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45 results for “Survey of galaxies”
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>
Data for "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection"
<p>The data are the full versions of tables that will be published in the manuscript titled "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection" by The Astrophysical Journal Supplement Series.</p> <p>The table file named "FIRST_bt_table1.csv" is the full table for "A catalog of 4876 BTRGs identified from VLA FIRST survey". </p> <p>The table file named "FIRST_bt_table2.csv" is the full table for "Cluster details for BTRGs". </p>
SURVEY OF IONIZED GAS OF THE GALAXY, MADE WITH THE ARECIBO TELESCOPE (SIGGMA): INNER GALAXY DATA RELEASE
<p>The Survey of Ionized Gas of the Galaxy, Made with the Arecibo telescope (SIGGMA) provides a fully-sampled view of the radio recombination line (RRL) emission from the portion of the Galactic plane visible by Arecibo. Observations use the Arecibo L-band Feed Array (ALFA), which has a FWHM beam size of 3 0 .4. Twelve hydrogen RRLs from H163α to H174α are located within the<br> instantaneous bandpass from 1225 MHz to 1525 MHz. We provide here cubes of average (“stacked”) RRL emission for the inner Galaxy region 32 ◦ ≤ ` ≤ 70 ◦ , |b| ≤ 1.5 ◦ , with an angular resolution of 6 0 . The stacked RRL rms at 5.1 km s<sup>−1</sup> velocity resolution is ∼ 0.65 mJy beam<sup>−1</sup> , making this the most sensitive large-scale fully-sampled RRL survey extant. We use SIGGMA data to catalogue 319 RRL detections in the direction of 244 known H ii regions, and 108 new detections in the direction of 79 HII region candidates. We identify 11 Carbon RRL emission regions, all of which are spatially coincident with known H ii regions. We detect RRL emission in the direction of 14 of the 32 supernova remnants (SNRs) found in the survey area. </p>
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
<p>We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection".</p> <p>A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.</p> <p> </p> <p><strong>DATA:</strong></p> <p><strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class).</p> <p><strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class).</p> <p>SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5</p> <p>DeCALS: decals.zip</p> <p><strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field.</p> <p>SDSS (wide filed): the same data as in 2)</p> <p>SDSS (Strip 82 deep field): sdss_stripe82.zip</p> <p>All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .</p>
Digital Assets for "Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey"
<p>These are morphological catalogs and trained <a href="https://github.com/aritraghsh09/GaMPEN">GaMPEN</a> models for Hyper Suprime-Cam galaxies. Please refer to <a href="https://gampen.readthedocs.io/en/latest/Public_data.html">https://gampen.readthedocs.io/en/latest/Public_data.html</a> and <a href="https://arxiv.org/abs/2212.00051">https://arxiv.org/abs/2212.00051</a> for more details about this data release. </p> <p> </p> <p><strong>Catalog Files</strong></p> <ol> <li>g_0_025_preds_summary.csv --> Structural parameter catalog for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_preds_summary.csv --> Structural parameter catalog for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_preds_summary.csv --> Structural parameter catalog for 0.50 < z < 0.75 HSC i-band galaxies </li> </ol> <p> </p> <p><strong>Trained PyTorch Model Files</strong></p> <ol> <li>g_0_025_real_data.pt --> Trained Model for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_real_data.pt --> Trained Model for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_real_data.pt --> Trained Model for 0.50 < z < 0.75 HSC i-band galaxies </li> <li>sim_g_0_025.pt --> Trained Model for Simulated z < 0.25 HSC g-band galaxies </li> <li>sim_r_025_050.pt --> Trained Model for Simulated 0.25 < z < 0.50 HSC r-band galaxies </li> <li>sim_i_050_075.pt --> Trained Model for Simulated 0.50 < z < 0.75 HSC i-band galaxies </li> </ol>
Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys
<p>This repository contains the data released in the paper "Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology measurements for bright (<em>r </em>< 19) galaxies in the DESI Legacy Imaging Surveys footprint. These measurements estimate the presence of bars, spirals arms, ongoing mergers, and more.</p> <p>---</p> <p><strong>GZ DESI Detailed Morphology Catalogs</strong></p> <p>These catalogs are created by training deep learning models on Galaxy Zoo volunteer responses, to predict what volunteers might say for new galaxies. The models are available at [www.github.com/mwalmsley/zoobot](www.github.com/mwalmsley/zoobot). Our measurements are predicted vote fractions i.e. the fraction of volunteers expected to select a given answer for a given question.</p> <p>We share two catalog versions containing the same morphology measurements but presented in different ways.</p> <p>gz_desi_deep_learning_catalog_friendly.parquet contains the morphology measurements</p> <p>gz_desi_deep_learning_catalog_advanced.parquet contains the same measurements, and additional information:</p> <p>- _friendly includes only relevant vote fractions, defined as vote fractions to answers of questions that a majority of volunteers would have been asked. This removes predicted vote fractions for e.g. the fraction of volunteers answering "2 spiral arms" to a galaxy with no spiral arms. _advanced includes all vote fractions and instead reports the (column "proportion_asked"). The user must select which vote fractions they consider relevant (we suggest proportion_asked > 0.5, which recovers the _friendly fractions).</p> <p>- _advanced includes columns with estimated credible intervals (error bars) around each vote fraction. These are calculated from the vote fraction posterior predicted by our models.</p> <p>Finally, we separately present volunteer votes collected for 96k galaxies during the GZD-8 campaign, i.e. after the release of GZ DECaLS but before this (GZ DESI) release. These are split into the _core and _extended catalogs, where _extended includes galaxies which received five or more votes for "artifact". The models above were trained on these votes as well as votes from GZ DECaLS.</p> <p>---</p> <p><strong>External Catalog</strong></p> <p>For convenience, we also include an additional catalog of non-morphology measurements created by other authors (external_catalog.parquet) cross-matched to our morphology catalogs. Please credit those authors if you use this catalog (references are in the GZ DESI paper).</p> <p>A particularly important external measurement is redshift. Morphology is increasingly hard to resolve at higher redshift and so <strong>distant galaxies appear less featured</strong>. external_catalog.parquet includes the column "redshift", which is the SDSS spectroscopic redshift where available and a photometric redshift estimate otherwise (again, see the GZ DESI paper for references and credit). You may want to select only galaxies at lower redshifts.</p> <p>---</p> <p><strong>Data Notes</strong></p> <p>Parquet is a fast csv-like format which can be read with pd.read_parquet(loc, columns=[some columns]). Parquet files are read column-by-column (rather than row-by-row) and so you can chose which columns to load. You can easily check which columns are available using columns=['foo'] and reading the error message. We suggest loading only the columns you need when working with the larger catalogs. This will require much less memory than loading every column.</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 to follow on publication) when using the data in this repository.</p> <p>---</p> <p><strong>History</strong></p> <p>v0.0.1 - closed pre-release for internal review</p> <p>v1.0.0 - draft public release. Removed low-z pre-filtered catalogs.</p> <p>v1.0.1 - first public release. Added .csv version of _friendly catalog. Tweaked catalog formatting for clarity and consistency.</p>
JWST spectrum of galaxy COSMOS-11142 from the Blue Jay survey.
<p>Spectroscopic and photometric data for galaxy COSMOS-11142, studied in Belli et al. (2024).</p> <ul> <li>The JWST/NIRSpec spectroscopy is stored as a FITS table which includes wavelength (in angstrom), calibrated flux, uncertainty, and best-fit model (in erg/(s cm2 A)).</li> <li>The JWST and HST photometry is stored as a FITS table which includes the name of each filter, the effective wavelength (in angstrom), the observed flux and its uncertainty (in microJy).</li> </ul>
JWST Advanced Deep Extragalactic Survey: Thumbnail Images of Spectroscopically-Confirmed Galaxies at z>10.
<p>JWST NIRCam FITS image thumbnails for the spectroscopically-confirmed high-redshift galaxy sample in Robertson et al., arXiv:2212.04480. The tar file expands into the following directories:</p> <p>JADES-GS-z10-0/</p> <p>JADES-GS-z11-0/</p> <p>JADES-GS-z12-0/</p> <p>JADES-GS-z13-0/</p> <p>Each directory contains the F090W, F115W, F150W, F200W, F277W, F335M, F356W, F410M, and F444W FITS images of the galaxies. The FITS headers contain information about the observations, the units of the data, and the locations of the objects on the sky.</p>
Online Data: Evolution of dusty quiescent galaxies over the last six billion years from the hCOSMOS survey
<p>The dataset contains the physical and structural parameters of 545 dusty quiescent galaxies presented in Donevski et al. (2023, A&A accepted, arXiv:2304.05842). The galaxies are observed at intermediate redshifts (0.1 < z < 0.6) as part of the hCOSMOS spectroscopic survey. The dataset contains the physical parameters (redshift, D4000, gas-phase metallicities) estimated from optical spectra, as well as SED-derived specific dust masses and stellar masses. </p>
The CluMPR Galaxy Cluster Catalogue for DESI Legacy Survey DR9
<p>Galaxy cluster catalog and cluster member galaxy catalogs compiled using the CluMPR cluster-finding algorithm. </p> <p>Paper decribing the CluMPR algorithms and cluster catalogs: The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster catalogue (M. J. Yantovski-Barth et al.)</p> <p>File description: </p> <p>DESI_clusters_2024_simple contains the official cluster catalog,</p> <p>DESI_clusters_2024_extended is the official cluster catalog + some clusters which were flagged and removed,</p> <p>north_members_reweighted contains the member galaxies for clusters in the north region of DESI Legacy Survey,</p> <p>south_members_reweighted contains the member galaxies for clusters in the south region of DESI Legacy Survey.</p>
The TELPERION Survey for Extended Emission Regions around AGN: a strongly-interacting and merging galaxy sample
<p>FITS files of narrowband and broadband images used in the search of the TELPERION merging-galaxy sample for AGN-ionized extended [O III] clouds, and confirming longslit spectrum as described in the manuscript. A full list of observation sites and dates, and guide to file names, are in the file 000Readme.txt</p> <p>Analysis is in the paper of the same title submitted to the Monthly Notices of the Royal Astronomical Society.</p> <p> </p>
Galaxy groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys
<p>Using the extended halo-based group finder developed by <a href="http://iopscience.iop.org/article/10.3847/1538-4357/abddb2/pdf">Yang et al. 2021</a>, which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we (<a href="http://arxiv.org/abs/2205.05517">Li et al. 2022</a>) construct galaxy group and candidate protocluster catalogs in a wide redshift range (<span class="math-tex">\(0 < z < 6\)</span>) from the joint CFHT Large Area <em>U</em>-band Deep Survey (CLAUDS) and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) deep data set. Based on a selection of 5,607,052 galaxies with<em> i</em>-band magnitude m<em><sub>i </sub></em>< 26 and a sky coverage of 34.41 deg<sup>2</sup>, we identify a total of 2,232,134 groups, within which 402,947 groups have at least three member galaxies. By checking the galaxy number distributions within a 5-7 <em>h<sup>-1</sup></em>Mpc projected separation and a redshift difference <span class="math-tex">\(\Delta z \leq 0.1\)</span> around those richest groups at redshift <span class="math-tex">\(z > 2\)</span>, we identified a list of 761, 343 and 43 protocluster candidates in the redshift bins <span class="math-tex">\(2 \leq z < 3\)</span>, <span class="math-tex">\(3 \leq z < 4\)</span> and <span class="math-tex">\(z > 4\)</span>, respectively. </p> <p> </p> <p>More details can be found in our paper (<a href="http://arxiv.org/abs/2205.05517">arXiv: 2205.05517</a>). Our catalogs are also shared at <a href="http://gax.sjtu.edu.cn/data/PFS.html">https://gax.sjtu.edu.cn/data/PFS.html</a>.</p>
Catalog-level blinding on the bispectrum for DESI-like galaxy surveys
<p>Points used in the Figures in the paper "Catalog-level blinding on the bispectrum for DESI-like galaxy surveys".</p>
Galaxy populations in the Hydra I cluster from the VEGAS survey III. The realm of low-surface brightness features and intra-cluster light
<p><span>This appendix provides the azimuthally-averaged surface bright</span><span>ness and colour profiles of the sample galaxies listed in Table 1 of the paper "Galaxy populations in the Hydra I cluster from the VEGAS survey III. The realm of low-surface brightness features and intra-cluster light", by <span>Marilena Spavone</span><span>,</span><span> Enrichetta Iodice</span><span>,</span><span> Felipe S. Lohmann</span><span>, Magda Arnaboldi</span><span>, Michael Hilker</span><span>, Antonio La </span><span>Marca</span><span>, Rosa Calvi</span><span>, Michele Cantiello</span><span>, Enrico M. Corsini</span><span>, Giuseppe D’Ago</span><span>, Duncan A. Forbes</span><span>, Marco </span><span>Mirabile</span><span>, and Marina Rejkuba.</span><br></span></p>
JWST spectra and best-fit SED models of massive quiescent galaxies from the Blue Jay survey.
<ul> <li>The JWST/NIRSpec spectra are stored as FITS tables which include wavelength (in angstrom, in rest-frame), calibrated flux, uncertainty, and best-fit model (in 1e-19 erg/s/cm^2/AA).</li> </ul>
A Photometric Survey of Globular Cluster Systems in Brightest Cluster Galaxies
<p>Complete photometry and reference images from HST, for globular cluster systems around 26 giant early-type galaxies.</p>
MMT/Binospec Spectroscopic Survey of Two z~0.8 Galaxy Clusters: Galaxy Spectra Figures and FITS Data
<p>This dataset includes MMT/Binospec spectra of all 371 galaxies observed in 2019 and 2022, which were already discussed in the research paper (J. Di et al.) where you just got this dataset link. To quickly look at our spectra, we reduced our spectra in the JPG format. We also attached the raw FITS format 1D and 2D spectra of two years' observations.</p>
The mock samples for high-z spectroscopic galaxy survey
<p>The mock samples for high-z spectroscopic galaxy survey. The dataset includes zCOSMOS, VIPERS and PFS-like galaxy survey's mock samples. The final design of PFS galaxy survey is not determined. So if you want to get the PFS-like mocks, please contact meng.jiacheng@foxmail.com or go to https://lig.astro.tsinghua.edu.cn/astrodata.</p> <p>The files named as "lu14_zcosmos_mock_index.h5df" are mocks for zCOSMOS survey. The files named as "lu14_w1_mock_index.h5df" and "lu14_w4_mock_index.h5df" are mocks for VIPERS survey, where "w1" and "w4" represent the two fields of VIPERS survey. Index is from 0 to19, representing the 20 mocks with different direction of line-of-sight when constructing mocks. The meanings of the keys in hdf5 files are as follows:</p> <ul> <li>"ra" and "dec": coordiantes;</li> <li>"z_spec" and "z_phot": spectroscopic redshift and photometric redshift;</li> <li>"stellar_mass": stellar mass;</li> <li>"sfr": star formation rate;</li> <li>"halo_mass": halo mass;</li> <li>"group_index": ID of the galaxy group for the given galaxy;</li> <li>"central_flag": 1 for central galaxy, 0 for satellite galaxy;</li> <li>"m_*": apparent magnitude;</li> <li>"M_*": absolute magnitude;</li> <li>"flag": 0 for no observation, 1 for galaxy having spectroscopic redshift, others for galaxy only having photometric redshift;</li> <li>"snap": the snapshot of the model galaxy;</li> <li>"model_index": the ID of the model galaxy;</li> <li>"box_index": the box ID where the model galaxy are in when constructing mocks.</li> </ul>
Super sample covariance and the volume scaling of galaxy survey covariance matrices
<p>Simulation data used in Super sample covariance and the volume scaling of galaxy survey covariance matrices.</p>
Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
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