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19 results for “galaxy masses”
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>
The Outer Stellar Mass of Massive Galaxies: A SimpleTracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects
<p>These are the data for reproducing the results of the publication titled "The Outer Stellar Mass of Massive Galaxies: A Simple Tracer of Halo Mass with Scatter Comparable to Richness and Reduced Projection Effects" by Song Huang et al.</p> <p>Please see the Python scripts and Jupyter notebooks provided in the <a href="https://github.com/dr-guangtou/jianbing">jianbing</a> repo for examples about how to use these data files. And please contact dr.guangtou@gmail.com if you have any questions about these data.</p> <p>-------------------------------------------------------------------------------------------------</p> <p>Here is a brief description of all the files:</p> <p><strong>Data from N-body simulation:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_halos_0.7333_reduced_logmvir_13.npy?versionId=1648006b-a91a-4300-aadf-c4746d6f3ef2">mdpl2_halos_0.7333_reduced_logmvir_13.npy</a> <ul> <li>Basic information about the dark matter halos from MDPL2 simulation</li> <li>For scale factor = 0.7333 (or z~0.4).</li> <li>Only for halos with logMvir > 13.0.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/mdpl2_particles_0.7333_72m.npy?versionId=ff7d5847-df44-46f5-9bcc-d8a7f3cc040d">mdpl2_particles_0.7333_72m.npy</a> <ul> <li>Particle catalog of the a=0.7333 snapshot from MDPL2</li> <li>This is a down-sampled version with 72 million particles.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_theory_demo.pkl?versionId=7ed87c28-7adc-4987-9e00-b6223c744d42">topn_theory_demo.pkl</a> <ul> <li>These are the data used to create the theoretical demo of the TopN test.</li> <li>It is used for making the figures in <a href="https://github.com/dr-guangtou/jianbing/blob/master/notebooks/figure/fig1.ipynb">this notebook</a>.</li> </ul> </li> </ul> <p><strong>Catalogs of Galaxies or Galaxy Clusters:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/camira_s16a_cluster_use_bsm.fits?versionId=ca274c83-4025-41c4-b993-3cc9074f08b2">camira_s16a_cluster_use_bsm.fits</a> <ul> <li>The HSC S16A CAMIRA cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_hsc_s16a_cluster_bsm.fits?versionId=11608e41-2427-4808-9060-a06139de165c">redmapper_hsc_s16a_cluster_bsm.fits</a> <ul> <li>The HSC S16A redMaPPer cluster catalog.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/redmapper_sdss_cluster_bsm.fits?versionId=b977c4ed-11c9-4751-b32f-60883d2e81b0">redmapper_sdss_cluster_bsm.fits</a> <ul> <li>The SDSS DR8 redMaPPer clusters in the HSC S16A footprint.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_massive_logm_11.2.fits?versionId=603cb17c-bb64-4aa7-ae05-5ec61c7ee861">s16a_massive_logm_11.2.fits</a> <ul> <li>0.2 <z < 0.5 massive galaxies in the HSC S16A footprint.</li> </ul> </li> </ul> <p><strong>Galaxy-Galaxy Lensing Data:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/s16a_weak_lensing_medium.hdf5?versionId=593c4ba0-6d7d-4b83-b8d9-01740a351fcd">s16a_weak_lensing_medium.hdf5</a> <ul> <li>A compilation of the weak lensing data to calculate the DeltaSigma profiles.</li> <li>This includes the weak lensing source catalog, photometric redshift calibration file, and the random catalog.</li> <li>"medium" here means we applied the medium criteria for selecting source galaxies. Please refer to <a href="https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5658S/abstract">Speagle et al. (2019)</a> for the exact meaning of these criteria.</li> <li>We also have a "basic" and "strict" version. Please send your request if you need them.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/f10135d5-64ea-47c1-b292-bea86bbcdf08/topn_public_s16a_medium_precompute.hdf5?versionId=2216ecf9-b836-4dd5-a9dd-7e070e4977bf">topn_public_s16a_medium_precompute.hdf5</a> <ul> <li>A compilation of pre-computed lensing profiles for each individual object in a different galaxy or cluster samples for the TopN test.</li> <li>These are the data used to create the stacked DeltaSigma profiles.</li> <li>We also provide the "strict" and the "basic" versions if you want to test the robustness of the TopN tests against the different selections of source galaxies in weak lensing measurements. You just need these files to generate the stacked DeltaSigma profiles.</li> </ul> </li> </ul>
Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations
<p>This repository contains .npy files, loaded like:</p> <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> <p>which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations <a href="https://github.com/jmackereth/galactic-assembly-art.git">(explored in this repository)</a>.</p> <p>the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).</p>
Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)
<p>This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.</p>
The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements
<p>Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press).</p> <p>Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files</p> <p>When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations:</p> <p> SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W</p> <p>Please reach out if you have any questions or concerns.</p>
Stellar mass function for galaxies in A133
<p>This table provides the stellar mass functions (dN/dlg(M*)) of A133,<br> as shown in Fig 10 of Starikova et al. 2020, normalized to 1 square<br> degree on the sky.</p> <p> </p>
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
The Data For Inside-Out versus Upside-Down: The Origin and Evolution of Metallicity Radial Gradients in FIRE Simulations of Milky Way-mass Galaxies and the Essential Role of Gas Mixing
<p>The files titled Graf et al. 2024b store the x-axis and y-axis values for each line in each figure. The files which end in .py are the scripts which produced the data in the figures.</p> <p>This data abides by CC-BY.</p>
Data for the paper 'WISDOM Project - VI. Exploring the relation between supermassive black hole mass and galaxy rotation with molecular gas '
<p>This upload includes the data underlying the MNRAS paper Smith et al. (2020) entitled 'WISDOM Project - VI. Exploring the relation between supermassive black hole mass and galaxy rotation with molecular gas' (arXiv:2010.08565). The full author list is available from the paper, and we request that this paper is cited if this data is used in future publications.</p> <p>The upload includes machine-readable versions (csv) of Tables 3, 4, A1 and A2 of the paper. The upload also includes the calibrated spectra from the two observing programmes (191-18 at the IRAM 30m telescope and 2018-04a at the OSO 20m telescope), and the homogenised spectra used in the project. Full descriptions are given in the paper. </p> <p>Table 3: CubeData.csv; The table contains the determined line widths, inclinations, and SMBH masses used for the resolved sample of this project. The references from which the cubes were obtained, are listed in Table 3 of the paper.</p> <p>UnresolvedData.csv: Table 4; The table contains the determined line widths, inclinations, and SMBH masses used for the unresolved sample of this project. The references from which the spectra were obtained, are listed in Table 4 of the paper.</p> <p>IRAMData.csv: Table A1; The table contains a list of galaxies observed using the IRAM 30m telescope as part of project 191-18. For each galaxy the rms noise is listed, and for detected galaxies the spectrally-integrated line intensity measured over the specified velocity range given. The inferred molecular gas mass is also listed. </p> <p>OSO20mData.csv: Table A2; The table contains a list of galaxies observed using the OSO 20m telescope as part of project 2018-04a. For each galaxy the rms noise is listed, and for detected galaxies the spectrally-integrated line intensity measured over the specified velocity range given. The inferred molecular gas mass is also listed. </p> <p>IRAM30m_191-18.tar: Calibrated spectra from programme 191-18.</p> <p>OSO2018-04a.tar: Calibrated spectra from programme 2018-04a.</p> <p>ResolvedSample.tar: Homogenised spectra for the spatially-resolved sample. Note that some spectra were sourced from other works, the original references are given in the fits headers.</p> <p>UnresolvedSample.tar: Homogenised spectra for the spatially-unresolved sample. Note that some spectra were sourced from other works, the original references are given in the fits headers.</p>
Measuring the conditional luminosity and stellar mass functions of galaxies by combining the DESI LS DR9, SV3 and Y1 data
Open the record for dataset details and reuse information.
Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
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age metallicity data for simulated Milky Way mass galaxy from the NIHAO-UHD suite
<p>pickle file containing ages and metallicities of stellar particles from the galaxy g2.79e12 from the NIHAO-UHD simulation suite</p>
Galaxy Histories with in silico mass spectra of Mirex, Ethylene, Benzophenone and Enilconazole predicted via QCxMS
<p>Galaxy histories containing spectra predicted using the QCxMS software. The molecules are Mirex, Ethylene, Benzophenone and Enilconazole. Calculations have been carried our using Galaxy and histories exported as ROCrates.</p>
The Data For Spatial Variations of Stellar Elemental Abundances in FIRE Simulations of Milky Way-Mass Galaxies: Patterns Today Mostly Reflect Those at Formation
<p>Spatial patterns of stellar elemental abundances encode rich information about a galaxy’s formation<br>history. We analyze the radial, vertical, and azimuthal variations of metals in stars, both today and at<br>formation, in the FIRE-2 cosmological simulations of Milky Way-mass galaxies, and we compare<br>with the Milky Way. Overall, spatial variations of stellar metallicities show only modest differences between formation and today; spatial variations today primarily reflect the conditions of stars at birth, with spatial redistribution of stars after birth contributing secondarily. </p> <p> </p> <p>This data abides by CC-BY.</p>
Molecular gas budget of strongly magnified low-mass star-forming galaxies at cosmic noon Appendix
<p>Appendix of the article Molecular gas budget of strongly magnified low-mass star-forming galaxies at cosmic noon by V. Catán et al.</p>
X-ray Binary Luminosity Function Scaling Relations in Elliptical Galaxies: Evidence for Globular Cluster Seeding of Low-Mass X-ray Binaries in Galactic Fields
<p>Processed Chandra images, Chandra source catalog, and extended Figure 2 for Lehmer et al. (2020)</p>
Catalog of High-Mass X-Ray Binaries in the Galaxy (4th Ed.)
This table contains the 4th edition of the Catalog of High-Mass X-Ray Binaries (HMXBs) in the Galaxy. The catalog contains source name(s), coordinates, finding charts, X-ray luminosities, system parameters, and stellar parameters of the components and other characteristic properties for 114 HMXBs, together with a comprehensive selection of the relevant literature. The aim of this catalog is to provide some basic information on the X-ray sources and their counterparts in other wavelength ranges (gamma-rays, UV, optical, IR, radio). About 60% of the high-mass X-ray binary candidates are known or suspected Be/X-ray binaries, while 32% are supergiant/X-ray binaries. Some sources, however, are only tentatively identified as high-mass X-ray binaries on the basis of their X-ray properties similar to the known high-mass X-ray binaries. Further identification in other wavelength bands is needed to finally determine the nature of these sources. In cases where there is some doubt about the high-mass nature of the X-ray binary this is noted. Literature published before 1 October 2005 has, as far as possible, been taken into account. Information on the numbers used to code references is available at the URL <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/455/1165/refs.dat">https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/455/1165/refs.dat</a> Individual notes on each HMXB are available at the URL <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/455/1165/notes.dat">https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/455/1165/notes.dat</a> This database was first created by the HEASARC in January 2001, based on the 2000 version of this catalog. It was updated to the 4th edition in September 2006, based on the <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/A+A/455/1165">CDS catalog J/A+A/455/1165</a>. This is a service provided by NASA HEASARC .
Star-Forming Galaxies High-Mass X-Ray Binaries Catalog
Based on a homogeneous set of X-ray, infrared and ultraviolet observations from Chandra, Spitzer, GALEX and 2MASS archives, the authors studied populations of high-mass X-ray binaries (HMXBs) in a sample of 29 nearby star-forming galaxies and their relation with the star formation rate (SFR). In agreement with previous results, the authors find that HMXBs are a good tracer of the recent star formation activity in the host galaxy and their collective luminosity and number scale with the SFR, in particular, L<sub>X</sub> ~ 2.6 x 10<sup>39</sup> x SFR. However, the scaling relations still bear a rather large dispersion of rms ~ 0.4 dex, which the authors believe is of a physical origin. This table contains the catalog of 1055 compact X-ray sources detected within the D25 ellipse for galaxies of this sample which the authors used to construct the average X-ray luminosity function (XLF) of HMXBs with substantially improved statistical accuracy and better control of systematic effects than achieved in previous studies. The XLF follows a power law with slope of 1.6 in the log(L<sub>X</sub>) ~ 35 - 40 luminosity range with a moderately significant evidence for a break or cut-off at L<sub>X</sub> ~ 10<sup>40</sup> erg/s. As before, the authors did not find any features at the Eddington limit for a neutron star or a stellar mass black hole. In their paper, the authors discuss the implications of their results for the theory of binary evolution. In particular, they estimate the fraction of compact objects that once during their lifetime experienced an X-ray active phase powered by accretion from a high mass companion and obtain a rather large number, f<sub>X</sub> ~ 0.2 x (0.1 Myr/tau<sub>x</sub>), where tau<sub>x</sub> is the life time of the X-ray active phase. This is about 4 orders of magnitude more frequent than in low-mass X-ray binaries (LMXBs). The authors also derive constrains on the mass distribution of the secondary star in HMXBs. Note that, in their paper, the authors estimate that ~ 300 of the 1055 sources are likely to be background AGNs (cosmic X-ray background or CXB sources) and that the majority (<~ 700) of the remaining ~ 750 sources are young HMXB systems associated with star formation in their host galaxies. This table was created by the HEASARC in March 2012 based on <a href="https://cdsarc.cds.unistra.fr/ftp/cats/J/MNRAS/419/2095">CDS Catalog J/MNRAS/419/2095</a> file hmxb.dat. This is a service provided by NASA HEASARC .
Catalog of High-Mass X-Ray Binaries in the Galaxy (Dynamic Version)
High-Mass X-ray Binaries (HMXBs) are a particular class of high-energy sources which require multi-wavelength observational efforts to be properly characterized. New identifications and refinement of previous measurements are regularly published in the literature by independent teams of researchers and could, once collected in a catalog, offer a tool to facilitate further studies on HMXBs. The authors aim to update on previous instances of catalogs of HMXBs in the Galaxy, and provide the community easy access to the most complete set of observables on Galactic HMXBs. On top of the fixed version that is available in VizieR, they also aim to host and maintain a dynamic version that can be updated upon request from users, and where any modification will be logged. Using previous catalogs of HMXBs supplemented by listings of hard X-ray sources detected in the past 20 years, the authors produce a base set of HMXBs and candidates by means of identifier and sky coordinate cross-matches. They query SIMBAD for unreferenced HMXBs. They search for as many hard X-ray, soft X-ray, optical and infrared counterparts to the HMXBs as we can in well-known catalogs and compile their coordinates. Each HMXB is subject to a meticulous search in the literature to find relevant measurements and their original reference. The authors provide a catalog of HMXBs in the Galaxy with their best known coordinates, companion star spectral type, systemic radial velocities, component masses, orbital period, eccentricity and spin period when available. This catalog also provides the coordinates and identifiers for each counterpart found from hard X-rays to near-infrared, including counterparts from the recent Gaia DR3 catalog. This catalog was created from data-mining the published literature. It takes into account information available through 2022. Values for binary parameters are joined with a reference in which the value was derived. Position data for which the authors have manually found a counterpart also have a specific reference; if not, then the data comes directly from the corresponding catalog. This database table was first created by the HEASARC in October 2023, based on the 2023-09 version of this catalog, found from the <a href="https://binary-revolution.github.io/HMXBwebcat/">Binary rEvolution website</a>. It is automatically updated in the HEASARC database within a few days of a new version being released on that website. This is a service provided by NASA HEASARC .
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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)
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DANDI Archive for NWB datasets
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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.