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VAST void catalogs for SDSS DR7
<p><em>Version 1.3.1 of the void catalogs; the NSAID column in the V2*galzones.dat have been corrected (previous versions incorrectly listed the row number of the galaxy into the NSA catalog as the NSAID). The peer-reviewed publication for these catalogs is published in the Astrophysical Journal Supplemental Series, an American Astronomical Society journal. The paper is available at <a href="https://iopscience.iop.org/article/10.3847/1538-4365/acabcf" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.3847/1538-4365/acabcf</a>; please cite this article when using any of these catalogs.</em></p> <p>We provide three void catalogs using a volume-limited subsample of the SDSS DR7 for two cosmologies (Planck 2018 and WMAP5) using new implementations of two different void-finding algorithms from the Void Analysis Software Toolkit (<a href="https://vast.readthedocs.io/en/latest/">VAST</a>): VoidFinder and Voronoi Voids (V<sup>2</sup>). We identify 1163 cosmic voids with VoidFinder, 531 with V<sup>2</sup> using VIDE pruning, and 518 with V<sup>2</sup> using REVOLVER pruning, with the Planck 2018 cosmology, and 1184, 535, and 519 voids with the WMAP5 cosmology, respectively. These catalogs exist within the SDSS DR7 main survey volume, out to a maximum redshift of 0.114. Effective radii and centers for all voids are computed and included with the catalogs; the median void effective radius is 15-19 <em>h</em><sup>-1</sup> Mpc for all three catalogs, and none have an effective radius larger than 54 <em>h</em><sup>-1</sup> Mpc.</p> <p>All catalog files are in ASCII format with commented headers. An example of each file is shown in Tables 1-5 in the associated journal article. For each galaxy in the _galzones.dat files, the NSAID column corresponds to that galaxy's ID number in the NASA-Sloan Atlas. Guidance on how to use these catalogs can be found at <a href="https://vast.readthedocs.io/en/latest/">https://vast.readthedocs.io/en/latest/</a>. We also provide the mask file used in generating the VoidFinder void catalog with Planck 2018 cosmology.</p> <p><strong>Planck 2018 cosmology</strong></p> <ul> <li><strong>VoidFinder</strong> <ul> <li>VoidFinder-nsa_v1_0_1_Planck2018_comoving_holes.txt</li> <li>VoidFinder-nsa_v1_0_1_Planck2018_comoving_maximals.txt</li> <li>NSA_main_mask.pickle</li> </ul> </li> <li><strong>V<sup>2</sup></strong> <ul> <li><strong>VIDE pruning</strong> <ul> <li>V2_VIDE-nsa_v1_0_1_Planck2018_galzones.dat</li> <li>V2_VIDE-nsa_v1_0_1_Planck2018_zonevoids.dat</li> <li>V2_VIDE-nsa_v1_0_1_Planck2018_zobovoids.dat</li> </ul> </li> <li><strong>REVOLVER pruning</strong> <ul> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_galzones.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_zonevoids.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_Planck2018_zobovoids.dat</li> </ul> </li> </ul> </li> </ul> <p><strong>WMAP5 cosmology</strong></p> <ul> <li><strong>VoidFinder</strong> <ul> <li>VoidFinder-nsa_v1_0_1_WMAP5_comoving_holes.txt</li> <li>VoidFinder-nsa_v1_0_1_WMAP5_comoving_maximals.txt</li> </ul> </li> <li><strong>V<sup>2</sup></strong> <ul> <li><strong>VIDE pruning</strong> <ul> <li>V2_VIDE-nsa_v1_0_1_WMAP5_galzones.dat</li> <li>V2_VIDE-nsa_v1_0_1_WMAP5_zonevoids.dat</li> <li>V2_VIDE-nsa_v1_0_1_WMAP5_zobovoids.dat</li> </ul> </li> <li><strong>REVOLVER pruning</strong> <ul> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_galzones.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_zonevoids.dat</li> <li>V2_REVOLVER-nsa_v1_0_1_WMAP5_zobovoids.dat</li> </ul> </li> </ul> </li> </ul>
SDSS-IV MaNGA DR17 Principcal Component Analysis spaxel classifcations
<p>The zip files contains 10120 fits.gz files and two Python .p files.</p><p>The files spaxel_properties_master_DR17.p and elliptical_radii_params_DR17.p contain all of the PCA values of spaxels from all galaxies and all the fraction of spaxels of a particular type in an ellipse (so looping over all of the maps to get this information is not necessary). </p><p>There is one map per MaNGA galaxy. The data structure of each fits.gz file is:</p><p>HDU 0: [image] primary header from the DAP MAPS file. </p><p>HDU 1: [image] 'PC1' - PC1 amplitude. </p><p>HDU 2: [image] 'PC2' - PC2 amplitude. </p><p>HDU 3: [image] 'PC3' - PC3 amplitude. </p><p>HDU 4: [image] 'PC1ERR' - PC1 error. </p><p>HDU 5: [image] 'PC2ERR' - PC2 error. </p><p>HDU 6: [image] 'PC3ERR' - PC3 error. </p><p>HDU 7: [image] qualmask – Mask applied to PC1 map, has mask=(snr_4000A.T < 4.) | (pc1_map_reshaped.T < -10.) | (nocov) | (lowcov) | (donotuse) | (deadfiber) | (forestar). i.e. it excludes low S/N spaxels, weird PCA values and bad spaxels. </p><p>HDU 8: [image] 'snr4000A' - Median signal-to-noise in the 4000A break region. </p><p>HDU 9: [image] 'norm' - normalisation of the spectrum in the PCA. Used for reconstruction of the spectrum. </p><p>HDU 10: [image] 'class_map' - map of PCA classifications. 1=quiescent, 2=star-forming, 3=starburst, 4=green valley, 5-post-starburst, 0=unclassified (do not use)</p><p>HDU 11: [image] 'spx_bin_mask' - Mask accounting for identical values in a bin (see below for more details). </p><p>The PCA code (see https://github.com/KateRowlands/MaNGA-PCA, Rowlands et al. 2018, based on Wild et al. 2007) is run on the HYB10-MILESHC-MASTARSSP cubes, where the stellar continuum is binned but the emission line measurements are done on the unbinned spectra (see SDSS DR17 DAP documentation for more details). In these maps the spectra in each stellar continuum bin are identical, so the PC amplitudes are identical. The analysis is done in this way to preserve the shape of the maps for comparing to other quantities. The identical nature of spaxels in the same bin needs to be accounted for in some analysis e.g. those which count spaxels of a certain PCA class. The spx_bin_mask accounts for this double counting by providing a mask which has the central spaxel in the Voronoi bin set to 1. For spaxels with unique PCA values, set spx_bin_mask==1.</p><p>If plotting 2D maps of the PCA classes then spx_bin_mask should not be applied otherwise there will be gaps in the maps.</p><p>To flag out poor quality spaxels, reject anything with snr4000A < 4, although different S/N cuts may be applied depending on your science case. Furthermore, the PCA parameters are affected by dust. PCA classifications of PSBs in regions with visible dust lanes e.g. in edge-on and or/ dusty galaxies should be closely examine by hand to ensure robustness. Values of -99 and 99 indicate no data or bad data and should be excluded.</p>
SDSS-IV Cosmic Web Catalog
<p>This repository contains the cosmic web catalog data released in the paper "Cosmic Web Catalog on SDSS-IV Data with SCONCE" (preparing).</p> <p>The catalog is constructed on the SDSS-IV galaxies and quasars (QSO) using our proposed Directional Subspace Constrained Mean Shift (DirSCMS) algorithm. We release both the cosmic filaments and local modes (i.e., local maxima of the estimated galaxy/QSO density field, which serves as candidates of galaxy clusters) within 325 thin redshift slices, each of which spans 20Mpc under the Planck15 cosmology. The entire catalog covers the redshift range from <span class="math-tex">\(z=0\)</span> to <span class="math-tex">\(z=3\)</span>.</p> <p> 1. "<strong>Cosmic_filaments_2D_DirSCMS_new1</strong>": The file contains some discrete realizations of the estimated cosmic filaments in some particular redshift slices. The meaning of each column in the file is described as follows:</p> <ul> <li><strong>RA</strong> -- right ascension.</li> <li><strong>DEC</strong> -- declination.</li> <li><strong>z_low</strong> -- lower limit of the redshift slice.</li> <li><strong>z_high</strong> -- upper limit of the redshift slice.</li> <li><strong>comov_dist_low</strong> -- lower limit of the comoving distance in the redshift slice under the Planck15 cosmology.</li> <li><strong>comov_dist_high</strong> -- upper limit of the comoving distance in the redshift slice under the Planck15 cosmology.</li> <li><strong>bw</strong> -- smoothing bandwidth parameter for the DirSCMS algorithm in the redshift slice.</li> <li><strong>unc_meas</strong> -- uncertainty measure of the filamentary point by the nonparametric bootstrap techinque.</li> <li><strong>density</strong> -- (proportional) estimated galaxy/QSO density value at the filamentary point.</li> <li><strong>grad_Dir1</strong> -- (Riemannian) gradient of the estimated density field (first direction).</li> <li><strong>grad_Dir2</strong> -- (Riemannian) gradient of the estimated density field (second direction).</li> <li><strong>grad_Dir3</strong> -- (Riemannian) gradient of the estimated density field (third direction).</li> <li><strong>knot_label</strong> -- indicator of whether the filamentary point is a knot (i.e., the intersection of several filaments) or not.</li> </ul> <p> 2. "<strong>Cosmic_local_modes_2D_DirMS_unique1</strong>": The file contains some discrete realizations of the estimated local modes in some particular redshift slices. The columns are subsumed by the ones in the cosmic filament file and has been described above.</p> <p><em>Additional notes: We provide both the "csv" and "fits" format for each of the above file.</em></p> <p> </p> <p>Please cite the paper when using the data in this repository.</p> <p>► Pure catalog data:</p> <p>[1] <strong>Cosmic Web Catalog on SDSS-IV Data with SCONCE</strong>. (In preparation)</p> <p>►Methodology:</p> <p>[1] Yikun Zhang, Rafael S. de Souza, and Yen-Chi Chen (2022). <strong>SCONCE: A Cosmic Web Finder for Spherical and Conic Geometries</strong>. <em>arXiv preprint arXiv:2207.07001</em></p> <p>[2] Yikun Zhang and Yen-Chi Chen (2022) <strong>Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data</strong>. <em>Information and Inference: A Journal of the IMA</em>, iaac005, <a href="https://doi.org/10.1093/imaiai/iaac005">https://doi.org/10.1093/imaiai/iaac005</a></p> <p>[3] Yikun Zhang and Yen-Chi Chen (2021) <strong>Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data</strong>. <em>Journal of Machine Learning Research</em> <strong>22</strong>(154): 1-92.</p>
The SDSS Peculiar Velocity Catalogue
<p>Data, randoms and mock galaxy catalogues for the <em>Sloan Digital Sky Survey Peculiar Velocity Catalogue</em>; Howlett et. al., 2022, MNRAS, in press. See arXiv:2201.03112 for more details.</p> <p>Changelog:</p> <ul> <li>1.1.0: <ul> <li>Fixed error in v1.0.0 in specObjID column caused (at some point in the pipeline) due to rounding errors when reading/writing large numbers. v1.1.0 has the correct specObjIDs. Note that 'objid' is correct in both versions of the SDSS PV catalogue, and our recommended method to crossmatch to the spectroscopic SDSS data (wherein the corresponding column to match with is 'bestobjid').</li> </ul> </li> </ul>
The data for SKYSURF-5: Probing the Integrated Galaxy Light with a SDSS-SKYSURF Cross-Matched Catalog
<p>The SKYSURF Project (Windhorst et al. 2022) analyzes the extragalactic background light (both directly using sky background measurements and indirectly using galaxy counts) using the HST Archive. While HST images probe faint galaxies unseen by ground-based imaging, its small field of view prevents it from probing the large-scale structure around its observations.</p> <p>To supplement SKYSURF analysis, we cross-match SKYSURF pointings with SDSS observations able to probe the surrounding large-scale environment (Bhatia et al. 2024). The tables in this database include galaxies brighter than r=22.5 AB mag photometrically identified in SDSS, within +/-5 arcmin around a SKYSURF pointing.</p> <p>The tables in the Object_AB directory include information on all SDSS objects, organized by the HST camera and filter of the central pointing.</p> <p>The tables in the IGL directory include the total galaxy counts and integrated galaxy light (down to AB mag=22.5) for all SDSS objects surrounding a given SKYSURF image.</p>
Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample
<p>Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs. </p>
SDSS Photometric Measurements with Labels
<p>The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, namely the photometric measurements and spectroscopic labels of around 2.8 million objects. The dataset was generated by submitting an SQL query to the DR12 catalog on the SDSS CasJobs site.</p> <p>Each row in the dataset corresponds to an object in the sky. There are 14 columns:</p> <ul> <li>The first two columns contain the right ascension (<strong>ra</strong>) and declination (<strong>dec</strong>) of an object. These two coordinates uniquely determine its position.</li> <li>The third column (<strong>class</strong>) is the spectroscopic class (Star, Galaxy, and Quasar) as determined by expert opinion. This can be the target vector in a classification model.</li> <li>There are 11 columns that we can use as feature vectors. These are the different PSF and Petrosian magnitude and colour measurements: <ul> <li><strong>psfMag_r_w14</strong>: The PSF magnitude measurement in r-band, assuming the object is a point source.</li> <li><strong>psf_u_g_14</strong>: The difference between the PSF magnitude measurement in u-band and the g-band (i.e. the u-g colour), assuming the object is a point source.</li> <li><strong>psf_g_r_14</strong>: The difference between the PSF magnitude measurement in g-band and the r-band (i.e. the g-r colour), assuming the object is a point source.</li> <li><strong>psf_r_i_14</strong>: The difference between the PSF magnitude measurement in r-band and the i-band (i.e. the r-i colour), assuming the object is a point source.</li> <li><strong>psf_i_z_14</strong>: The difference between the PSF magnitude measurement in i-band and the z-band (i.e. the i-z colour), assuming the object is a point source.</li> <li><strong>petroMag_r_w14</strong>: The Petrosian magnitude measurement in r-band, assuming the object is an extended source.</li> <li><strong>petro_u_g_w14</strong>: The difference between the Petrosian magnitude measurement in the u-band and the g-band (i.e. the u-g colour), assuming the object is an extended source.</li> <li><strong>petro_g_r_w14</strong>: The difference between the Petrosian magnitude measurement in the g-band and the r-band (i.e. the g-r colour), assuming the object is an extended source.</li> <li><strong>petro_r_i_w14</strong>: The difference between the Petrosian magnitude measurement in the r-band and the i-band (i.e. the r-i colour), assuming the object is an extended source.</li> <li><strong>petro_i_z_w14</strong>: The difference between the Petrosian magnitude measurement in the i-band and the z-band (i.e. the i-z colour), assuming the object is an extended source.</li> <li><strong>petroRad_r</strong>: The size measurement of the object in r-band in arc seconds.</li> </ul> </li> </ul> <p>The measurements have been corrected for dust extinction (the scattering of light by the galactic dust) using the correction set provided by Wolf (2014). Each feature has also been standardised to have zero mean and unit variance.</p> <p>For the code to generate this dataset, please go to the Github repo: https://github.com/chengsoonong/mclass-sky</p> <p>If you use the SDSS data in your papers, please see here for instructions on how to cite: http://www.sdss.org/collaboration/citing-sdss/</p> <p>Please also cite this upload if you have used this particular pre-processed dataset.</p>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
Extreme Variability Quasars Catalog from SDSS DR16Q
<p>We provide all 20,069 available spectral measurements of 14,012 EVQs selected in <a href="https://doi.org/10.3847/1538-4357/ac3828">Ren et al. 2022</a>. Repeated observed spectra of a same EVQ will have the same SDSS NAME with different spectral info (PLATE,MJD, and FIBER). We include the SPECPRIMARY flag to indicate if the spectrum is the best observation of this object. The unmeasurable parameters are set to -9999. The errors are obtained from 100 iterations of Monte Carlo simulation.</p> <p>We extend my heartfelt gratitude to our Data Editor of the AAS Journal, August Muench, for his invaluable assistance in the data verification and MRT format compilation of this dataset. His expertise and meticulous attention to detail were instrumental in enhancing the quality and accessibility of this work.</p> <p>We note that, in most cases, all measurements in a same line complex would be either all null or all valid, however, there exist some exceptions.</p> <p>In our dataset, there are generally two scenarios lead a measurement to the null value (-9999).</p> <ol> <li>The most frequent case is due to the spectral coverage. When a emission line region is not measureable, we will set the whole relavent value to null.</li> <li>Besides, there are some exceptions that we do have a real fitting but still can not give a reasonable measurement for some specific elements. <ol> <li>For any line component with FLUX==0, we reserve the FLUX and EW of this line to 0 but change the rest measurements (FWHM, PEAK, etc.) to -9999.</li> <li>For double peak broad component, we set the line FWHM/FWQM/FW10M and their corresponding center shifts (Z50/Z25/Z10) to -9999</li> </ol> </li> <li>In addition, since the systematic shift is relatively minor feature of a line, spectra with very low S/N ratio can not well constrained the line shape. User could find a bunch of lines with Z50/Z25/Z10 == 0. Nevertheless, in those cases, their error would be extremely high indicating that measurements could be unreliable.</li> </ol>
The SDSS DR16 / CatWISE2020 Spectro-Photometric Sample
<p>SDSS DR16 sources with clean (ZWARNING == 0) spectra are matched with CatWISE2020 sources and sources meeting the following criteria</p> <p>dered_r > 12 && dered_r < 22 && dered_u != -9999.0 && dered_g != -9999.0 && dered_r != -9999.0 && dered_i != -9999.0 && dered_z != -9999.0 && w1mpro != 9999. && w2mpro != 9999. && w1snr > 5 && w2snr > 5 && blend_ext_flags == 1</p> <p>have been retained. SDSS magnitudes are in the SDSS AB system, where CatWISE2020 magnitudes have been converted from the CatWISE2020 Vega system to the AB system.</p> <p>The "small" and "large" versions contain the very same sample (i.e. rows) but a more limited and more extensive set of properties/features (i.e. columns) respectively.</p>
SDSS Galaxy Subset
<p>The <a href="https://www.sdss.org">Sloan Digital Sky Survey</a> (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 100077 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the <a href="https://github.com/nunorc/astromlp-models">astromlp-models</a> collection of deep learning models for galaxies characterisation.</p> <p>The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):</p> <ul> <li><strong>objid</strong>: unique SDSS object identifier </li> <li><strong>mjd</strong>: MJD of observation</li> <li><strong>plate</strong>: plate identifier</li> <li><strong>tile</strong>: tile identifier</li> <li><strong>fiberid</strong>: fiber identifier</li> <li><strong>run</strong>: run number</li> <li><strong>rerun</strong>: rerun number</li> <li><strong>camcol</strong>: camera column</li> <li><strong>field</strong>: field number</li> <li><strong>ra</strong>: right ascension</li> <li><strong>dec</strong>: declination</li> <li><strong>class</strong>: spectroscopic class (only objetcs with GALAXY are included)</li> <li><strong>subclass</strong>: spectroscopic subclass</li> <li><strong>modelMag_u</strong>: better of DeV/Exp magnitude fit for band u</li> <li><strong>modelMag_g</strong>: better of DeV/Exp magnitude fit for band g</li> <li><strong>modelMag_r</strong>: better of DeV/Exp magnitude fit for band r</li> <li><strong>modelMag_i</strong>: better of DeV/Exp magnitude fit for band i</li> <li><strong>modelMag_z</strong>: better of DeV/Exp magnitude fit for band z</li> <li><strong>redshift</strong>: final redshift from SDSS data z</li> <li><strong>stellarmass</strong>: stellar mass extracted from the <a href="https://www.sdss.org/dr16/spectro/eboss-firefly-value-added-catalog">eBOSS Firefly catalog</a></li> <li><strong>w1mag</strong>: WISE W1 "standard" aperture magnitude</li> <li><strong>w2mag</strong>: WISE W2 "standard" aperture magnitude</li> <li><strong>w3mag</strong>: WISE W3 "standard" aperture magnitude</li> <li><strong>w4mag</strong>: WISE W4 "standard" aperture magnitude</li> <li><strong>gz2c_f</strong>: Galaxy Zoo 2 classification from <a href="https://academic.oup.com/mnras/article/435/4/2835/1022913">Willett et al 2013</a></li> <li><strong>gz2c_s</strong>: simplified version of Galaxy Zoo 2 classification (<a href="https://github.com/nunorc/astromlp-models#galaxy-zoo-2-simplified-classes-gz2c">labels set</a>)</li> </ul> <p>Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the <strong>objid </strong>column from the CSV file, with the corresponding data, the following directories tree is available:</p> <pre><code class="language-bash">sdss-gs/ ├── data.csv ├── fits ├── img ├── spectra └── ssel</code></pre> <p>Where, each directory contains:</p> <ul> <li><strong>img</strong>: RGB images from the object in JPEG format, 150x150 pixels, generated using the <a href="https://skyserver.sdss.org/dr16/en/help/docs/api.aspx">SkyServer DR16 API</a></li> <li><strong>fits</strong>: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the <a href="https://github.com/jhoar/ImageCutter">ImageCutter</a> library</li> <li><strong>spectra</strong>: full best fit spectra data from SDSS between 4000 and 9000 wavelengths</li> <li><strong>ssel</strong>: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by <a href="https://arxiv.org/abs/1003.3186">Sánchez Almeida 2010</a></li> </ul> <p><strong>Changelog</strong></p> <ul> <li>v0.0.4 - Increase number of objects to ~100k.</li> <li>v0.0.3 - Increase number of objects to ~80k.</li> <li>v0.0.2 - Increase number of objects to ~60k.</li> <li>v0.0.1 - Initial import.</li> </ul>
Catalog of X-ray & WISE AGN in the SDSS-IV eBOSS Stripe 82X Survey
<p>This catalog contains 4847 spectroscopically identified X-ray sources and <em>WISE</em> AGN candidates within the 36.8 deg<sup>2</sup> SDSS-IV eBOSS Stripe 82X survey area. This survey overlaps the largest contiguous portion of the Stripe 82 X-ray survey (15.6 deg<sup>2</sup>). Based on X-ray luminosities or <em>WISE</em> <em>W1</em>-<em>W2</em> colors (based on Assef et al. 2018), there are 4730 AGN in this catalog: 1790 X-ray AGN and 3638 <em>WISE</em> AGN, of which 698 are X-ray and <em>WISE</em> AGN. The sample is 82% complete to <em>r</em> ~ 22, where the X-ray and <em>WISE</em> AGN samples are 88% and 82% complete, respectively, at this magnitude limit.</p> <p>The redshifts and spectroscopic classifications include spectra from the SDSS-IV eBOSS Stripe 82X survey, previous data releases of SDSS (Abazajian et al. 2009; Aihara et al. 201; Alam et al. 2015; Albareti et al. 2017; Abolfathi et al. 2019; Pâris et al. 2017, 2018), 2SLAQ (Croom et al. 2009), 6dF (Jones et al. 2004, 2009), and dedicated follow-up programs of Stripe 82 X-ray sources (LaMassa et al. 2016, 2017, and published here for the first time). Description of the catalog columns are given in LaMassa et al. (2019). </p>
APOGEE-Kepler Catalog SDSS Internal Version
<p>This represents the publication of the SDSS internal version of the APOGEE-Kepler Catalog (v.7.4.0, August 2024), previous versions of which have been available to SDSS-IV collaboration members on the internal wiki page https://trac.sdss.org/wiki/APOGEE2/APOKASC/Catalog and have been used for a variety of projects. It has been designed to include all stars in the Kepler field which had APOGEE spectra at the end of SDSS-IV (DR17) and as such is not limited to giants or seismic oscillators. For giant oscillators, this catalog contains a subset of the information available in all of the tables available with the journal edition of the APOKASC-3 catalog (Pinsonneault et al., 2024), as well as some additional information including matches to external datasets that may be of interest. This table also contains some historical information, previous versions of some values, and so forth that were used for comparison and validation. The header_APOKASC_cat_v7.4.0.txt file contains a list of the available columns as well as some descriptions of where the values are from. These values represent effort from a variety of people including those involved in the APOKASC collaboration as well as related individual efforts and anyone using this data is strongly encouraged to cite the original published version. In all cases where the values in this file and the values in the journal disagree, the journal values should be viewed as the correct version of record. The fits and ascii versions of the APOKASC catalog are the same, and both are provided for ease of use. </p>
CIV absorption catalog in SDSS DR12
<p>For each sight-line, identified by Column 1 and 2, we report the absorber’s redshift (Column 3), column density in log(cm −2 ) (Column 4), Doppler velocity dispersion in km s −1 (Column 5), rest equivalent width for 1548 Å W𝑟 ,1548 (Column 6), rest equivalent width for 1550 Å W𝑟 ,1550 (Column 7), the posterior probability of the C iv absorber P(M𝐷 ) (Column 8), and the posterior probability of the singlet absorber P(M𝑆 ) (Column 9). We show only absorbers with P(M𝐷 )≠NaN. This table demonstrates a portion of the full table for the first ten rows. Note that those measurements with large errors are uncertain (i.e. low absorption model posterior probability).</p>
Empirical Stellar Templates Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>This is the dataset of empirical stellar templates associated with the publication "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra", which has been accepted for publication in the Astrophysical Journal Supplements. </p>
SDSS DR72 Large Scale Structure random catalog
<p>This is a catalog of random points in the footprint of the Sloan Digitial Sky Survey (specifically the NYU-VAGC) used for defining the angular mask of the survey. This dataset was originally created/hosted by Michael Blanton at NYU as part of the Sloan Digital Sky Survey.</p>
SDSS DR8 with Petrosian Errors
<p>SELECT<br> G.ra, G.dec, S.mjd, S.plate, S.fiberID, S.class, S.z, S.zErr, S.rChi2, S.velDisp, S.velDispErr,<br> G.extinction_u, G.extinction_g, G.extinction_r, G.extinction_i, G.extinction_z,<br> G.fiberMag_u, G.fiberMag_g, G.fiberMag_r, G.fiberMag_i, G.fiberMag_z, <br> G.modelMag_u, G.modelMag_g, G.modelMag_r, G.modelMag_i, G.modelMag_z, <br> G.cmodelMag_u, G.cmodelMag_g, G.cmodelMag_r, G.cmodelMag_i, G.cmodelMag_z,<br> phioffset_r, G.fracdeV_r, <br> G.deVRad_r, G.deVRadErr_r, G.deVAB_r, G.deVABErr_r, deVPhi_r, G.lnLDeV_r, G.deVMag_r, G.deVMagErr_r, <br> G.expRad_r, G.expRadErr_r, G.expAB_r, G.expABErr_r, expPhi_r, G.lnLExp_r, G.expMag_r, G.expMagErr_r, <br> G.petroMag_u, G.petroMag_g, G.petroMag_r, G.petroMag_i, G.petroMag_z, <br>G.petroMagErr_u, G.petroMagErr_g, G.petroMagErr_r, G.petroMagErr_i, G.petroMagErr_z, <br> G.petroRad_u, G.petroRad_g, G.petroRad_r, G.petroRad_i, G.petroRad_z, G.petroR50_r, G.petroR90_r, G.petroR50Err_r, G.petroR90Err_r, <br> GSL.h_alpha_flux, GSL.h_alpha_flux_err, GSL.oiii_5007_flux, GSL.oiii_5007_flux_err,<br> GSX.d4000, GSX.d4000_err, <br> GSE.bptclass, GSE.lgm_tot_p50, GSE.sfr_tot_p50, G.objID, <br> GSI.specObjID<br>INTO mydb.sdss_specgal_dr8 FROM SpecObj S CROSS APPLY<br> dbo.fGetNearestObjEQ(S.ra, S.dec, 0.06) N, Galaxy G,<br> GalSpecInfo GSI, GalSpecLine GSL, GalSpecIndx GSX, GalSpecExtra GSE<br>WHERE N.objID = G.objID<br> AND GSI.specObjID = S.specObjID<br> AND GSL.specObjID = S.specObjID<br> AND GSX.specObjID = S.specObjID<br> AND GSE.specObjID = S.specObjID<br> AND (G.petroMag_r > 10 AND G.petroMag_r < 18)<br> AND (G.modelMag_u-G.modelMag_r) > 0<br> AND (G.modelMag_u-G.modelMag_r) < 6<br> AND (modelMag_u > 10 AND modelMag_u < 25)<br> AND (modelMag_g > 10 AND modelMag_g < 25)<br> AND (modelMag_r > 10 AND modelMag_r < 25)<br> AND (modelMag_i > 10 AND modelMag_i < 25)<br> AND (modelMag_z > 10 AND modelMag_z < 25)<br> AND S.rChi2 < 2<br> AND (S.zErr > 0 AND S.zErr < 0.01)<br> AND S.z > 0.0</p>
Model Fit Figures for: The 100 pc White Dwarf Sample in the SDSS Footprint II. A New Look at the Spectral Evolution of White Dwarfs
<p>Figures for the model atmosphere fits to the spectroscopically confirmed white dwarfs in the 100 pc sample and the SDSS footprint (<span>arXiv:2412.04611). </span></p>
Keck and SDSS optical spectra of J0919+2720.
<p>This repository contains the Keck/LRIS and SDSS optical spectra of J0919+2720. See the attached README.txt file for details. </p>
Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra
<p>The Paper: <a href="https://arxiv.org/abs/1909.10963">https://arxiv.org/abs/1909.10963</a></p> <p>Abstract: We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model.</p> <p>When using this dataset, please reference our paper via the journal (<a href="https://arxiv.org/abs/1909.10963">https://arxiv.org/abs/1909.10963</a>) and this DOI (10.5281/zenodo.3459293). If you make use of our scripts please reference our Github repository DOI (10.5281/zenodo.3855160).</p> <p>File descriptions:</p> <p>All of these files are Pandas Dataframes, saved as pickle files. df_spec_classprobs.pkl contains the spectroscopically observed sources used for training and testing. This has been cleaned, and has the results of the random forest classifier added as additional columns (sources used for training have NaNs in the class_pred column). SDSS-ML-all contains the 111 million photometrically observed sources, with our class labels and probabilities added. SDSS-ML-galaxies/quasars/stars is the same file broken up by assigned class for convenience.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.