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65 results for “redshift”
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
X-rays across the galaxy population: The distribution of AGN accretion rates as a function of stellar mass and redshift
<p>We provide measurements of the probability distribution function of specific black hole accretion rates within a sample of galaxies of a given stellar mass and redshift, <span class="math-tex">\(p(\log \lambda_{sBHAR} | M_*,z)\)</span>. Measurements are provided for all galaxies, star-forming galaxies and quiescent galaxies. We also provide estimates of the AGN duty cycle, <span class="math-tex">\(f(\lambda_{sBHAR} >0.01)\)</span> i.e. the fraction of galaxies with an AGN above a given limit in specific accretion rate, based on the probability distribution functions. Full details are provided in Aird et al. (2018, MNRAS, 474, 1225); please cite this publication if you use these measurements. </p>
pop-cosmos: Galaxy property and redshift catalog for COSMOS2020
<p>This record (v≥2.0.0) contains data products associated with the paper "<em>pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population</em>" by Thorp et al. (2025). Earlier versions of this record (v<2.0.0) contain data products associated with the paper "<em>pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model</em>" by Thorp et al. (2024), which are superseded by the contents of v2.0.0. In v≥2.0.0, we include results for all COSMOS2020 galaxies with $\textit{Ch.1}<26$ or $r<25$.</p> <p>The included products are derived from spectral energy distribution (SED) fits to 26-band COSMOS2020 photometry, using the 16-parameter SPS model described in Thorp et al. (2024, 2025), and the <code>pop-cosmos</code> prior from Thorp et al. (2025). All results are based on Markov Chain Monte Carlo (MCMC) runs using the configuration described in Thorp et al. (2024, 2025). Results correspond to v2.1 of the COSMOS2020 catalog.</p> <p>The current release includes the following files:</p> <ul> <li><strong>README_v2_2_0.txt</strong>: Detailed information about how to read the other files in the record.</li> <li><strong>mcmc_summaries.h5.gz</strong>: Zipped HDF5 file with summaries (percentiles) of the posteriors.</li> <li><strong>mcmc_samples_pop_cosmos.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>pop-cosmos</code> prior).</li> <li><strong>mcmc_samples_Prospector.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>Prospector</code>-$\alpha$ prior).</li> </ul> <p>If you make use of any of these products, please cite this repository and Thorp et al. (2024, 2025). Please also cite the <code>pop-cosmos</code> overview paper by Alsing et al. (2024), and the paper by Deger et al. (2025). If you make use of any COSMOS data products, please cite Weaver et al. (2022) and any other relevant publications. If you make use of COSMOS spectroscopic data, please cite Khostovan et al. (2025) and references therein.</p> <p>If you spot any issues or have any requests, please contact the corresponding author (Stephen Thorp) using the details in the README.</p> <p>If you want to access <code>pop-cosmos</code> mock galaxy catalogs, these can be found on <a href="https://doi.org/10.5281/zenodo.15622324">Zenodo</a>.</p> <p>Related software, including a demo notebook for working with the data in this record, can be found on <a href="https://github.com/Cosmo-Pop/pop-cosmos">GitHub</a>. </p> <p>References:</p> <ol> <li>Alsing et al. (2024). ApJS 274, 12. [<a href="https://arxiv.org/abs/2402.00935">arXiv:2402.00935</a>][<a href="https://doi.org/10.3847/1538-4365/ad5c69">doi</a>]</li> <li>Deger et al. (2025). MNRAS, submitted. [<a href="https://arxiv.org/abs/2509.20430">arXiv:2509.20430</a>]</li> <li>Khostovan et al. (2025). ApJ, submitted. [<a href="https://arxiv.org/abs/2503.00120">arXiv:2503.00120</a>]</li> <li>Thorp et al. (2024). ApJ 975, 145. [<a href="https://arxiv.org/abs/2406.19437">arXiv:2406.19437</a>][<a href="https://doi.org/10.3847/1538-4357/ad7736">doi</a>]</li> <li>Thorp et al. (2025). ApJ, accepted. [<a href="https://arxiv.org/abs/2506.12122">arXiv:2506.12122</a>]</li> <li>Weaver et al. (2022). ApJS 258, 11. [<a href="https://arxiv.org/abs/2110.13923">arXiv:2110.13923</a>][<a href="https://doi.org/10.3847/1538-4365/ac3078">doi</a>]</li> </ol>
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
The [CII] 158 μm line emission in high-redshift galaxies: Data Set
<p>This data set contains all data tables associated to the publication: "The [CII] 158 μm line emission in high-redshift galaxies"; A&A Lagache, Cousin, Chatzikos 2018. Please cite it if you use those data.</p>
CO excitation, molecular gas density and interstellar radiation field in local and high-redshift galaxies
<p>This dataset includes the SED fitting figures and full sample table produced in the study of Liu et al. (2020, ApJ). Two example figures are shown in the Figure 1 of the paper. And selected columns of the full sample table is shown in the Table 1 of the paper. </p> <p> </p>
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3
<p>Review of redshift values of bright AGNs with hard spectra in 4LAC catalog</p> <p>FITS table and description file (pdf)</p>
Catalogue of Bayesian SZNet's spectroscopic redshift predictions
<p>The "dr16q_superset_redshift.csv" file provides a catalogue of spectroscopic redshift predictions for spectra from the <a href="https://www.sdss.org/dr16/algorithms/qso_catalog/">16th data release of the Sloan Digital Sky Survey (SDSS) quasar superset catalogue</a> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>. Redshifts are predicted by a Bayesian convolutional neural network named Bayesian SZNet with associated predictive uncertainties in the form of predictive variances. The catalogue is released in the CSV format with the following columns:</p> <ul> <li><em>plate</em>: spectroscopic plate number;</li> <li><em>mjd</em>: modified Julian day of the spectroscopic observation;</li> <li><em>fiberid</em>: fiber identification number;</li> <li><em>z_pred</em>: redshift from Bayesian SZNet;</li> <li><em>variance</em>: predictive variance associated with redshift from Bayesian SZNet;</li> <li><em>z</em>: primary redshift;</li> <li><em>source</em><em>_z</em>: origin of the reported redshift in <em>z;</em></li> <li><em>is_qso_final</em>: flag indicating quasars included in the DR16Q <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>;</li> <li><em>z_vi</em>: redshift from visual inspection;</li> <li><em>z_pipe</em>: redshift from the SDSS pipeline;</li> <li><em>zwarning</em>: quality flag on the redshift from the SDSS pipeline;</li> <li><em>z_dr12q</em>: redshift from the DR12Q catalogue <a href="http://ui.adsabs.harvard.edu/abs/2017A%26A...597A..79P/abstract">(Pâris et al., 2017)</a>;</li> <li><em>z_dr7q_sch</em>: redshift from the DR7Q catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010AJ....139.2360S/abstract">(Schneider et al., 2010)</a>;</li> <li><em>z_dr6q_hw</em>: redshift from the DR6 catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.2302H/abstract">(Hewett and Wild, 2010)</a>;</li> <li><em>z_10k</em>: redshift from the random visual inspection of 10000 spectra in the DR16Q superset;</li> <li><em>z_pca</em>: redshift from the <a href="https://ascl.net/2106.017">redvsblue algorithm</a>;</li> <li><em>z_qn</em>: redshift from QuasarNET <a href="https://arxiv.org/abs/1808.09955">(Busca and Balland, 2018)</a>;</li> <li><em>z_pred_1</em> to <em>z_pred_256</em>: sampled redshifts from Bayesian SZNet;</li> </ul> <p>where columns <em>z</em>, <em>source_z</em>, <em>is_qso_final</em>, <em>z_vi</em>, <em>z_pipe</em>, <em>zwarning</em>, <em>z_dr12q</em>, <em>z_dr7q_sch</em>, <em>z_dr6q_hw</em>, <em>z_10k</em>, <em>z_pca</em>, and <em>z_qn</em> are taken from the 16th data release of the SDSS quasar superset catalogue.</p>
Photometric Redshifts for Cosmology: Improving accuracy and uncertainty estimates using Bayesian Neural Networks
<p><strong>This data consists of 286,401 with broad-band g,r,i,z,y photometry from the HSC DR2 survey and spectroscopic redshifts. The majority of galaxies in our sample lies between redshift of 0.01 and 2.5</strong></p>
Photometric Galaxy Redshift Prediction
<h3>Sloan Digital Sky Survey (SDSS) Galaxy Redshift Dataset</h3> <p>This dataset comprises a curated collection of galaxy observations from the Sloan Digital Sky Survey (SDSS). It features photometric and spectroscopic data for 100 galaxies, specifically selected to cover a range of redshifts from 0 to 0.4. The dataset includes the following key parameters for each galaxy:</p> <ul> <li><strong>Photometric Data</strong>: Magnitudes in the SDSS 'u', 'g', 'r', 'i', and 'z' bands.</li> <li><strong>Spectroscopic Data</strong>: Measured redshift (<code>redshift</code>) and its error (<code>redshift_error</code>).</li> <li><strong>Additional Metadata</strong>: <ul> <li><code>objid</code>: Unique identifier for the photometric object.</li> <li><code>specObjID</code>: Unique identifier for the spectroscopic object.</li> <li><code>ra</code>: Right ascension in decimal degrees.</li> <li><code>dec</code>: Declination in decimal degrees.</li> <li><code>class</code>: Classification of the object, all marked as 'GALAXY'.</li> </ul> </li> </ul> <h3>Purpose and Use</h3> <p>This dataset is intended for use in astronomical research and education, particularly in studies involving galaxy properties and distribution, cosmology, and machine learning applications such as redshift prediction models. The data is well-suited for developing and testing predictive models that estimate redshifts from photometric data, aiding in the expansion of accessible astronomical analysis tools.</p> <h3>Data Collection Method</h3> <p>The data was extracted using SQL queries against the public SDSS DR16 database, ensuring accuracy and relevance in current astronomical research contexts.</p> <h3>Accessibility</h3> <p>The dataset is made available under a CC0 license to promote open scientific research and collaboration within the astronomical community and beyond.</p>
Euclid Early Release Observations of Abell 2390 & 2764: NISP-selected photometry and photometric redshift catalogue
<p><strong>Euclid Early Release Observations 'Magnifying Lens' (PI: Atek)</strong><br><strong>Elementary Photometric Catalogues and Photometric Redshifts</strong></p> <p>Version 1.0.0</p> <p>Authors: J. Weaver (UMass), S. Taamoli (UCR), and H. Atek (IAP)<br>Contact: john.weaver.astro@gmail.com</p> <p>If you use this work, please cite:</p> <p>Survey Paper: Atek et al. 2024<br>Processing Paper: Cuillandre et al. 2024<br>Demonstration Paper: Weaver et al. 2024</p> <p>While photometric redshfits are provided, we caution that they are derived from only four optical-NIR bands and are designed for high-z dropout galaxies. Users may encounter issues at lower redshifts.</p> <p>These files are provided 'as is'. We the authors retain the right to modify the files at any time.</p> <p>Please see the dedicated README file for details.</p>
Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift
<p>Database of catalogs and numerical results for the paper: Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift.</p> <p> </p> <p>ABSTRACT</p> <p>Hierarchical black hole (BH) mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution. <br>We find that the evolution of the host GC quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for $m_1 > 50 \, \msun$ regardless of the assumed BH pairing function. <br>At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density. <br>We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with $m_1 \geq 30 \, \msun$ for $z\geq{}2$.<br>Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very <br>sensitive to the spins, which favor a large fraction ($>0.6$) of BBHs born in GCs in order to reproduce misaligned spin observations.</p> <p> </p> <p>FILES DESCRIPTION:</p> <p>Files Catalogs.zip contain the data used in this paper. </p> <p>The directory Metallicities contains the outputs of the Fastcluster runs at Z=0.0002. For each model and for each GC evolutionary case, we report the populations of BBHs at first ("first_generation.csv") and nth ("nth_generation.csv") generation. </p> <p>The directory Merger_Rate_Density contains the catalogs from Cosmorate+Fastcluster at redshift 0 to 4 ("redshift_*.csv") and the merger rate density as a funcion of redshift ("merger_rate_density.csv"), for different GC models. Also, it contains the mixing fractions for all the models presented in this paper ("mixing_fractions.csv").</p> <p>The Jupyter notebooks generate the Figures in the main body of the paper. </p>
high redshift (z~6) quasars (ESI)
<p>This data set contains 34 quasars at high redshift, i.e. 5.77<z<6.54, that were taken with the Echellette Spectrograph and Imager (ESI) at Keck II. The data was taken between the years of 2002 and 2016 and in most cases multiple exposures have been co-added to increase the S/N. The data release is described in Eilers et al. 2018. </p> <p>All spectra are reduced, flux-calibrated, co-added and continuum fitted. Each file contains four columns for wavelength, flux, noise, and the continuum estimate.</p> <p>If the data is being used, please cite Eilers et al. 2018 (https://ui.adsabs.harvard.edu//#abs/2018ApJ...864...53E/).</p>
XMM2ATHENA: Catalogue of photometric redshifts for 4XMM-DR12
<p>We provide photometric redshift estimations for 4XMM sources having optical counterparts (in the SDSS, PanSTARRS or SkyMapper surveys for 4XMM-DR12), identified as AGN in the classification catalogue, and outside the galactic plane.</p> <p>For more details, visit the <a href="http://xmm-ssc.irap.omp.eu/xmm2athena/catalogues/">XMM2ATHENA project site</a>.</p>
DESI EDR qsofit systematic redshift
<p>The dataset includes improved systematic redshift for quasars in DESI EDR computed from the spectral decompositions.</p>
DESI Complete Calibration of the Color-Redshift Relation (DC3R2): Results from early DESI data
<p>The data and python code used to reproduce the figures in the paper of the same name, J. McCullough et al.</p>
HIGH-REDSHIFT NARROW LINE SEYFERT 1 GALAXIES: A CANDIDATE SAMPLE
<p>The study of Narrow line Seyfert 1 galaxies (NLS1s) is now mostly limited to low redshift (z < 0.8). This is because their definition requires the presence of the Hβ emission line, which is red-shifted out of the spectral coverage of major ground-based spectroscopic surveys at z > 0.8. We studied the correlation between the properties of Hβ and Mg II lines of a large sample of SDSS DR14 quasars to find high-z NLS1 candidates. Based on the strong correlation of FWHM(MgII)=(0.880±0.005)×FWHM(Hβ)+(0.438±0.018), we present a sample of high-z NLS1 candidates having FWHM of Mg II < 2000 km s−1. The high-z sample contains 2684 NLS1s with redshift z = 0.8 − 2.5 with a median logarithmic bolometric luminosity of 46.16 ± 0.42 erg s−1, logarithmic black hole mass of 8.01 ± 0.35M⊙, and logarithmic Eddington ratio of 0.02 ± 0.27. The fraction of radio-detected high-z NLS1s is similar to that of the low-z NLS1s and SDSS DR14 quasars at a similar redshift range and their radio luminosity is found to be strongly correlated with their black hole mass.</p> <p>This page contains the catalog of high-z NLS1 candidates. </p>
HETDEX-LOFAR Spectroscopic Redshift Catalog
<p>We combine the power of blind integral field spectroscopy from the Hobby-Eberly Telescope (HET) Dark Energy Experiment (HETDEX) with sources detected by the Low Frequency Array (LOFAR) to construct the HETDEX-LOFAR Spectroscopic Redshift Catalog. Starting from the first data release of the LOFAR Two-metre Sky Survey (LoTSS), including a value-added catalog with photometric redshifts, we extracted 28,705 HETDEX spectra. Using an automatic classifying algorithm, we assigned each object a star, galaxy, or quasar label along with a velocity/redshift, with supplemental classifications coming from the continuum and emission line catalogs of the internal, fourth data release from HETDEX (HDR4). We measured 9,087 new redshifts; in combination with the value-added catalog, our final spectroscopic redshift sample is 9,710 sources. This new catalog contains the highest substantial fraction of LOFAR galaxies with spectroscopic redshift information; it improves archival spectroscopic redshifts, and facilitates research to determine the [O II] emission properties of radio galaxies from 0.0 < z < 0.5, and the Lyα emission characteristics of both radio galaxies and quasars from 1.9 < z < 3.5. Additionally, by combining the unique properties of LOFAR and HETDEX, we are able to measure star formation rates (SFR) and stellar masses. Using the Visible Integral-field Replicable Unit Spectrograph (VIRUS), we measure the emission lines of [O III], [Ne III], and [O II] and evaluate line-ratio diagnostics to determine whether the emission from these galaxies is dominated by AGN or star formation.</p> <p> </p> <p>The catalog is comprised of one FITS file with two extensions:</p> <ul> <li>No. 0 (1036, 28705): The first extension contains the spectrum, of length 1036, for each object. The corresponding wavelength values range from 3470 - 5540 angstroms.</li> <li>No. 1 (28705 rows x 13 columns): The second extension contains the derived values found for each source. The table below describes each column: <table> <tbody> <tr> <td><strong>COLUMN NAME</strong></td> <td><strong>DESCRIPTION</strong></td> <td><strong>DATA TYPE</strong></td> </tr> <tr> <td>objID</td> <td>LoTSS Object ID</td> <td>string</td> </tr> <tr> <td>source_name</td> <td>Source Name</td> <td>string</td> </tr> <tr> <td>RA</td> <td>PanSTARRS1 Right Ascension (J2000)</td> <td>float</td> </tr> <tr> <td>Dec</td> <td>PanSTARRS1 Declination (J2000)</td> <td>float</td> </tr> <tr> <td>z_diagnose</td> <td>Best fit redshift from Diagnose</td> <td>float</td> </tr> <tr> <td>z_hdr4</td> <td>Best fit redshift from ELiXer</td> <td>float</td> </tr> <tr> <td>z_archive</td> <td>Spectroscopic redshift from value-added LoTSS catalog</td> <td>float</td> </tr> <tr> <td>z_best</td> <td>HETDEX-LOFAR redshift</td> <td>float</td> </tr> <tr> <td>z_best_src</td> <td>1 = Diagnose, 2 = HDR4, 3 = Archive</td> <td>integer</td> </tr> <tr> <td>classification</td> <td>STAR, AGN, LOWZGAL, HIGHZGAL, or ARCHIVE</td> <td>string</td> </tr> <tr> <td>log_mass</td> <td>MCSED derived stellar mass</td> <td>float</td> </tr> <tr> <td>log_SFR</td> <td>MCSED derived star formation rate</td> <td>float</td> </tr> <tr> <td>log_L150</td> <td>MCSED derived 150 MHz luminosity</td> <td>float</td> </tr> </tbody> </table> </li> </ul> <p> </p> <p>We request that the following acknowledgement be included in any paper using HETDEX-LOFAR data:</p> <blockquote> <p>HETDEX is led by the University of Texas at Austin McDonald Observatory and Department of Astronomy with participation from the Ludwig-Maximilians-Universität München, Max-Planck-Institut für Extraterrestrische Physik (MPE), Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Pennsylvania State University, Institut für Astrophysik Göttingen, The University of Oxford, Max-Planck-Institut für Astrophysik (MPA), The University of Tokyo and Missouri University of Science and Technology.</p> <p>Observations for HETDEX were obtained with the Hobby-Eberly Telescope (HET), which is a joint project of the University of Texas at Austin, the Pennsylvania State University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen. The HET is named in honor of its principal benefactors, William P. Hobby and Robert E. Eberly. The Visible Integral-field Replicable Unit Spectrograph (VIRUS) was used for HETDEX observations. VIRUS is a joint project of the University of Texas at Austin, Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University, Max-Planck-Institut fürExtraterrestrische Physik (MPE), Ludwig-Maximilians-Universität München, Pennsylvania State University, Institut für Astrophysik Göttingen, University of Oxford, and the Max-Planck-Institut fur Astrophysik (MPA).</p> <p>Funding for HETDEX has been provided by the partner institutions, the National Science Foundation, the State of Texas, the US Air Force, and by generous support from private individuals and foundations.</p> </blockquote> <p>We request that the following papers are cited when using any HETDEX-LOFAR data:</p> <p>Debski, M. H., Zeimann, G. R., Hill, G. J., et al. 2024, arXiv e-prints, arXiv:2411.08974, doi: 10.48550/arXiv.2411.08974</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.