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44 results for “Stars: Formation”
Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.1709T">Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini ≤ 10-4 in the early Universe - I. Effect of surface opacity on stellar evolution and the dust-driven wind</a></p>
A possible formation channel for blue hook stars in globular cluster - II. Effects of metallicity, mass ratio, tidal enhancement efficiency and helium abundance
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2016MNRAS.463.3449L">Lei et al. (2016)</a>. MESA version 7211.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stw2242">10.1093/mnras/stw2242</a></p>
Dataset from: On the formation history of Galactic double neutron stars
<p>The results of all simulations shown in "<a href="http://doi.org/10.1093/mnras/sty2463">On the formation history of Galactic double neutron stars</a>", published in <em>Monthly Notices of the Royal Astronomical Society</em>, Volume 481, Issue 3, December 2018, Pages 4009–4029 (<a href="https://arxiv.org/abs/1805.07974">arXiv</a>).</p> <p>Contents:</p> <p>allDoubleCompactObjects_XX.dat<br> README</p> <p>Where XX is the number of the simulation of interest. XX = {00,01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18}.</p> <p>All simulations made using <a href="http://compas.science/">COMPAS</a></p>
It's written in the massive stars: The role of stellar physics in the formation of black holes
<p>This repository contains additional data for the paper "It's written in the massive stars: The role of stellar physics in the formation of black holes" by E. Laplace, F.R.N. Schneider, and Ph. Podsiadlowski (2024).</p> <p> </p>
Data and Code From Formation of Stripped Stars From Stellar Collisions in Galactic Nuclei
<p>This repository is for the paper "Formation of Stripped Stars From Stellar Collisions in Galactic Nuclei" by Charles F. A. Gibson. It is associated with manuscript number ApJ AAS55868.</p> <p>We include the MESA inlists, SPH input files, and the last SPH output file for the StarSmasher relaxations of the parent models in Table 1 and collisions from Tables 2 and 3.</p> <p>We also include the StarSmasher source code with the Stretchy HCP relaxation technique and the post processing codes used to generate MESA input from the StarSmasher output.</p> <p>Finally, we provide the MESA input files for the post-collision stellar evolution of the models shown in Figures 7 and 9.</p>
Five star movie ratings form the MovieLens 25M dataset, grouped by user id, in JSON format
<p>From the MovieLens 25M dataset, I have extracted the five star ratings and grouped them by user ID. The original source files can be found here: </p> <p>https://grouplens.org/datasets/movielens/</p> <p> </p>
Data from: STAR locally prolongs effective refractory period and increases ventricular tachycardia cycle length without short-term scar formation or functional decline: Insights from a translational porcine model study
Open the record for dataset details and reuse information.
Global Star-formation Properties Extracted from Synthetic Star-forming Regions | Appendix C
<p>We provide in this online-material <span class="math-tex">\(\sim 5800\)</span> realistic synthetic observations (FITS files) of a synthetic star-forming region described in detail in Chapter 4 of the PhD thesis. </p> <p>Please cite the following papers:</p> <p>http://adsabs.harvard.edu/abs/2017ApJ...849….3K<br> http://adsabs.harvard.edu/abs/2017ApJS..233....1K</p>
Global Star-formation Properties Extracted from Synthetic Star-forming Regions | Appendix D
<p>We provide in this online-material measured dust surface density maps, dust temperature maps and corresponding <span class="math-tex">\(\chi^2\)</span> maps of a synthetic star-forming region described in detail in Chapter 5 of the PhD thesis.</p> <p>Please cite the following papers:</p> <p>http://adsabs.harvard.edu/abs/2017ApJ...849….3K<br> http://adsabs.harvard.edu/abs/2017ApJS..233....1K<br> http://adsabs.harvard.edu/abs/2017ApJ...849….1K</p>
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter. in Termite coprolites (Blattodea: Isoptera) from the Early Cretaceous of eastern Inner Mongolia, Northeast China
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter.
Grouped star formation: converting sink particles to stars in hydrodynamical simulations
<p>This repository contains the essential raw data and codes to recreate the plots in this paper (10.1093/mnras/stab3617; <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.2657L/abstract">Liow et al. 2022</a>), which contains the following directories and codes:</p> <p><strong>B12/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the sub-parsec-scale isolated cluster models (see Sections 2.3.1 and 3.1). The only exception is the B12/B12/ subdirectory, which contains the sink particles at 0.19 Myr from the original simulation (Bate 2012). </li> <li>The gas, sink and star files were produced using AMUSE v2021.6.0 (Portegies Zwart et al. 2021; <a href="https://zenodo.org/record/4946130">https://zenodo.org/record/4946130</a>), while the random state and setting files were created using Ekster v1.01 (Rieder and Liow 2021; <a href="https://zenodo.org/record/5520944">https://zenodo.org/record/5520944</a>). See Ekster for more information on how to run the code.</li> </ul> <p><strong>L20/ </strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the parsec-scale cloud-cloud collision models (see Sections 2.3.2 and 3.2). The only exception is the L20/L20/ subdirectory, which contains the sink particles at 1.75 Myr from the original simulation (Liow and Dobbs 2020). </li> </ul> <p><strong>J+/</strong></p> <ul> <li>This directory contains the stars at 5 Myr, and stars and sinks at 20 Myr for all the parsec-scale isolated cluster models (see Sections 2.3.3 and 3.3). The only exception is the J+/J+/ subdirectory, which contains the sink particles at 5 Myr and at 20 Myr from the original simulation (Jaffa et al. 2022).</li> </ul> <p><strong>R+/</strong></p> <ul> <li>This directory contains the stars at 1.80 Myr and at 2.40 Myr for the kilo-parsec-scale spiral arm model (see Sections 2.3.4 and 3.4).</li> </ul> <p><strong>L20_Extra/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of changing random seeds on cluster properties (see Section 4.1).</li> </ul> <p><strong>B12_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 0.19 Myr for all the additional sub-parsec-scale isolated cluster models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>L20_VG/</strong></p> <ul> <li>This directory contains the initial condition (or the snapshot right before star formation) and the snapshot at 1.75 Myr for all the additional parsec-scale cloud-cloud collision models to study the effect of varying upper star mass limits (see Section 4.2).</li> </ul> <p><strong>misc/</strong></p> <ul> <li>This directory contains the miscellaneous files to produce some of the plots in the paper. </li> </ul> <p><strong>load_data.py</strong></p> <ul> <li>This python code loads the necessary sink and star files for the code 'plot.py'. </li> </ul> <p><strong>plot.py</strong></p> <ul> <li>This python code recreates the plots in the paper. See individual functions for more information.</li> </ul> <p>Please contact the authors for more information.</p>
The miniJPAS Survey: The Radial Distribution of Star Formation Rate in Faint X-ray AGN
<p>Figures show the AGN host galaxies accompanied by their observed J-spectra within their Kron radii and the fitted spectrum with \texttt{CIGALE} to determine their physical properties. We note that \texttt{CIGALE} does not fit AGN emission for 1 source with miniJPAS object ID 2241-18160. This object fell in the composite area in the BPT diagram, and in the LINER area in the WHAN diagram. This could represent a case where the AGN is obscured due to dust and \texttt{CIGALE} fails to fit optical AGN emission for this case. This further emphasizes the importance of X-ray emission to identify AGN systems that are optically obscured and are seen as normal galaxies without X-ray detections. We report that the inclusion or emission of this object from our AGN sample does not produce any differences in the results obtained in this paper.</p>
The SOFIA Massive (SOMA) Star Formation Q-band Follow-up. I. Carbon-Chain Chemistry of Intermediate-Mass Protostars
<p>These figures show spectra obtained by the Yebes 40m telescope toward eleven intermediate-mass protostars. Black lines indicate the observational spectra and red curves indicate the best-fitting models with the MCMC method. Blue curves show the results of the Gaussian fitting.</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>
RNA-seq-based identification of StAR upregulation by islet amyloid formation
GEO Series GSE135276. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
Graphics: Evolutionary pathways leading to Double Neutron Star formation
<p>Schematical representations of the evolutionary pathways leading to Double Neutron Stars formation as presented in the paper "Common–Envelope Episodes that lead to Double Neutron Star formation" (<a href="https://arxiv.org/abs/2001.09829">arXiv:2001.09829).</a></p> <p>Contents:</p> <p>./Channel_I/<br> - 00_Channel_I_circular.pdf <br> - 00_Channel_I_circular.png <br> - 00_Channel_I_vertical.jpg<br> - 00_Channel_I_vertical.pdf<br> - 00_Channel_I_horizontal.pdf <br> - 00_Channel_I_horizontal.jpg <br> - Channel_I_individual_pdfs.tar.gz<br> - Channel_I_individual_pngs.tar.gz</p> <p>./Channel_II/<br> - 00_Channel_II_circular.jpg <br> - 00_Channel_II_circular.pdf <br> - 00_Channel_II_vertical.jpg<br> - 00_Channel_II_vertical.pdf<br> - 00_Channel_II_horizontal.jpg <br> - 00_Channel_II_horizontal.pdf <br> - Channel_II_individual_pdfs.tar.gz<br> - Channel_II_individual_pngs.tar.gz</p> <p>00_Channel_*_circular.*, 00_Channel_*_vertical.* and 00_Channel_*_horizontal.* contain the figure of the specified formation channel in the specified format in a circular/vertical/horizontal direction.</p> <p>Channel_*_individual_*.tar.gz contain the individual figures for each formation channel in the specified format.</p> <p>If you use these illustrations please kindly include a citation to:<br> A. Vigna-Gómez, M. MacLeod, C. J. Neijssel, F. S. Broekgaarden, S. Justham, G. Howitt, S. E. de Mink, S. Vinciguerra, and I. Mandel. Common envelope episodes that lead to doubleneutron star formation. PASA, 37:e038, Jan. 2020 (<a href="https://ui.adsabs.harvard.edu/abs/2020PASA...37...38V/abstract">ADS</a>)</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>
Star formation timescale in the molecular filament WB 673
<p>Ammonia lines data in molecular filament WB 673. Files WB673_NH3_11.fits, WB673_NH3_22.fits and WB673_NH3_33.fits correspond to maps in lines NH<sub>3</sub> (1,1), NH<sub>3</sub> (2,2) and NH<sub>3</sub> (3,3), respectively. Data obtained at the 100-m Effelsberg telescope (Germany) and covered the entire area of the filament with a size 10' <span class="math-tex">\(\times\)</span> 50' with respect to the minor and major axes. </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
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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.