adsabs/AstroCLIMB huggingface dataset:
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: The TARDIS spectral-synthesis models on days 131 (left) and 480 (right), for the reference density/abundance structure extrapolated from the analysis for the earlier epochs (blue). Also shown is the same model but with the abundance suppression of Ca and heavier elements above 1000 km s−1 (red). As the model input, the bolometric luminosity and the photospheric velocity are set as follows; 107.05 L ⊙ and 230 km s−1 on day 131; 106.05 L ⊙ and 35 km s−1 on day 480. The photospheric temperature is ∼5500 K on day 131 and ∼4100 K on day 480.
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: A roughly expected range of the photospheric velocity as a function of time. For the reference model (thin black curves; Section 4.1) and the C+O-rich inner component model (blue lines; see Section 4.3), two curves are shown for each model with a constant opacity of 0.025 g cm−1 and 0.5 g cm−3. For the 56Ni-rich model (Section 4.3), the same range is shown by the red-shaded area. For comparison, the photospheric velocity evolution derived by Barna et al. (2021) for the early phase is shown by a thick black curve, and the photospheric velocities in the late phase adopted in the TARDIS calculations in this work (Section 4.3) are shown by black-filled squares.
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: The distribution of the optical depths of the allowed transitions based on the expansion opacity formalism, for the reference model on days 131 and 480. The contributions from Fe II (blue) and Fe I (red; rarely seen in this model) are shown, as well as the total optical depth including contributions from other species (green). For comparison, the observed spectra of SN 2019muj at the corresponding epochs are shown with an arbitrary flux scale.
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: The spectra around [O I]λλ6300, 6363, on day 131 and day 480 (black). To place upper limits on the fluxes, we plot the Gaussian profiles adopting the same line profile with [Ca II] but with the flux set not to exceed the observed flux. The line ratio between the two components is fixed to be 3:1 (red) or 1:1 (blue). The flux scale for the spectrum on day 480 is multiplied by a factor of 10 for visualization purposes. Also plotted for comparison is a spectrum of SN Iax 2010ae at +252 days since the maximum light (gray) with an arbitrarily scaled flux level.
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: The spectrum of SN 2019muj on day 131 in 4550–5350 Å. Plotted here for comparison are the same Gaussian profiles for some strong allowed Fe II (red), Co II (blue), Mg II (magenta), and Si II (green) lines, adopting the profile identical to that for the Ca II NIR on day 131.
Paper: Properties of Type Iax Supernova 2019muj in the Late Phase: Existence, Nature, and Origin of the Iron-rich Dense Core
Authors: Keiichi Maeda, Miho Kawabata
Figure caption: The TARDIS spectral-synthesis models on days 131 (left) and 480 (right), for the density/abundance structure with the inner dense core; the 56Ni-rich compositions (red) and the C+O-rich compositions (blue). As the model input, the bolometric luminosity and the photospheric velocity are set as follows; 107.05 L ⊙ and 230 km s−1 on day 131, and 106.05 L ⊙ and 35 km s−1 on day 480. The photospheric temperature is 5800 K on day 131 and 4100 K on day 480 for the 56Ni-rich model; ∼7500 K on day 131 and ∼4100 K on day 480 for the C+O-rich model.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: 68% contour luminosity density result for Model A as a function of z at 12 wavelengths (shaded orange region). The red squares are the data from Saldana-Lopez et al. (2021), the violet diamonds represent the data from Andrews et al. (2017), and the blue downward-pointing triangles represent the data from Tresse et al. (2007). The rest of the data from galaxy surveys are plotted as the black circles. All of these data were used in the model fit, while the green upward-pointing triangles represent older, superseded data from Sawicki & Thompson (2006) that were not used in the fit. The blue curves are the luminosity densities from the older Model C from Finke et al. (2010). The wavelengths of the data and models are labeled on the plots.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: 68% contour of the star formation rate density (SFRD), ψ(z), as a function of redshift z for our models, as described in the legend. Also shown are the SFRD from Madau & Dickinson (2014) and Madau & Fragos (2017). Models A and C are nearly indistinguishable on the left plot.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: Simple stellar population spectra for t = 10 Myr for different metallicities; IR emission increases and UV emission decreases for increasing metallicity (Z).
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: Rest-frame wavelength and redshift of the luminsity density data from the literature that we use. Data from Tresse et al. (2007), Andrews et al. (2017), and Saldana-Lopez et al. (2021) are represented by symbols as shown in the legend; all other sources are represented by circles.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: The 68% contour extragalactic background light (EBL) absorption optical depth as a function of observed energy for different redshift ranges for our Model A. The Large Area Telescope absorption measurements from Abdollahi et al. (2018) and the imaging atmospheric Cherenkov telescope measurement from Desai et al. (2019) are shown as the black circles and red squares, respectively. The blue curve is the older Model C from Finke et al. (2010). Note the different energy scales on the different plots.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: 68% contour luminosity density result for Model A as a function of λ at four redshifts (shaded orange region). The red squares are the data from Saldana-Lopez et al. (2021), the violet diamonds represent the data from Andrews et al. (2017), and the blue downward-pointing triangles represent the data from Tresse et al. (2007). The rest of the data from galaxy surveys used in the fit are plotted as the black circles. The blue curves are the luminosity densities from the older Model C from Finke et al. (2010). The redshifts of the data and models are labeled on the plots.
Paper: Repeated Mergers of Black Hole Binaries: Implications for GW190521
Authors: Oliver Anagnostou, Michele Trenti, Andrew Melatos
Figure caption: The percentage of chains with length L from our Monte Carlo method used to calculate recoil velocities with randomized spin in postprocessing, plotted on a vertical log scale. Approximately 99.3% of the chains reach L = 1, approximately 0.719% reach L = 2, approximately 0.0112% reach L = 3, and approximately 3.06 × 10−4% reach L = 4. There are no chains with length L ≥ 5.
Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History
Authors: Justin D. Finke, Marco Ajello, Alberto Domínguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez
Figure caption: 68% contour EBL intensity results as a function of λ at z = 0 for Models A and B. A variety of lower limit data and models curves are shown as well, as seen in the legend.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption: Schematic showing the flow of our U-Net C-VAE architecture. Our model can be viewed as four interconnected parts: an encoder for the dark matter fields, an encoder for the hydrodynamical fields, a variational block, and a decoder. The network is constructed such that after training the hydrodynamical block can be removed and the latent space sampled from a unit Gaussian to generate the corresponding hydrodynamical field. Plotted is only the dark matter density and the baryon temperature, but the model also is fed as an input the dark matter velocity and outputs the baryon density and line-of-sight velocity. The term “dark matter encoder” is used as the model can be viewed as encoding the dark matter structure over the course of training, but we do not explicitly train a model to decode this field. The full network architecture can be found online at https://github.com/bhorowitz/HyPhy-public.
Paper: Spitzer/IRS Full Spectral Modeling to Characterize Mineralogical Properties of Silicate Dust in Heavily Obscured AGNs
Authors: T. Tsuchikawa, H. Kaneda, S. Oyabu, T. Kokusho, H. Kobayashi, Y. Toba
Figure caption: Relations of the properties of silicate with the optical depth of the 10 μm silicate absorption feature, τ 10, shown only for high-L IR sources with ﹩{\mathrm{log}}_{10}({L}_{\mathrm{IR}}/{L}_{\odot })\gt 11.2﹩. The Pearson’s correlation coefficient and the p-value are shown in the top left corner.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption: Diagram showing the broadcasting of the latent space samples into the same dimensionality as the downsampled dark matter field. This stack will comprise the filters passed to the upsampling convolutional layers in the hydrodynamics decoder. This allows changing input dark matter map sizes during generation while keeping the same dimensionality of the latent space and avoiding the need for dense layers. The presence of dense layers during generation would implicitly fix the box size and not allow a fully convolutional structure. Note that in our model each filter is three-dimensional, not two-dimensional as shown here.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption: A typical example single slice of the HYPHY mapping is shown. On the left are the test input dark-matter-only maps and in the center are the hydrodynamical fields (baryon density, velocity, temperature) are compared. There is strong qualitative agreement, with the network accurately learning various characteristics of the hydrodynamical fields including the variable baryon pressure smoothing and thermal properties. On the far right the networks’ estimated variance is shown, calculated from 1000 samples over the latent space, normalized by the mean value of the field.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption: Same as Figure 3, but showing one of the worse reconstructed test boxes. The characteristics of moderately large shocks are difficult for our network to learn, resulting in a less sharp feature. However, this uncertainty in the reconstruction is captured when sampling over latent space, as we see significant variance in the shocked region. If we look at the individual posterior samples this is even more visually apparent; see Figure 9.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption: Top: transfer function, defined as the ratio between the large-volume HYPHY run with the simulated NYX. Bottom: correlation coefficient between the two boxes, defined as ﹩{R}_{c}(k)={P}_{\mathrm{nyx},\mathrm{hyphy}}/\sqrt{{P}_{\mathrm{nyx}}{P}_{\mathrm{hyphy}}}﹩, where P nyx,hyphy is the cross-power between the two fields.
Paper: Spitzer/IRS Full Spectral Modeling to Characterize Mineralogical Properties of Silicate Dust in Heavily Obscured AGNs
Authors: T. Tsuchikawa, H. Kaneda, S. Oyabu, T. Kokusho, H. Kobayashi, Y. Toba
Figure caption: Same as Figure 9, but the data points are color-coded according to the optical depth of the 6 μm H2O ice absorption, τ 6.
Paper:  HYPHY: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics
Authors: Benjamin Horowitz, Max Dornfest, Zarija Lukić, Peter Harrington
Figure caption:  HYPHY run on a box significantly larger than the training volumes. The fully convolutional nature of the network allows the network to be run on any box that is a multiple of a base dimension (currently 8 pixels) and has the same spatial resolution. There are no obvious artifacts caused by the increased image size.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: Comparison of the calculated vs. observed mass-loss rates for the formulae we have found and other works. The solid line shows our PL strip fit and matches a 1:1 line by construction (Table 5). The dotted lines show the results of the linear fits (Table 4); the green diagonal line for the PL strip method coincides with what was found by fitting ﹩\mathrm{log}L(\mathrm{log}\dot{M},\mathrm{log}P)﹩ (black dotted line). The dashed lines show our fits to Equations (22)–(28). The formulae in those equations have been used to calculate a mass-loss rate (y-axis), and then a line has been fitted to the result of the formula vs. the observed mass-loss rate (x-axis). Note that the fit lines have been extended beyond the range of data in order to display the formulae in a single plot and to show their divergent behavior. Nearly all the other relations show the low-slope signature of regression dilution, except the formula from Vassiliadis & Wood (1993). Note that this formula is offset in the M0 panel, due to a difference in mass-loss rate scaling. The spread of data around the various lines can be found in Tables 6 and 7.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: Comparison of the pulsation–mass–radius (PMR) relations found in this work and Trabucchi et al. (2019) (solid lines, black for M0, red for C0, and orange for first overtone) compared to the frequently used relations from Fox & Wood (1982) (dotted–dashed) and Ostlie & Cox (1986) (dashed; blue for fundamental mode, purple for first-overtone mode). (a) The mass dependence of the relations is removed and (b) the radius dependence is removed, to project a three-dimensional fit into two dimensions. The models used to determine the relations are marked by dots of the same color on each plot. Note that neither the Fox and Wood formula nor the two Ostlie and Cox formula distinguish M and C stars. While this does not seem to have a significant impact on the relation between pulsation period and radius, we can see a distinct offset when comparing pulsation period and mass. The radius dependences of all formulae are in general agreement with each other. The mass dependences of the formulae are generally in agreement, but with different scaling coefficients; the first-overtone formulae have a small difference while our fundamental-mode formulae agree much better with the Fox and Wood formula than the Ostlie and Cox formula.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: The evolution of the stars using the derived mass-loss formulae (Equation (16), Table 5) is superimposed on the distribution (contours) with the death line indicated (dotted line). Lines of constant mass (dashed lines) and evolutionary tracks (dotted–dashed lines) are also included. The pattern is clear: before the death line, the mass changes relatively little; after, it is seen to be decreasing. The horizontal axis is also a time axis; ﹩{\rm{\Delta }}t=({\rm{\Delta }}{\rm{log}}L/2.3)\times {t}_{{\rm{ev}}}﹩, so with t ev = 3.2 Myr, ﹩{\rm{\Delta }}\mathrm{log}L=0.1﹩ corresponds to 0.14 Myr.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: Locations of the death lines for our four sets of stars. The death line is defined by the luminosity and pulsation periods of stars where ﹩\dot{M}={\dot{M}}_{{\rm{crit.}}}\equiv M/{t}_{{\rm{ev}}}﹩. The timescale for luminosity evolution, t ev, is set to 0.6 Myr for the M0 stars, 0.5 Myr for the M1 stars, 2.5 Myr for the C0 stars, and 3.2 Myr for the C1 stars. Points are marked every ﹩{\rm{\Delta }}\mathrm{log}L=0.015﹩, with “+” marking fundamental-mode points and “×” marking overtone mode points; given ﹩{\rm{\Delta }}\mathrm{log}L=2.3{\rm{\Delta }}t/{t}_{\mathrm{ev}}﹩, we can see why a majority of stars are expected to be found prior to the death zone. The evolution tracks terminate at a final mass determined using the initial–final mass relation in Zhao et al. (2012). The death lines span the tenth to ninetieth percentiles in mass for each data set.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: The quality of the fit of our mass-loss rate formula ﹩\mathrm{log}\dot{M}=\mathrm{log}{A}_{\dot{M}{LP}}+{B}_{\dot{M}{LP}}\mathrm{log}L+{C}_{\dot{M}{LP}}\mathrm{log}P﹩ from Equation (13) for our five combinations of spectral class and pulsation mode. On the x-axis is ﹩\mathrm{log}P﹩ and on the y-axis is ﹩\mathrm{log}\dot{M}-{B}_{\dot{M}{LP}}\mathrm{log}L﹩. The full details of each fit can be found in Table 4. Despite the least-squares fit working as intended, we will see in Section 6 that these fits are experiencing regression dilution.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: Stars in the PL strip in the Riebel et al. (2012) data set, split into five categories of spectral type and pulsation mode as indicated on the figures and as detailed in Section 5.2. The black solid line is the best-fit line of each data set, and the black dashed lines bound the stars in the best-fitting strip, with their width and height given in Table 5. Contours depict the fraction of stars relative to the peak density.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: The distribution of ﹩\mathrm{log}(\dot{M}/M)﹩ showing the death line (﹩\dot{M}/M=1/{t}_{\mathrm{ev}}﹩) and the death zone (﹩\dot{M}/M=1/{t}_{{\rm{ev}}}\pm 1﹩). The drop-off above the death line results from rapid evolution at high mass-loss rates. The drop-off below the death line is presumed to be due to the difficulty of measuring lower dust mass-loss rates and/or a lower dust-to-gas ratio at low mass-loss rates.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: To project this three-dimensional fit into two dimensions, (a) the luminosity dependence of the relations is removed and (b) the mass dependence is removed. Dots of the same color show the models that were used to determine the relations, color-matched to the fits.
Paper: Relation of Observable Stellar Parameters to Mass-loss Rate of AGB Stars in the LMC
Authors: Henry A. Prager, Lee Anne Willson, Massimo Marengo, Michelle J. Creech-Eakman
Figure caption: Contour plots depicting the location of the LM strip for the combinations of pulsation mode and C/O composition. The number of stars in each set can be seen in Table 1. The distribution of stars in (c) and (d) is consistent with our assertion that the death zone is well populated for all four categories of stars.
Paper: STITCH: A Subgrid-scale Model for Energy Buildup in the Solar Corona
Authors: J. T. Dahlin, C. R. DeVore, S. K. Antiochos
Figure caption: Comparison of helicity injection by FHC vs. STITCH for the idealized embedded-bipole configuration. (a, d) Horizontal components of magnetic field, B  s , generated by vortical flows imposed on the boundary to drive the FHC case. Horizontal components of magnetic field, B  s , injected just above the boundary to drive STITCH case with uniform ζ (panels (b) and (e)) and localized ζ (panels (c) and (f)). Black contours indicate radial magnetic field values B  r  = [−50, −40, −30, −20, −10, 0] G.
Paper: STITCH: A Subgrid-scale Model for Energy Buildup in the Solar Corona
Authors: J. T. Dahlin, C. R. DeVore, S. K. Antiochos
Figure caption: Global diagnostics for STITCH (black curves and time-axis labels) with uniform ζ and FHC (red curves and time-axis labels): (a) E  M /E  M0; (b) ﹩d\left({E}_{M}/{E}_{M0}\right)/{dt};﹩ (c) E  k /E  M0; and (d) ﹩| {B}_{\phi }{| }_{\max }﹩.
Paper: STITCH: A Subgrid-scale Model for Energy Buildup in the Solar Corona
Authors: J. T. Dahlin, C. R. DeVore, S. K. Antiochos
Figure caption: Initial embedded-bipole configuration. Surface gray shading indicates the sign and strength of the radial magnetic field component, B  r ; its PILs are colored cyan. Magnetic field lines are colored according to whether they close entirely within the active region (red), between the active region and the background magnetic field (blue and magenta), or entirely within the background field (green), or are separatrix lines bounding these flux systems (gray/silver).
Paper: STITCH: A Subgrid-scale Model for Energy Buildup in the Solar Corona
Authors: J. T. Dahlin, C. R. DeVore, S. K. Antiochos
Figure caption: Global diagnostics for uniform ζ (black curves) and localized ζ (green curves): (a) E  M /E  M0; (b) ﹩d\left({E}_{M}/{E}_{M0}\right)/{dt};﹩ (c) E  k /E  M0; and (d) ﹩| {B}_{\phi }{| }_{\max }﹩. Dashed black curves in panels (a)–(c) show halved quantities for uniform ζ.