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15 results for “stars: abundances”
Data Associated with Chemical Cartography with APOGEE: Two-process Parameters and Residual Abundances for 288,789 Stars from Data Release 17
<p>Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity log(g) in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star's measurements into the amplitudes A_cc and A_Ia and the element-by-element residuals from this two-parameter fit. Here we present the log(g)-calibrated abundances, fit parameters, process amplitudes, and element-by-element abundance residuals of 288,789 stars (310,427 spectra) in APOGEE DR17 that accompany <a href="https://arxiv.org/abs/2403.08067" target="_blank" rel="noopener">the paper</a>.</p> <p>calibration_values_final.dat contains all derived calibration offsets, including the grids of log(g) calibration offsets and zero-point offsets for two-process model analysis. The first five rows of this catalog are reproduced in Table 2 of the paper.</p> <p>logg_calib_example.ipynb is a Jupyter notebook containing Python code to load calibration_values_final.dat, extract the log(g) calibration offsets for specific element, and apply calibration offsets to 10 sample stars.</p> <p>2process_residual_abund_catalog_final.fits is the catalog of 310,427 APOGEE DR17 spectra (288,789 unique stars) containing calibrated abundances, two-process fit parameters, and abundance residuals. A full listing of columns in this catalog is given in Table 5 of the paper.</p> <p>catalog_examples.ipynb is a Jupyter notebook containing Python code to load 2process_residual_abund_catalog_final.fits, cross match with other catalogs (using AstroNN and the APOGEE DR17 Globular Cluster Value-Added Catalog as examples), and make some example plots utilizing the cross-matched data.</p>
The data catalog for Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way
<p>Stellar chemistry contains information on the environment in which the star was born. Therefore, measuring the chemical abundances of stars in the Milky Way, such as the overall metallicity [M/H] and the alpha-abundance [alpha/M], is essential in Galactic astronomy.</p> <p>We estimate ([M/H], [alpha/M]) for giants and dwarfs in low dust extinction region from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 (Abdurro’uf et al. 2022) and the metal-poor star sample of Li et al. (2022).</p> <p>Here, we upload the catalogues of ([M/H], [alpha/M]) for 182 million stars. The data are divided into 10 fits files. The i-th file (i=1,2,...,10) contains stars with E(B-V) value between 0.1*(i-1) and 0.1*i. Because our machine-learning models are trained on stars with low dust extinction (E(B-V)<0.1), we recommend using 48 million stars with low-dust extinction region with 0<E(B-V)<0.1 (table_light_mh_am_0p0ebv0p1.fits). The description for each column of the data is shown in column_description.png. </p> <p>The source paper of this catalog:</p> <ul> <li>Kohei Hatori "Metallicity and alpha-abundance for 48 million stars in low-extinction regions in the Milky Way" <br>https://iopscience.iop.org/article/10.3847/1538-4357/ad9686</li> </ul> <p>References:</p> <div> <div> <div> <ul> <li>Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 1026 35, doi: 10.3847/1538-4365/ac4414</li> </ul> </div> </div> </div> <ul> <li>Li, H., Aoki, W., Matsuno, T., et al. 2022, ApJ, 931, 147, doi: 10.3847/1538-4357/ac6514</li> </ul> <p> </p>
Spectro-photometric distances and self-calibrated abundances for Apogee DR16 RGB stars within the Milky Way disk
<p>The data file contains 48,853 RGB stars from Apogee DR16 within the Milky Way disk, for which we determine spectro-photometric parallax estimates (as described in Hogg et al. 2019, AJ, 158, 147), as well as self-calibrated stellar element abundances. The data set is described in detail and analyzed in Eilers et al. 2022 (arXiv: 2112.03295).</p>
r-process abundances in neutron star merger dynamical ejecta given different fission yields
<p>This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE).</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.</p>
Data from: Reciprocal abundance shifts of the intertidal sea stars, Evasterias troschelii and Pisaster ochraceus, following sea star wasting disease
Disease emergence occurs within the context of ecological communities, and disease driven declines in host populations can lead to complex direct and indirect ecological effects. Varying effects of a single disease among multiple susceptible hosts could benefit relatively resistant species. Beginning in 2013, an outbreak of sea star wasting disease (SSWD) led to population declines of many sea star species along the west coast of North America. Through field surveys and laboratory experiments, we investigated how and why the relative abundances of two co-occurring sea star species, Evasterias troschelii and Pisaster ochraceus, shifted during the ongoing wasting epidemic in Burrard Inlet, British Columbia, Canada. We hypothesized that Evasterias is competitively inferior to Pisaster but more resistant to SSWD. Thus, we predicted that SSWD-induced declines of Pisaster could mitigate the negative effects of SSWD on Evasterias, as the latter would experience competitive release. We document shifts in sea star abundance from 2007-2017: Pisaster abundance and mean size declined during the outbreak, while Evasterias abundance increased from relatively rare to numerically dominant within the intertidal. When exposed to symptomatic sea stars, Pisaster and Evasterias both showed signs of SSWD, but transmission and susceptibility was lower in Evasterias. Despite diet overlap documented in our field surveys, Evasterias was not outcompeted by Pisaster in laboratory trails conducted with the relatively small Pisaster available after the outbreak. Interference competition with larger Pisaster, or prey exploitation by Pisaster during summer when Evasterias is primarily subtidal, may explain the rarity of Evasterias prior to Pisaster declines. Our results suggest that indirect effects mediated by competition can mask some of the direct effects of disease outbreaks, and the combination of direct and indirect effects will determine the restructuring of a community after disturbance.
Carbon and nitrogen abundances as indicators of material mixing in evolved stars
<p>Carbon and nitrogen abundances are among most useful quantitative indicators of mixing processes in evolved stars. Because of the first dredge-up abundances of 12C decrease while abundances of 13C and 14N increase. These alterations become efficient again on the red giant branch when stars reach the so-called luminosity bump, and depend on stellar evolutionary stage, mass, metallicity, rotation, magnetic activity and other parameters and processes. An overview is provided on observational analyses of evolved low mass giants accomplished using the Nordic Optical Telescope.</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>
Data from: Reciprocal abundance shifts of the intertidal sea stars, Evasterias troschelii and Pisaster ochraceus, following sea star wasting disease
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MESA Inlists for: Barium Surface Abundances of Blue Straggler Stars in the Open Clusters NGC 7789 and M67
<p>These are the MESA v15140 inlists and Python script associated with the models presented in Nine et al. (2024). The files are as follows:</p> <p>inlist, inlist_start_header, inlist_to_end_core_h_burn_header, inlist_to_start_he_core_flash_header, inlist_to_end_core_he_burn_header, inlist_to_end_agb_header, inlist_to_wd_header: Pointer files to tell MESA which inlists to read for control, kappa, and PGStar commands.</p> <p>inlist_start, inlist_to_end_core_h_burn, inlist_to_start_he_core_flash, inlist_to_end_core_he_burn, inlist_to_end_agb, inlist_to_wd: MESA inlist files with control, kappa, and PGStar commands for each stage.</p> <p>turnoff_masses.py: a Python script that takes in the file inlist_start_base and rewrites it to create a starting model with the mass of a turnoff star in NGC 7789 (1.8 solar masses), NGC 6819 (1.5 solar masses), M67 (1.3 solar masses), and NGC 188 (1.1 solar masses). This starting model is then used in the following inlists to create AGB star models with these masses.</p> <p>inlist_start_base: The base starting inlist that turnoff_masses.py reads in and alters to create inlist_start.</p>
Data for J-PLUS: Beyond spectroscopy III. Stellar parameters and elemental-abundance ratios for five million stars from DR3
<p>The table contains stellar atmospehric parameters (Teff, logg, [Fe/H]), luminosity classification, element abundance ratios ([C/Fe], [Mg/Fe] and [alpha/Fe]), distance and age for over five million stars derived using J-PLUS DR3 + Gaia EDR3.</p><p>It includes the following parameters:</p><p>ra: Right Ascension from J-PLUS DR3 (J2000)</p><p>dec: Declination from J-PLUS DR3 (J2000)</p><p>gl: Galactic longitude derived from ICRS coordinates</p><p>gb: Galactic latitude derived from ICRS coordinates</p><p>mag[12]: Magnitudes of J-PLUS twelve bands (r, g, i, z, u, J0378, J0395, J0410, J0430, J0515, J0660, J0861)</p><p>err_mag[12]&Uncertainties of magnitudes of J-PLUS twelve bands (r, g, i, z, u, J0378, J0395, J0410, J0430, J0515, J0660, J0861)</p><p>gp1/rp1/ip1: Magnitudes from Pan-STARRS1</p><p>err_gp1/rp1/ip1: Uncertainties of magnitudes from Pan-STARRS1</p><p>G/BP/RP: Magnitudes for the Gaia three bands from EDR3; note G represents a calibration-corrected G magnitude</p><p>err_G/BP/RP: Uncertainties of magnitudes for the three Gaia bands from EDR3</p><p>ebv_sfd: Value of E(B - V) from the extinction map of SFD98, corrected for a 14% systematic</p><p>[Fe/H]: Photometric metallicity</p><p>err_[Fe/H]: Uncertainty of photometric metallicity</p><p>flg_[Fe/H]: Quality flag of [Fe/H]</p><p>[C/Fe]: Photometric carbon-to-iron abundance ratio</p><p>err_[C/Fe]:Uncertainty of photometric carbon-to-iron abundance ratio</p><p>flg_[C/Fe]: Quality flag of [C/Fe]</p><p>[Mg/Fe]: Photometric magnesium-to-iron abundance ratio</p><p>err_[Mg/Fe]: Uncertainty of photometric magnesium-to-iron abundance ratio</p><p>flg_[Mg/Fe]: Quality flag of [Mg/Fe]</p><p>[alpha/Fe]: Photometric alpha-to-iron abundance ratio</p><p>err_[alpha/Fe]: Uncertainty of photometric alpha-to-iron abundance ratio</p><p>flg_[alpha/Fe]: Quality flag of [alpha/Fe]</p><p>Teff: Effective temperature</p><p>err_Teff: Uncertainty of effective temperature</p><p>logg: Surface gravity</p><p>err_logg: Uncertainty of surface gravity</p><p>dist: Distance </p><p>err_dist: Uncertainty of distance</p><p>flg_dist: Flag to indicate the method used to derive distance, which takes the values ``parallax", ``CMF", and ``NO"</p><p>age: Stellar age</p><p>err_age: Uncertainty of stellar age</p><p>rv: Radial velocity</p><p>err_rv: Uncertainty of radial velocity</p><p>flg_rv: Flag to indicate the source of radial velocity, which takes the values ``GALAH", ``APOGEE'', ``Gaia",``RAVE", ``LAMOST", ``SEGUE"</p><p>parallax: Parallax from {it Gaia} EDR3</p><p>err_parallax: Uncertainty of parallax from {it Gaia} EDR3</p><p>pmra: Proper motion in Right Ascension direction from Gaia EDR3</p><p>err_pmra: Uncertainty of proper motion in Right Ascension direction from Gaia EDR3</p><p>pmdec: Proper motion in Declination direction from Gaia EDR3</p><p>err_pmdec&Uncertainty of proper motion in Declination direction from Gaia EDR3</p><p>ruwe: Renormalised unit weight error from Gaia EDR3</p><p>type: Flag to indicate classifications of stars, which takes the values ``dwarf" and ``giant" </p><p>subtype: Flag to indicate further sub-classifications of dwarf stars, which takes the values ``TO", ``MS" and ``Binary"</p>
Radiative acceleration calculation methods and abundance anomalies in Am stars
<p>This archive contains stellar structures used to create the figures of surface abundance evolution with time of the paper entitled "Radiative acceleration calculation methods and abundance anomalies in Am stars" to be published in Astronomy & Astrophysics.</p> <p>The first directory level refers to the dataset used to compute the Rosseland mean opacities (OP = Opacity Project for instance), the second to the stellar mass (in solar mass unit), and the third to the g_rad calculation method and the prescriptions used for macroscopic transport processes (e.g. SVP2004_RMT for g_rad computed with the 2004 version of the SVP approximation and the RMT turbulence model).</p> <p>In each of these directories, the stellar structure file name contains the age (e.g. "structure.00215.78.gz" for an age of 215.78 Myr). </p> <p>The first line of each stellar structure file shows the meaning of each field. The columns are separated with tabulations to allow easy an importation in spreadsheets. All quantities are expressed in cgs units, the abundances in mass fractions.</p>
Two decades of change in sea star abundance at a subtidal site in Puget Sound, Washington
<p>Long-term datasets can reveal otherwise undetectable ecological trends, illuminating the historical context of contemporary ecosystem states. We used two decades (1997–2019) of scientific trawling data from a subtidal, benthic site in Puget Sound, Washington, USA to test for gradual trends and sudden shifts in total sea star abundance across 11 species. We specifically assessed whether this community responded to the sea star wasting disease (SSWD) epizootic, which began in 2013. We sampled at depths of 10, 25, 50 and 70 m near Port Madison, WA, and obtained long-term water temperature data. To account for species-level differences in SSWD susceptibility, we divided our sea star abundance data into two categories, depending on the extent to which the species is susceptible to SSWD, then conducted parallel analyses for high-susceptibility and moderate-susceptibility species. The abundance of high-susceptibility sea stars declined in 2014 across depths. In contrast, the abundance of moderate-susceptibility species trended downward throughout the years at the deepest depths – 50 and 70 m – and suddenly declined in 2006 across depths. Water temperature was positively correlated with the abundance of moderate-susceptibility species, and uncorrelated with high-susceptibility sea star abundance. The reported emergence of SSWD in Washington State in the summer of 2014 provides a plausible explanation for the subsequent decline in abundance of high-susceptibility species. However, no long-term stressors or mortality events affecting sea stars were reported in Washington State prior to these years, leaving the declines we observed in moderate-susceptibility species preceding the 2013–2015 SSWD epizootic unexplained. These results suggest that the subtidal sea star community in Port Madison is dynamic, and emphasizes the value of long-term datasets for evaluating patterns of change.</p>
ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS): I. Potential Metal Enrichment of the Exoplanet AF Lep b and a Novel Retrieval Approach for Cloudy Self-luminous Atmospheres
<p>This dataset contains materials presented in the paper ``ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS): I. Potential Metal Enrichment of the Exoplanet AF Lep b and a Novel Retrieval Approach for Cloudy Self-luminous Atmospheres'' (Zhang et al. 2023) for publication by Astronomical Journal. Please cite this paper and the Zenodo repository if these data are used in your work.</p> <p>A readme file <strong>README_ELPIS_AFLep.dat</strong> describes the content of the entire dataset. Description of other files:</p> <ul> <li><strong>tull_spectrum_AF_Lep_A.zip</strong>: Tull spectrum of the host star AF Lep A (Section 2)</li> <li><strong>stellar_parameters_AF_Lep_A.zip</strong>: Stellar parameters of AF Lep A inferred by isochrones (Section 3)</li> <li><strong>orbit_analysis_AF_Lep_b.zip</strong>: Orbit analysis of the exoplanet AF Lep b (Section 4)</li> <li><strong>RCE_dlnT_dlnP_forward_models.zip</strong>: The radiave-convective equilibrium (dlnT/dlnP) profiles of several sets of forward models (shown in Figure 5 and Section 6.2)</li> <li><strong>mini_grid_emission_spectra.zip</strong>: A mini-grid of modeled emission spectroscopy (Figure 6 and Section 6.4)</li> <li><strong>fitted_TP_profiles.zip</strong>: Fitted T-P profiles from multiple retrieval runs (Section 7)</li> <li><strong>fitted_model_spectra.zip</strong>: Fitted model spectra from multiple retrieval runs (Section 7)</li> <li><strong>retrieval_parameter_posteriors.zip</strong>: Parameter posteriors from multiple retrieval runs (Section 7).</li> </ul>
Correlations between the abundances of Barium stars, the AGB model residuals and the delta parameter
<p>Additional figures for Világos et al. 2024, Sect. 3.2. For the sample of Barium stars, correlations between the observationally dervied abundances; the residuals between the AGB models and the abundances; and the delta parameter.</p> <p>The figures are divided into the following directories:</p> <ul> <li>'1-obs_feh': Abundances and elemental ratios of different peaks as a function of the [Fe/H] (Sect. 3.2.1 and Fig. 5).</li> <li>'2-res_feh': Residuals of the abundances and elemental ratios as a function of the [Fe/H] (Sect. 3.2.2 and Fig. 6).</li> <li>'3-obs_obs': Abundances as a function of the other abundances (Sect. 3.2.3 and Fig. 7).</li> <li>'4-res_res': Residuals of the abundances as a function of the other residuals (Sect. 3.2.4 and Fig. 8).</li> <li>'5-res_obs': Residuals of the abundances as a function of the abundances (see Sect. 3.2.5 and Fig. 9).</li> <li>'6-delta': Abundances and residuals as a function of the delta parameter, that indicates the fraction of AGB material in the Ba star envelope (Sect. 3.2.6 and Fig. 10).</li> </ul> <p>For figures in directories 1-2, we refer to some elements as [A/B], although sometimes [B/A] vs. [Fe/H] is shown. Visually, this swapping of the numerator and denominator elements means a vertical reflection around the y = 0 axis. Thus, $r_S$ and $a$ for [B/A] vs. [Fe/H] have the same absolute value but different sign than for [A/B] vs. [Fe/H].</p> <p>For figures in directories 3-5, if we refer to [A/Fe] vs [B/Fe], but plot [B/Fe] vs [A/Fe], the sign of the relationship (correlation or anti-correlation) remains the same. The swap of the two axes results in a reflection around a diagonal line with a slope of 1. The resulting slope is therefore $1/a$, while $r_S$ is the same in both cases.</p>
Two decades of change in sea star abundance at a subtidal site in Puget Sound, Washington
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