’dovich (tSZ) signal, we search for hot gas in superclusters identified using the Sloan Digital Sky Survey Data Release 7 (SDSS/DR7) galaxies. We stack a Comptonizationymap produced by thePlanckCollaboration around the superclusters and detect the tSZ signal at a significance of 6.4σ. We further search for an intercluster component of gas in the superclusters. For this, we remove the intracluster gas in the superclusters by masking all galaxy groups/clusters detected by thePlancktSZ, ROSAT X-ray, and SDSS optical surveys down to a total mass of 1013 M⊙. We report the first detection of intercluster gas in superclusters withy = (3.5 ± 1.4) × 10−8at a significance of 2.5σ. Assuming a simple isothermal and flat density distribution of intercluster gas over superclusters, the estimated baryon density is (Ωgas/Ωb)×(Te/8 × 106 K) = 0.067 ± 0.006 ± 0.025. This quantity is inversely proportional to the temperature, therefore taking values from simulations and observations, we find that the gas density in superclusters may account for 17–52% of missing baryons at low redshifts. A better understanding of the physical state of gas in the superclusters is required to accurately estimate the contribution of our measurements to missing baryons.