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34 results for “core collapse”
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: multi-messenger signals
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes the multi-messenger data from the STIR simulations. The filename indicates the turbulent mixing parameter a and progenitor mass m of the simulation. Columns are time [s], shock radius [cm], explosion energy [ergs], electron neutrino mean energy [MeV], electron neutrino rms energy [MeV], electron neutrino luminosity [10^51 ergs/s], electron antineutrino mean energy [MeV], electron antineutrino rms energy [MeV], electron antineutrino luminosity [10^51 ergs/s], x neutrino mean energy [MeV], x neutrino rms energy [MeV], x neutrino luminosity [10^51 ergs/s], gravitational wave frequency from eigenmode analysis of the protoneutron star structure [Hz]. Note that the x neutrino luminosity is for <strong>one</strong> neutrino flavor - to get the total mu/tau neutrino and antineutrino luminosities requires multiplying this number by 4.</p> <p>v1.1 - removed unnecessary duplicate files</p> <p>v1.2 - upload failed. Obsolete.</p> <p>v1.3 - packaging alpha values as separate tar files for easy download.</p>
Stellar Mass Black Hole Formation and Multimessenger Signals from Three-dimensional Rotating Core-collapse Supernova Simulations
<p>Gravitational waveforms from <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...914..140P/abstract">Pan et al. (2021) .</a></p> <p>They are the 40 solar mass model from Woosley & Heger 2007 with different<br>initial rotational speeds:</p> <p>Model NR: Omega_0 = 0.0 rad/sec<br>Model SR: Omega_0 = 0.5 rad/sec<br>Model FR: Omega_0 = 1.0 rad/sec</p> <p>/* File content */</p> <p>They are 5 files for each simulation.</p> <p>Files "data_s40_[model]_d3_[Cross/Plus][Equator/Pole].d" are GW strains for different<br>mode of polarization [h_plus or h_cross] and viewing angles [equator or pole].</p> <p>1st column is time [s] in postbounce. <br>2nd column s the GW strain, assuming d=10 kpc.<br> <br>Files "data_s40_[model]_d3_Idotdot.d" are the second time derivative of the<br>quadruple moments.</p> <p>1st: postbounce time [s] <br>2nd: Idd_xx [cgs]<br>3rd: Idd_xy = Idd_yx [cgs]<br>4th: Idd_yy = Idd_yy [cgs]<br>5th: Idd_zx = Idd_zx [cgs]<br>6th: Idd_zy = Idd_zy [cgs]<br>7th: Idd_zz = Idd_zz [cgs]</p> <p> </p>
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
Core-Collapse Supernova
<p>Collection of certified trajectories, covering a wide range of<sup> </sup>core-collapse supernova nucleosynthesis conditions. Every trajectory comes with the selected initial abundances and other key information, such as the reference of the publication, the stellar region from where it was extracted, the mass of the progenitor star and its metallicity.</p>
Raw D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Growing Season D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Average D and 18O isotope data for a core from the center and moat of the BBC collapse scar: Bonanza Creek Experimental Forest Flood Plains
This data set contains D and 18O data for a core from the center and moat (0 and 6 m) of the BBC collapse scar. Two well-preserved cores from the center of the bog was collected in March 2003 with a gasoline-powered permafrost corer. Cores were stored frozen and sampled using a radial saw. The core was sampled every two cm for macrofossil, diatom analysis and chemistry. Oxygen isotope ratios in aquatic cellulose has been shown to be a reliable tracer of lakewater isotope ratios (Sauer et al., 2001). I predicted that ?D and ?18O signatures in Sphagnum leaves would respond to enrichment and depletion in the bog water over time, providing an independent line of evidence for diatom-inferred hydrologic change. All isotope samples were processed by the Alaska Stable Isotope Facility using a Thermo Finnigan TC EA and Deltaplus XL mass spectrometer (Thermo Electron Corporation, Bremen, Germany) in continuous flow mode. Data were reported relative to standard mean ocean water (SMOW). Instrument precision was 1.2 ? SE for ?D and 0.3 ? SE for ?18O. I separated Sphagnum leaves from the bog and moat cores, and ran whole organic matter samples for ?D and ?18O stable isotopes. To determine the correlation between ?D and ?18O stable isotopes in peat leaves and bog water, I analyzed samples of Sphagnum and adjacent surface water. I collected surface water samples without headspace and refrigerated at 4 ?C until sample analysis. Snow samples were kept frozen until analysis. I refrigerated surface sample Sphagnum leaves from branches directly below the capitulum after harvest until analysis for ?D and ?18O stable isotopes. I identified Sphagnum samples to species. This data set was collected to relate ?D and ?18O to fires, changes in succession, changes in hydrology, and diatom assemblages.
Soil data for cores from a transect from the center of the BBC collapse scar into the surrounding burn
This data set contains soil data for cores from a transect from the center of the BBC collapse scar (0 m) into the surrounding burn (30 m). Thirty-five cores were collected soil cores along the transect in March 2003. We drilled cores using a gasoline powered, permafrost corer while soils were frozen. Two to four cores were drilled every 3 m along the transect, yielding a total of 35 cores. We stored cores frozen and cut sample sections using a radial saw. Cores were sampled at the interfaces between different soil layers. We classified soils using the Canadian Soil Classification system (Soil Classification Working Group 1998) identifying fibric, mesic, and humic organic horizions and the A and C mineral horizons. Nine cores were sampled only to the mineral boundary. We measured bulk density, %C and %N for all soil samples. The pH of sample was determined using litmus paper. We oven-dried at 50 - 65 degC and ground all samples before analysis. We analyzed samples for %C and %N using a Carlo Erba EA1108 CHNS analyzer (CE Instruments, Milan, Italy) and a COSTECH ECS 4010 CHNS-O analyzer (Costech Analytical Technologies Inc., Valencia, CA,USA). Sample standard errors were +/- 0.01% for nitrogen, +/- 0.45% for carbon. To indicate fire events in the surrounding ecosystem, charcoal layers in the cores were quantified. We estimated charcoal by emptying dried samples of a known volume and depth (on mean 4.5 cm3) over a 10 cm x 10 cm grid and counting macroscopic charcoal fragments (greater than 0.05 mm in diameter) in each cm grid cell.
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
Equation of State Effects on Gravitational Waves from Rotating Core Collapse
<p>Gravitational waveforms from 1824 fiducial and detailed electron capture simulations, sampled at 65535 Hz. The file is in HDF5 format, using the flags {dtype="f4",compression="gzip",shuffle=True,fletcher32=True}. Each group is contained in the "waveforms" top-level group and is named with the "A" and "omega_0" values from Equation 5 and the EOS. In each sub-group is a dataset containing timestamps in seconds (t=0 is core bounce) and a dataset containing the strain multiplied by the distance in centimeters. The values of A in kilometers, omega_0 in radians/s, and the EOS are stored as attributes of each group.</p> <p>In addition, the Ye(rho) profiles are stored in the "yeofrho" top-level group. Each sub-group is labeled by the EOS used to generate the profile.</p> <p>Finally, select reduced data is stored in the "reduced_data" top-level group. The following quantities are each stored as a 1824-element array, where elements of the same index from different datasets correspond to the same 2D simulation.</p> <p>A(km) -- differential rotation parameter in Equation 5<br> D*bounce_amplitude_1(cm) -- The minimum of the first (negative) GW strain peak, multiplied by distance.<br> D*bounce_amplitude_2(cm) -- The maximum of the second (positive) GW strain peak, multiplied by distance.<br> EOS -- the equation of state used in the simulation<br> MbarICgrav(Msun) -- gravitational mass of the inner core, averaged over time after core bounce<br> Mgrav1_IC_b(Msun) -- gravitational mass of the inner core at bounce<br> Mrest_IC_b(Msun) -- rest mass of the inner core at bounce<br> SNR(aLIGOfrom10kpc) -- signal to noise ratio of the GW signal, assuming a distance of 10kpc and aLIGO sensitivity<br> T_c_b(MeV) -- central temperature at bounce<br> Ye_c_b -- central electron fraction at bounce<br> alpha_c_b -- central lapse at bounce<br> beta1_IC_b -- ratio of rotational kinetic to gravitational potential energy of the inner core at bounce<br> fpeak(Hz) -- frequency of the post-bounce GW oscillations<br> j_IC_b() -- angular momentum of the inner core at bounce<br> omega_0(rad|s) -- initial (pre-collapse) rotation rate used in Equation 5<br> omega_max(rad|s) -- maximum rotation rate achieved outside of 5km<br> rPNSequator_b(km) -- radius of the rho=10^11 g/ccm contour along the equator at bounce<br> rPNSpole_b(km) -- radius of the rho=10^11 g/ccm contour along the pole at bounce<br> r_omega_max(km) -- radius where omega_max occurs<br> rho_c_b(g|ccm) -- central density at bounce (not time averaged)<br> rhobar_c_postbounce(g|ccm) -- central density time averaged after bounce<br> s_c_b(kB|baryon) -- central entropy at bounce<br> t_postbounce_end(s) -- time of the end of the postbounce signal (t=0 is core bounce)<br> tbounce(s) -- time of core bounce (t=0 is the beginning of the simulation)<br> </p>
Dataset for Bate (2022): Dust coagulation during the early stages of star formation: molecular cloud collapse and first hydrostatic core evolution
<p>This data set contains 12 smoothed particle hydrodynamics (SPH) dump files that were used to produce some of the figures in the journal paper:</p> <p>Bate, Matthew. R., 2022, Monthly Notices of the Royal Astronomical Society, accepted 13 May 2022</p> <p>Each of the SPH dump files is from a different calculation of the early stages of star formation: the gravitational collapse of a molecular cloud core, including dust coagulation. Each SPH dump file gives the state of the SPH calculation when the maximum temperature reached 1500 K, except for the beta=0.05 cases which give the state when the maximum hydrogen number density reaches 10^{14} cm^{-3}. The calculations were each performed using 3 million SPH particles and differed by their initial rotation rate, which was parameterised by beta=0, 0.0025, 0.005, 0.01, 0.02, and 0.05 (the magnitude of the ratio of the rotational and gravitational potential energies). Dump files from calculations that include and exclude envelope turbulence are provided (both are used for Figure B1). The dump files associated with each calculation are:</p> <p>beta=0: B1M0123 (does not include envelope turbulence)<br> beta=0.0025: B1M2123 (does not include envelope turbulence)<br> beta=0.005: B1M5123 (does not include envelope turbulence)<br> beta=0.01: B1M1128 (does not include envelope turbulence)<br> beta=0.02: B1M2126 (does not include envelope turbulence)<br> beta=0.05: B1M5109_b05_NoEnvTurb (does not include envelope turbulence)</p> <p>beta=0.0: B1M0123_b0_EnvTurb<br> beta=0.0025: B1M2177_b0025_EnvTurb<br> beta=0.005: B1M5209_b005_EnvTurb<br> beta=0.01: B1M1219_b01_EnvTurb<br> beta=0.02: B1M2221_b02_EnvTurb<br> beta=0.05: B1M5321_b05_EnvTurb</p> <p>The SPH dump files are Fortran binary files written in big endian format and generated by the sphNG code (Benz 1990; Bate 1995; Bate & Keto 2015). They can be read, visualised, and manipulated using the free, publicly available SPLASH visualisation code (which reads sphNG dump files), written by Daniel J. Price, that can be downloaded from: </p> <p>http://users.monash.edu.au/~dprice/splash/ </p> <p>The SPLASH configuration files used to produce Figs. 10,11,12,and B1 in Bate (2022) are included with this dataset in a gzipped tar file.</p> <p> </p>
Convective boundary mixing in a post-He core burning massive star model: Collapse and starlog data
<p>The starlog data and collapse profiles from the publication, Convective boundary mixing in a post-He core burning massive star model. </p> <p>The full directories including the MESA profiles can be found here: http://www.canfar.net/storage/list/nugrid/data/projects/Davis2019_CBM_M25</p>
Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse
<p>Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure. </p>
Equation of state for simulations of core-collapse supernovae and neutron-star mergers
<p>We construct a new equation of state (EOS) for numerical simulations of core-collapse supernovae and neutron-star mergers based on an extended relativistic mean-field model with a small symmetry energy slope L, which is compatible with both experimental nuclear data and recent observations of neutron stars. The new EOS table (EOS4) based on the extended TM1 (TM1e) model with L=40 MeV is designed in the same tabular form as the commonly used Shen EOS (EOS2) based on the original TM1 model with L=110.8 MeV. This is convenient and useful for performing numerical simulations and examining the influences of symmetry energy and its density dependence on astrophysical phenomena. </p> <p> </p>
A Detailed Comparison of Multi-Dimensional Boltzmann Neutrino Transport Methods in Core-Collapse Supernovae
<p>This dataset contains results of 1D and 2D static neutrino transport calculations using discrete ordinates and Monte Carlo methods, as described in the 2017 paper by the same name. The results include the neutrino momentum space grids used for the simulations, the full spectral and angular distribution functions, energy-dependent angular moments of the distribution functions, and neutrino heating rates. The HDF5 dataset name corresponds to the simulation of the same name in the 2017 paper, Table 1. Also included are the fluid backgrounds and opacities. See the readme for a description of all quantities in the datasets.</p> <p>There are also two simulation setups including all initial conditions and parameters for 1D and 2D Monte Carlo calculations using the open-source code Sedonu. This accompanies Sedonu commit 8bd509b27aa0461df26b194df65b4526d6c153fe.</p>
Asymmetric core collapse of rapidly rotating massive star
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.474.2419G/abstract">Gilkis (2018)</a>. MESA version 7624.</p> <p>Publication DOI: 10.1093/mnras/stx2934<a href="https://doi.org/10.1093/mnras/stx2934">10.1093/mnras/stx2934</a></p>
The Impact of Nuclear Reaction Rate Uncertainties on the Evolution of Core-collapse Supernova Progenitors
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...19F/abstract">Fields et al. (2018)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4365/aaa29b">10.3847/1538-4365/aaa29b</a></p>
Light-curve and spectral properties of ultrastripped core-collapse supernovae leading to binary neutron stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.466.2085M">Moriya et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stw3225">10.1093/mnras/stw3225</a></p>
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