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1,584 results for “Star”
Line luminosities of Galactic and Magellanic Cloud Wolf-Rayet stars
<p>Template (optical, continuum subtracted) emission line spectra of Milky Way and Magellanic Cloud Wolf-Rayet stars, presented in Appendix B of Crowther, Rate & Bestenlehner (2023, MNRAS, in press; https://arxiv.org/abs/2301.11297). Ascii format, wavelength (Angstrom) versus monochromatic luminosity (erg/s/Angstrom). Separate templates are provided for single-only and all (single+binary) since binaries are often contaminated by (Balmer) absorption lines from companion OB stars. Highly reddened sources (primarily in Milky Way) also exhibit strong interstellar features (CaII H&K, NaI D, DIBs at 4430, 5780, 5797 Angstrom) and some datasets involved have not been corrected for atmospheric telluric features. Owing to the heterogeneous origin of individual datasets, templates cover a range of wavelength regions, and some exclude the 6070-6400 Ang region, owing to detector gaps for ANU 2.3m + DBS spectroscopy. Separate templates are provided for each galaxy with the exception of WN/C and WO stars, which are also combined into a single dataset (owing to the low total numbers involved). Templates are degraded to a uniform resolution of 10 Angstrom, with average radial velocity corrections of 284 km/s (LMC) and 162 km/s (SMC) applied. Further details are provided in the README.txt file.</p> <p>Please cite Crowther, Rate & Bestenlehner in publications if these templates are used in research. </p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as:</p> <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> <p>The files named <strong>data_*.txt</strong> contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows:</p> <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
SEVN parameter file from the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023
<p>The repository contains the runtime parameters used in the SEVN simulations analysed in the paper "Binary neutron star populations in the Milky Way" by Sgalletta et al., 2023.</p> <p><strong>Repository content: </strong></p> <p>- <em>used_params_Sgalletta2023.txt<br> </em>The file contains all the runtime parameters used in the SEVN simulations. The parameters that have been varied in different runs are indicated with **** and the explored values are reported in the comment. See the SEVN userguide (<a href="https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf">https://gitlab.com/sevncodes/sevn/-/blob/SEVN/resources/SEVN_userguide.pdf</a>) for the description of each parameter </p> <p> </p>
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>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Atmospheric Effects on Neutron Star Parameter Constraints with NICER
<p>Posterior sample files associated with the publication "Atmospheric Effects on Neutron Star Parameter Constraints with NICER" by Salmi et al. (2023; <a href="https://doi.org/10.48550/arXiv.2308.09319">arXiv:2308.09319</a>; <a href="https://doi.org/10.3847/1538-4357/acf49d">https://doi.org/10.3847/1538-4357/acf49d</a>).</p><p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p><p>Please refer to the README for detailed information.</p>
TESS Confirmed and First Identified SuperWASP Variable Stars
<p>This catalog consists of the TESS-confirmed and First Identified SuperWASP Variable Stars of types $\delta$ Scuti, $\gamma$ Doradus, RR Lyrae, eclipsing binary systems with pulsating components, rotating variables, and others. This dataset is part of a short summary submitted to RNAAS entitled "Identifying SuperWASP Detected Candidate Variables with TESS" (Zhou, A.-Y., 2023 Research Notes of the AAS, vol.7). </p><p> </p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions
<p>This repository contains the data set presented in the manuscript "Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions" (Ribas et al. 2017), and includes a table with several sample properties (e.g. stellar properties, Herschel photometry, different spectral indices), spectral energy distributions, Spitzer/IRS and Herschel/SPIRE spectra, the median SEDs of Taurus, Ophiuchus and Chamaeleon I, and the Herschel maps used.</p> <p>ERRATUM: three Chamaeleon I sources (Hn 11, T45a, and WY Cha) were mislabeled in the original version of the manuscript, which resulted in their names, stellar parameters, extinction values, infrared slopes, and silicate feature properties being assigned to incorrect coordinates. Because the photometry and spectroscopy presented in the original article is coordinate- based, the provided SEDs and spectra were also missmatched: the data files labeled Hn 11 in the original manuscript correspond to T45a, those labeled T45a correspond to WY Cha, and those labeled WY Cha correspond to Hn 11. Additionally, due to a mislabeling issue in Manoj et al. 2011, the source formerly labeled UX Cha is actually CHSM 8284. Therefore, stellar parameters and photometry labeled UX Cha in our original manuscript correspond to CHSM 8284 The updated version of the repository fixes the issue both in the sample.csv file and in the individual SED and Spitzer/IRS spectra files. The published erratum is available here: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/abb66e">https://iopscience.iop.org/article/10.3847/1538-4357/abb66e</a>.</p>
Evolution of binary stars on the HR diagram
<p>We studied the evolution of different classes of binary objects that can be observed in a typical stellar cluster, using a grid of detailed massive binary evolution models (Wang et al. 2020) with an initial metallicity of that of the Small Magellanic Cloud (SMC). To compute the models, we use the 1D stellar evolution code MESA (Modules for Experiments in Stellar Astrophysics, Paxton et al. 2011, 2013, 2015, 2018, version 8845).</p> <p>Our grid consists of 2078 binary models with initial primary masses greater than 5 MSun. This translates to a total cluster mass of ∼10^5 MSun in stars between 0.1 to 100 MSun (assuming a binary fraction of 1. The grid covers an initial mass ratio (mass of secondary over the mass of primary, hence always less than 1) range of 0.3-0.95 and orbital periods of 1 day to 8.6 yrs. In this range of masses, mass ratios, and orbital periods, a Monte Carlo method was used to sample initial binary model parameters assuming a Saltpeter initial mass function (IMF) (Salpeter 1955), a flat distribution of mass ratios and logarithm of initial orbital periods.</p> <p>Translucent grey circles indicate pre-interaction binaries - binaries that have not yet undergone a mass transfer phase via Roche Lobe overflow. Hence, the grey line traced on the HRD by the collection of pre-interaction binaries together essentially denotes the Single Star Isochrone (SSI). Grey squares indicate the merger product when we expect a binary to merge during the Case A mass transfer phase. We note that we only model and follow the evolution of Main Sequence mergers and not the mergers coming from the Case B channel. As such, the number of mergers in each frame is likely to be the lower limit to the number of merger products. Single star tracks at SMC metallicity are also plotted in the background from 5-100 MSun. When any component of a binary system completes core carbon burning at a certain cluster age (or helium-burning for the most massive stars), we mark the occurrence of a supernova by putting an ‘*’ symbol in the HRD, that fades over three time steps in the animation.</p> <p>Mass donors and accretors are shown with triangles and diamonds respectively. The binaries that are interacting or have interacted during their Main Sequence lifetime (i.e. the Case A models) are shown in colour, with the colour coding describing the rotation of the component stars (v rot /v crit ) of the binary. All donors and accretors of binaries that have interacted via Case B/C are shown in greyscale. A black frame around the triangles for the mass donor indicates that the surface Hydrogen mass fraction is less than 0.1. Similarly, a black frame around the diamonds for the mass accretors indicates that the surface Helium mass fraction is greater than 0.3. The current age of the cluster is displayed in the center bottom with a time bar that fills up as the animation moves forward in time.</p> <p>In the table above the legend, (from top) we indicate the number of Algol systems i.e. in the nuclear timescale slow Case A mass transfer phase, the number of Main Sequence merger products that are still burning hydrogen at the core, and the number of cool red supergiants (log T e f f < 3.7) at the respective time frames. Moreover, in the next two rows, we denote the number of OB stars that has a neutron star or black hole companion, arising from Case A and Case B evolution channels, at that cluster age. In the next row, we indicate the number of supernovae that have already happened until the current cluster age of the animation. We report the numbers of supernovae occurring from Case A and Case B donors separately from the other progenitors as we expect that the donor stars that have interacted via the Case A or Case B channels will be highly stripped of their envelopes and will likely be progenitors to stripped-envelope supernova (of type Ib and IIb) while the remaining will be progenitors to type IIp/n. The last line gives the number of pre-interacting binaries having luminosity lesser than the brightest non-interacted binary component by up to 1.5 dex.</p> <p>The individual components of the binaries that are in the semi-detached configuration and are interacting via the nuclear timescale slow Case A phase are joined together with solid black lines with an arrow indicating the direction of mass transfer (donor to the accretor). On the other hand, the individual components that have interacted in the past via Case A or B mass transfer are connected to each other with grey dotted lines. These are usually the systems where the donor star is a post-Main Sequence helium star and the accretor is a rejuvenated star still burning hydrogen at the core. The black and white dots over the Case A and B accretors denote that the donors of those binaries have imploded/exploded to form a black hole or neutron star respectively.</p>
Axisymmetric models for neutron star merger remnants with realistic thermal and rotational profiles: dataset
<p>Dataset containing the results of the parameter space exploration of binary neutron star merger remnants and 12 selected models:<br> * `search_results.dat` contains the parameters and properties of the successful results of the study.<br> * `model_*.log` are the logs with settings, parameters, and properties of the selected models.<br> * `model_*.out` are the profiles of the selected models in binary format.<br> * `XNS_reader.py` is a python script to read the binary format, convert it to text, and compute some derived and global quantities. EDIT 2022-05-30: the output file in binary format does contain the profiles of temperature and entropy per baryon, but those are not outputted in the converted text file. You can manually modify the python script in order to output these profiles too.<br> * `properties.csv` is a summary of the parameters and properties of the selected models.<br> <br> This dataset has been obtained with the stationary code XNS in General Relativity with the Conformal Flatness Approximation [Bucciantini and Del Zanna 2011; Pili et al. 2014; Camelio et al. 2018 and 2019].<br> The EOS is implemented as a cold piecewise polytrope [Read et al. 2009] plus a thermal gamma law.<br> The models have been selected between those obtained in the parameter space exploration.<br> For details see the companion paper [Camelio et al. 2021, PRD 103:063014].<br> <br> If you use this dataset, please cite its Zenodo DOI and the companion paper [Camelio et al. 2021, PRD 103:063014].</p> <p>EDIT 2022-05-30: an updated version of the code that has been used to produce this dataset is now on Zenodo (https://doi.org/10.5281/zenodo.6594069).<br> This updated version is called ASWNS code, and it does not contain the model of binary neutron star merger remnant used for this dataset, but an older version of the model of nonbarotropic neutron star (from Camelio et al. 2019).<br> You can implement any neutron star model on top of ASWNS, as shown in the examples provided with ASWNS.</p>
Library of equivalent widths of H I , He I , and He II lines of Massive Stars
<p>Library with the equivalent widths of the Balmer lines λλ 3835, 3889,<br> 3970, 4101, 4349, 4861; the He II lines λλ 4541 and 4200; the He I lines λλ 4471, 4387, 4144; and the He I +He II blended lines λ4026, measured in 45,000 CMFGEN models (Zsargó et al. 2020), and 202 PoWR models (Hainich et al. 2019) of OB stars.</p>
SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science
<p>Table of 301 previously unidentified SuperWASP stellar variables and related characteristics, not including rotators and unknown variables. The variable type has been decided by citizen scientists through the SuperWASP Variable Stars Zooniverse project. The types and periods of each object have been assessed by the authors to correct for mis-classifications; whilst they have been corrected as much as possible, some types periods remain best guesses. All periods have an uncertainty of 0.1%.</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>
The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves
<p>We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4]. </p> <p>The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain:</p> <p><span class="math-tex">\(( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )\)</span></p> <p>where <span class="math-tex">\(D_{\rm cm}\)</span> is the distance in cm from the source.</p> <p>The data files include a machine readable "/metadata" group with:</p> <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> <p>We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz.</p> <p>We make these waveforms freely available in the hope that they will be useful. We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms.</p> <p>---<br /> [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html<br /> [2] https://eob.ihes.fr/<br /> [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103.<br /> [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.</p>
Data and configuration files for "Expansion of accreting main-sequence stars during rapid mass transfer"
<p>Data and configuration files that can be used to reproduce results from the paper <a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...966L...7L/abstract">Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer</a>. This directory contains MESA inlists and starting models used for calculations performed with MESA r15140, and YAML configuration files for calculations performed with COMPAS v02.41.04.</p> <p>See README.txt for a description of all files.</p> <p> </p> <p>Any work making use of these files should cite</p> <p>Lau, M., Hirai, R., Mandel, I., Tout, C., 2024, Expansion of Accreting Main-sequence Stars during Rapid Mass Transfer, ApJL, 966, 1</p> <div></div>
Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars: Stationary solutions and modeling potential
<p>This repository contains the material available online that accompanies <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> (ArXiv link). </p> <p>It contains zipped archives that contain inlists and final data products for the MESA stellar evolution code\(^1\) (version 15140), the GYRE stellar pulsation/oscillation code\(^2\) (version 6.0.1) and the AESolver stellar oscillation mode coupling code\(^3\).</p> <p>In the technical information section below you may find a description of the contents of this repository. The abstract of <a href="https://arxiv.org/abs/2311.02972" target="_blank" rel="noopener">Van Beeck et al. (2024)</a> is also available below.</p> <p> </p> <p><em>Footnotes :</em></p> <p><em>\(^1\): see <a href="https://docs.mesastar.org/en/r15140/" target="_blank" rel="noopener">https://docs.mesastar.org/en/r15140/</a> for additional details about the MESA stellar evolution code.</em></p> <p><em>\(^2\): see <a href="https://gyre.readthedocs.io/en/v6.0.1/">https://gyre.readthedocs.io/en/v6.0.1/</a> for additional details about the GYRE stellar pulsation/oscillation code.</em></p> <p><em>\(^3\): the AESolver code can be downloaded from its Github repository: <a href="https://github.com/JVB11/AESolver" target="_blank" rel="noopener">https://github.com/JVB11/AESolver</a>; its documentation may be consulted at <a href="https://jvb11.github.io/AESolver/" target="_blank" rel="noopener">https://jvb11.github.io/AESolver/</a>.</em></p>
Data Tables for Enrichment by Extragalactic First Stars in the Large Magellanic Cloud
<p>Machine-readable data tables for the article, "Enrichment by Extragalactic First Stars in the Large Magellanic Cloud"-- DOI: 10.1038/s41550-024-02223-w. Full descriptions of the tables are in the article, but we briefly summarize here-</p> <p>Table 1 (Table1.csv): This table provides general information on the stars for which we obtained long-exposure Magellan/MIKE data to derive their detailed elemental abundances. Columns include names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, and stellar parameters and metallicities with their respective random uncertainties.</p> <p>Table 2 (Table2.csv): This table provides names, radial velocities, metallicities, and selected elemental abundances with random uncertainties for stars listed in Table 1. [C/Fe]_c indicates carbon abundances that are corrected for the evolutionary state of the star following Placco et al. (2014). Abundances that are upper limits are flagged by the ul_[X/Fe] columns and have "nan" values for the uncertainty.</p> <p>Extended Data Table 1 (Extended_Data_Table1.csv): This table summarizes all of our observations, by providing names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, exposure times, dates of observation, and the instrument for these observations. This table includes stars observed with MagE, those observed with short-exposures with MIKE for just metallicities and carbon abundances, and those flagged as more metal-rich upon initial exposure and hence, not further observed. </p> <p>Extended Data Table 2 (Extended_Data_Table2.csv): This table provides names, followed by stellar parameters, metallicities, and carbon abundances, along with their respective uncertainties, for stars observed with MagE or MIKE for short exposures to just obtain a metallicity and carbon abundance. As in Table 2, abundances that are upper limits are flagged by the ul_[C/Fe] column and "nan" entries for the uncertainty.</p> <p>Supplementary Data 1 (Summary_Data_1.csv or Summary_Data_1.ascii): This table provides the suite of detailed element abundances and uncertainties from the long-exposure MIKE spectra of the stars in Table 1. Columns include the name, atomic number and ionization state of the element (element), the number of features used to estimate the elemental abundance (N), the solar abundance of that element (Solar), the absolute abundance (logeps), the chemical abundance scaled by the solar abundance relative to hydrogen ([X/H]), the ratio with respect to the iron abundance ([X/Fe]), the random uncertainty ([X/H]_err) and an upper limit flag (ul), and errors from propagating the uncertainties in the individual stellar parameters and the overall systematic and total uncertainty ([X/H]_errteff, [X/H]_errlogg, [X/H]_errvt, [X/H]_errsys, [X/H]_errtot). These are followed by the same columns, but with respect to iron (e.g., [X/Fe]_errteff, [X/Fe]_errlogg). Abundances of the CH molecule are indicated by 106.0 in the "element" column. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability.</p> <p>Supplementary Data 2 (Summary_Data_2.csv or Summary_Data_2.ascii): This table summarizes the chemical abundances from individual absorption features for the LMC stars with long-exposure MIKE spectra. The columns include the star name, the atomic number and ionization state of the element (species), the solar abundance of that element (Solar), followed by the wavelength, excitation potential, and loggf of the line (wavelength, expot, loggf), and then the measured equivalent width (EW), absolute abundance (logeps), and a flag indicating whether the abundance is an upper limit (ul). Abundances derived via spectral synthesis have "nan" entries for expot, loggf, and EW, and abundances of the CH molecular band are indicated by 106.0. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability. </p>
Parameter catlogs of "Photometric Stellar Parameters for 195,478 KIC Stars"
<h1>Data Abstract</h1> <p>Here are the parameter catalogs of KIC (Kepler Input Catalog) stars from the paper "Photometric Stellar Parameters for 195,478 KIC Stars." The paper includes two separate catalogs: one for A, F, G, and K type stars, and another for M type stars. Please note that the reliability of M-type star parameters is lower compared to that of AFGK stars, and caution is advised when using these values for further analysis. For this reason, we do not provide isochrone-fitting parameters for M-type stars. For detailed information on each catalog, please refer to the "Catalog Description" section. Each catalog is available in both FITS and CSV formats, with identical data content across these formats.</p> <h1>Article Abstract</h1> <p>The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1\,dex on metallicity [Fe/H], 100\,K on effective temperature $T_{\rm eff}$, and 0.2\,dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. The resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.</p> <h1>Status of Paper Publication</h1> <p>Paper ‘Photometric Stellar Parameters for 195,478 KIC Stars’ is published by <em>ApJS</em>.</p> <h1>Notes of the Data</h1> <div>Please note the following two points:</div> <ol> <li> <div>The A, F, G and K-type star parameter table contains the complete rows of KIC, with a total of 200,038 rows. The M-type star table includes the stars that have been filtered with $(BP-RP)_0$ > 1.8.</div> </li> <li> <div> <p>The total number of stars with parameter measurements is determined as follows:</p> <p>1) For A-, F-, G-, and K-type stars, we include those with metallicity measurements derived from either photometric data synthesized from Gaia XP spectra or KIS photometry, resulting in a total of 190,226 stars.</p> <p>2) For M-type stars, only 5,252 stars with metallicity measurements based on Gaia XP spectra are considered, as the UV radiation of M-type stars is weak, making KIS-based measurements unreliable. Although these KIS-derived values are listed in the table, they are not included in the final count.</p> <p>Thus, the total number of stars with reliable parameter measurements presented in this article is 195,478.</p> </div> </li> </ol> <h1>Catalog Description</h1> <table> <tbody> <tr> <td>Column Name</td> <td>Description</td> </tr> <tr> <td>kepid</td> <td>ID of the KIC star</td> </tr> <tr> <td>degree$\_$ra</td> <td>RA of KIC star</td> </tr> <tr> <td>degree$\_$dec</td> <td>DEC of KIC star</td> </tr> <tr> <td>$G$</td> <td>$G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$G$$\_$err</td> <td>Uncertainty of $G$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$</td> <td>$BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP$$\_$err</td> <td>Uncertainty of $BP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$</td> <td>$RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$RP$$\_$err</td> <td>Uncertainty of $RP$-band photometry from Gaia DR3</td> </tr> <tr> <td>$BP-RP$</td> <td>$BP-RP$ color from Gaia DR3</td> </tr> <tr> <td>$v$</td> <td>$v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$v$$\_$err</td> <td>Uncertainty of $v$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$</td> <td>$b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$b$$\_$err</td> <td>Uncertainty of $b$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$</td> <td>$y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$y$$\_$err</td> <td>Uncertainty of $y$-band photometry synthesized from Gaia DR3 XP spectra</td> </tr> <tr> <td>$U$</td> <td>$U$-band photometry from KIS DR2</td> </tr> <tr> <td>$U$$\_$err</td> <td>Uncertainty of $U$-band photometry from KIS DR2</td> </tr> <tr> <td>$E(BP-RP)$</td> <td>$BP-RP$ excess from our 3-D extinction map</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT/MH$\_$KIS$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$KIS$\_$PHOT$\_$err/MH$\_$KIS$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from KIS colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT/MH$\_$GaiaSyn$\_$PHOT$^a$</td> <td>[Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>FeH$\_$GaiaSyn$\_$PHOT$\_$err/MH$\_$GaiaSyn$\_$PHOT$\_$err$^a$</td> <td>Uncertainty of [Fe/H]/[M/H] from Gaia synthesized colors</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT</td> <td>Photometric Effective temperature</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$PHOT$\_$err</td> <td>Uncertainty of photometric effective temperature</td> </tr> <tr> <td>log $g$$\_$PHOT</td> <td>Photometric Surface gravity</td> </tr> <tr> <td>log $g$$\_$PHOT$\_$err</td> <td>Photometric Uncertainty of surface gravity</td> </tr> <tr> <td>Age$\_$ISO$^b$</td> <td>Age by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$low$^b$</td> <td>16th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>Age$\_$ISO$\_$up$^b$</td> <td>84th percentile of age posterior by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$^b$</td> <td>Mass by isochrone-fitting method</td> </tr> <tr> <td>$M$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of mass fitted by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$^b$</td> <td>Surface gravity by isochrone-fitting method</td> </tr> <tr> <td>log $g$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of surface gravity by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$^b$</td> <td>Effective temperature by isochrone-fitting method</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of effective temperature by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$^b$</td> <td>Luminosity by isochrone-fitting method</td> </tr> <tr> <td>log $L$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of luminosity by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$^b$</td> <td>Radius by isochrone-fitting method</td> </tr> <tr> <td>$R$$\_$ISO$\_$err$^b$</td> <td>Uncertainty of radius fitted by isochrone-fitting method</td> </tr> <tr> <td>FeH$\_$LAMOST/MH$\_$LAMOST$^{a}$</td> <td>[Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$LAMOST$\_$err/MH$\_$LAMOST$\_$err$^{a}$</td> <td>Uncertainty of [Fe/H]/[M/H] from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST</td> <td>Effective temperature from LAMOST DR10</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$LAMOST$\_$err</td> <td>ncertainty of effective temperature from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST</td> <td>Surface gravity from LAMOST DR10</td> </tr> <tr> <td>log $g$$\_$LAMOST$\_$err</td> <td>Uncertainty of surface gravity from LAMOST DR10</td> </tr> <tr> <td>FeH$\_$APOGEE</td> <td>[Fe/H] from APOGEE DR17</td> </tr> <tr> <td>FeH$\_$APOGEE$\_$err</td> <td>Uncertainty of [Fe/H] from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE</td> <td>Effective temperature from APOGEE DR17</td> </tr> <tr> <td>$T_{\mathrm{eff}}$$\_$APOGEE$\_$err</td> <td>Uncertainty of effective temperature from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE</td> <td>Surface gravity from APOGEE DR17</td> </tr> <tr> <td>log $g$$\_$APOGEE$\_$err</td> <td>Uncertainty of surface gravity from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE</td> <td>[M/H] from APOGEE DR17</td> </tr> <tr> <td>MH$\_$APOGEE$\_$err</td> <td>Uncertainty of [M/H] from APOGEE DR17</td> </tr> <tr> <td>pmra</td> <td>Proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmra$\_$err</td> <td>Uncertainty of proper motion in R.A. direction from Gaia DR3</td> </tr> <tr> <td>pmdec</td> <td>Proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>pmdec$\_$err</td> <td>Uncertainty of proper motion in decl. direction from Gaia DR3</td> </tr> <tr> <td>rgeo</td> <td>Geometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$low</td> <td>16th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rgeo$\_$up</td> <td>84th percentile of the geometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo</td> <td>Photogeometric distance from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$low</td> <td>16th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>rpgeo$\_$up</td> <td>84th percentile of the photogeometric distance posterior from Bailor-Jones et.al. 2021</td> </tr> <tr> <td>StarType</td> <td>Type of KIC star, including TO (turn-off), Giant, MS (main sequence), Binary, HS (hot star), Mgaint (M-type giant) and Mdwarf (M-type dwarf)</td> </tr> </tbody> </table> <p><br>a: [Fe/H] for A, F, G and K type stars and [M/H] for M type stars.<br>b: Only for A, F, G and K type stars.</p>
The detection of radio emission from known X-ray flaring star EXO 040830−7134.7
<p>This is the radio light curve of known X-ray flaring star EXO 040830−7134.7 observed by MeerKAT as part of ThunderKAT. These data are part of a publication in the Monthly Notice of the Royal Astronomical Society (Driessen et al., Accepted 2021 November 25. Received 2021 November 25; in original form 2021 August 25).</p> <p>The light curve is from the full-time-integration, full-frequency-integration images of VW Hyi, as processed by the LOFAR Transients Pipeline (<a href="https://tkp.readthedocs.io/en/latest/introduction.html">TraP</a>).</p> <p>The columns in the file are:</p> <ul> <li>mjd: the modified Julian Date (MJD) of the observation. The MJD is given by MJD=JD-2400000.5 where JD is the Julian Date</li> <li>f_int_Jy: the integrated flux density of the source in Jansky (Jy) determined by the LOFAR TraP</li> <li>f_int_err_Jy: the uncertainty on f_int_Jy in Jansky determined by the LOFAR TraP</li> <li>freq_eff_Hz: the effect frequency in Hertz (Hz) as determined by the LOFAR TraP</li> <li>taustart_ts: the ISO 8601 time of the observation in Coordinated Universal Time (UTC)</li> </ul> <p>The files were made using the Pandas package, so we recommend Python users load them using</p> <pre><code>import pandas as pd pd.read_csv(filename, comment='#')</code></pre> <p>If you use the data shared here please ensure that you cite the MNRAS paper (Driessen at al. 2021) and the Zenodo DOI: 10.5281/zenodo.5084298.</p> <p>The MeerKAT telescope is operated by the South African Radio Astronomy Observatory, which is a facility of the National Research Foundation, an agency of the Department of Science and Innovation.<br> LND acknowledges support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694745).</p>
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