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Dataset results
10 results for “galaxy formation”
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
Quenching of star formation from a lack of inflowing gas to galaxies
<p>This dataset provides HST and ALMA mosaics of the REQUIEM-ALMA survey of six strong gravitationally lensed quiescent galaxies at z=1.6 to z=3.2 (MRG-M1341, MRG-M0138, MRG-M2129, MRG-M0150, MRG-M0454, MRG-M1423) from Whitaker et al. (2021). The HST mosaics were produced with the <a href="https://github.com/gbrammer/grizli">grizli</a> software module. The ALMA data products include the full spectrally-averaged continuum data weighted for optimum sensitivity (with MRG-M2129 and MRG-M0138 including a correction for the primary beam response).</p>
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
The Formation of Isolated Ultra-Diffuse Galaxies in Romulus25
<p>Ultra-diffuse galaxies (UDGs) are a subset of the low surface brightness (LSB) galaxy population characterized by dwarf-like luminosities but light profiles that can extend as far as those of Milky-Way-mass galaxies. Although thousands of these objects have been discovered in cluster and group environments, fewer than 200 have thus far been confirmed in the field. We use the Romulus25 cosmological simulation to study the methods by which isolated UDGs form and evolve. Beyond their low central surface brightnesses and large effective radii, these galaxies have fairly typical global properties for their masses and environments. However, their central star formation rates are systematically low, leading to lower central stellar densities and older, fainter central stellar populations. We find that isolated UDGs are the products of early major mergers that produce a temporary boost in spin and cause star-forming gas to migrate from the centers of the galaxies to larger radii. Although our simulated UDGs are consistent with the few existent observations of isolated UDGs, we predict that many isolated UDGs have been missed by current surveys. The Nancy Grace Roman Telescope’s ability to provide deep, wide-field photometry will make it an ideal instrument for future discovery and study of these extreme LSB galaxies.</p>
Chaotic and Clumpy Galaxy Formation in an Extremely Massive Reionization-Era Halo
<p>This is a persistent version of the datasets and data table associated with Spilker et al. (2022), ApJL. I (Justin) recommend checking https://github.com/spt-smg/publicdata for any updates to the datasets contained here; this is just a tarball of the folder in that repository for this paper, commit 9e129f2.</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 and star formation in nearby starburst galaxy mergers
<p>This compressed file contains a python script, fits images and numpy table produce the animations in the paper. To make animations, run the script `make_final_animation.py` in `scripts/` directory. The script is run in python 3.7. After running the script, the generated html animations will be stored in `figures/` directory.</p>
Star formation and morphological properties of galaxies in the P\lowercase{an}-STARRS 3$\pi$ survey- I.\\ A machine learning approach to galaxy and supernova classification
<pre>This is the catalog presented in Baldeschi, et al (2020). The catalog is subdivided in 13 csv files. Files description: First column: Panstar ID (integer) Second column: Right ascension [deg] (float) Third column: Declination [deg] (float) Fourth column: Probability for a source of being a star (P_star) (float) Fifth column: Probability for a galaxy of being higly star-forming (P_HSFF) (float) Sixth column: Probability for a galaxy of being spiral (P_spiral) (float) seventh column: Compleatness flag (string) If using this catalog for publications, please cite Baldeschi, et al (2020). Fourth column values are from Tachibana & Miller (2018). For a detailed description of the columns we refer to the Appendix A of Baldeschi, et al (2020).</pre>
Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra - parquet format
<p>This is the same as the published data available under 10.5281/zenodo.3768398, but in the format of parquet files. This means you can access it using Dask for convenience when using cloud compute facilities. </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.4060257). 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 uncompressed parquet files for ease of access when using cloud compute such as Dask. df_spec_classprobs.parquet 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.parquet contains the 111 million photometrically observed sources, with our class labels and probabilities added.</p>
The catalogue of "MaNGA DynPop - II. Global stellar population, gradients, and star-formation histories from integral-field spectroscopy of 10K galaxies: link with galaxy rotation, shape, and total-density gradients"
<p><strong>NOTE: Three parameters related to dust extinction ("delta_Map", "Fred_tot_Map", "Fred_gal_Map") in v1 catalogue are incorrectly saved, we have updated the catalogue in <a href="https://zenodo.org/records/15742825">https://zenodo.org/records/15742825</a> .</strong></p> <p> </p> <p>This catalogue is related to the paper "<strong>MaNGA DynPop - II. Global stellar population, gradients, and star-formation histories from integral-field spectroscopy of 10K galaxies: link with galaxy rotation, shape, and total-density gradients</strong>" by <strong>Lu et al. </strong><a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.tmp.2611L/abstract">https://ui.adsabs.harvard.edu/abs/2023MNRAS.tmp.2611L/abstract</a>. In this paper, we analyze the stellar population properties and star formation histories for over 10,000 MaNGA galaxies.</p> <p>Below are the descriptions of the files: </p> <ul> <li><strong>DynPop2_SPSFH_v1.hdf5 </strong>A HDF5 file containing the catalogue.</li> <li><strong>HDF5_reading_script.py</strong> A Python script to read the catalogue from the HDF5 file.</li> <li><strong>SP_catalog_explanation.pdf</strong> The data explanations for the catalogue, also see Table B1 in the paper. </li> </ul>
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
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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.