adsabs/AstroCLIMB huggingface dataset:
Paper: Early Coformation of the Milky Way’s Thin and Thick Disks at Redshift z > 2
Authors: Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
Figure caption: Distribution of StarHorse ages for APOGEE (top left) and LAMOST (top right panel) split into thin (green) and thick (red) disks. The bottom panels show the percentage of thin (green dashed) and thick (red solid line) disk stars in each age bin, considering age bins of 1 Gyr. The sum of the thin and thick disks percentages totals 100%. The vertical band indicates the period of GSE merger (9–11 Gyr).
Paper: Early Coformation of the Milky Way’s Thin and Thick Disks at Redshift z > 2
Authors: Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
Figure caption: The columns were organized in the following sequence: [Fe/H] (first), [Mg/Fe] (second), and angular momentum in z-direction, Lz (third), as a function of StarHorse age. The top and bottom rows show the APOGEE and LAMOST data, respectively. The thick disk is represented by red dots, and the thin disk data by green dots. The black line represents the weighted average between the thick and thin disk samples, while the green and red lines represent the average for the thin and thick disk components, respectively. The vertical band indicates the period of GSE merger (11–9 Gyr).
Paper: Early Coformation of the Milky Way’s Thin and Thick Disks at Redshift z > 2
Authors: Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
Figure caption: Velocity dispersion in z-direction (﹩{\sigma }_{{V}_{z}}﹩; upper panels), radial direction (﹩{\sigma }_{{V}_{r}}﹩; middle panels), and azimuthal direction (﹩{\sigma }_{{V}_{\phi }}﹩; bottom panels) as a function of StarHorse age for our thin (green line) and thick (red line) disk samples. The left and right columns show the APOGEE and LAMOST samples, respectively. The vertical band indicates the period of GSE merger (11–9 Gyr ago).
Paper: A Semianalytic Framework of Population III and Subsequent Galaxy Formation on Cosmological N-body Simulations
Authors: Tomoaki Ishiyama, Shingo Hirano
Figure caption: Comparison of model results calculated on simulations with different mass resolutions (H3, M3, and L3). Left and right panels show results of models with vbc = 0 and 1σvbc, respectively. Top: number density of Pop III stars formed at all redshifts as a function of the Pop III mass in a halo (same as Figure 3). Middle: star formation rate density (same as Figure 5). Bottom: formation rate density of Pop III-forming halos (same as Figure 5).
Paper: Early Coformation of the Milky Way’s Thin and Thick Disks at Redshift z > 2
Authors: Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
Figure caption: Vϕ/σtot as a function of age is represented by the black line, where ﹩{\sigma }_{\,\rm{tot}\,}=\sqrt{{\sigma }_{{V}_{r}}^{2}+{\sigma }_{{V}_{z}}^{2}+{\sigma }_{{V}_{\phi }}^{2}}﹩. We consider age bins of 1 Gyr. APOGEE and LAMOST samples are represented in the upper and lower panels, respectively. The dashed lines represent the individual samples from the thin (green) and thick (red) disks.
Paper: Early Coformation of the Milky Way’s Thin and Thick Disks at Redshift z > 2
Authors: Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg, João A. S. Amarante, Anna B. A. Queiroz, Cristina Chiappini, Friedrich Anders, Rafael M. Santucci, Fabrícia O. Barbosa, João V. Nogueira-Santos
Figure caption: Estimated stellar ages for thin (leftmost) and thick disk (rightmost panel) stars older than 8 Gyr, considering the age prior on the vertical axis and considering only the spatial distribution of stars with no age prior on the horizontal axis. The lower panels show the difference between the two calculated ages (ΔAGE).
Paper: Luminous Late-time Radio Emission from Supernovae Interacting with Circumbinary Material
Authors: Samantha C. Wu, Daichi Tsuna
Figure caption: Left: mass-loss history of the stripped star models listed in Table 1, shown as the mass-transfer rate of the donor star as a function of time until core collapse. Right: density profiles of the CSM for the stripped star models listed in Table 1, shown for parameters vCSM = 0.3 vorb,c, and fΩ = 1. The dotted line shows a wind profile CSM for a mass-loss rate 10−5 M⊙ yr−1 and velocity of 103 km s−1, typically observed in Wolf–Rayet stars. The gray shaded region is taken from Figure 7 of K. Maeda & T. J. Moriya (2022) and represents the range of CSM density distributions derived for SNe Ibn in that work.
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The [N II]-BPT diagram with our suite of Cloudy models for BPASS stellar populations (gold circles) and black hole accretion disks (blue crosses) in slices of ionization parameter (increasing up) and gas-phase metallicity (increasing to the right). The lines show the demarcations for star formation and AGN (below and above the lines, respectively) for the [N II]-BPT diagnostics from G. Kauffmann et al. (2003, dashed) and L. J. Kewley et al. (2001, solid). (Animated versions of our models on the [N ii]-BPT, VO87, and OHNO diagrams are available. See Figures 11–16 in the Appendix in the online journal version or GitHub: https://github.com/njcleri/AR_05558_modeling/tree/main/figures/animations.)
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The inferred properties from our models for an observation in the [N II]-BPT plane, using the broad-line AGN GS_3073 from H. Übler et al. (2023) as an example. The top-left panel shows the H. Übler et al. (2023) object with 1σ uncertainties in the [N II]-BPT plane. The top-center, top-right, and bottom-left panels show the inferred ionization parameter, gas-phase metallicity, and ratios of the ionizing photon budgets in each ionization zone, respectively. The bottom-right panel shows the ionizing continua of the 10 most likely models at that point in the [N II]-BPT plane, which include both accreting black holes (blue) and stellar populations (gold). We show the ionizing continuum of the model with the highest likelihood in bold.
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The OHNO diagram with our suite of Cloudy models for BPASS stellar populations (gold circles) and black hole accretion disks (blue crosses) in slices of ionization parameter (increasing up) and gas-phase metallicity (increasing to the right). The lines show the demarcations for star formation and AGN (below and above the lines, respectively) for the OHNO diagnostic from B. E. Backhaus et al. (2022).
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The VO87 diagram with our suite of Cloudy models for BPASS stellar populations (gold circles) and black hole accretion disks (blue crosses) in slices of ionization parameter (increasing up) and gas-phase metallicity (increasing to the right). The lines show the demarcations for star formation and AGN (below and above the lines, respectively) for the VO87 diagnostic from J. R. Trump et al. (2015).
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The OHNO diagram shown with our suite of photoionization models colored by ionization parameter (top left) and gas-phase metallicity (bottom left). The right panels show the posteriors P(θ∣data) for the ionization parameter (top right) and gas-phase metallicity (bottom right) for a mock observation at the respective point in the OHNO plane, where the color shows the mean of the posterior.
Paper: Optical Strong Line Ratios Cannot Distinguish between Stellar Populations and Accreting Black Holes at High Ionization Parameters and Low Metallicities
Authors: Nikko J. Cleri, Grace M. Olivier, Bren E. Backhaus, Joel Leja, Casey Papovich, Jonathan R. Trump, Pablo Arrabal Haro, Véronique Buat, Denis Burgarella, Emilie Burnham, Antonello Calabrò, Jonathan H. Cohn, Justin W. Cole, Kelcey Davis, Mark Dickinson, Steven L. Finkelstein, Ray Garner, Michaela Hirschmann, Weida Hu, Taylor A. Hutchison, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Zach J. Lewis, Michael V. Maseda, Lise-Marie Seillé, Raymond C. Simons
Figure caption: The VO87 diagram shown with our suite of photoionization models colored by ionization parameter (top left) and gas-phase metallicity (bottom left). The right panels show the posteriors P(θ∣data) for the ionization parameter (top right) and gas-phase metallicity (bottom right) for a mock observation at the respective point in the VO87 plane, where the color shows the mean of the posterior.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Comparisons of reported ΩHI constraints from W22 (vertical triangles, horizontally offset for illustrative purposes), CHIME23 (square), P23 (horizontal triangles), and MeerKAT-WiggleZ constraints (C22; and C24, stars offset to left and right, respectively) to the ΩHI values revised according to the predicted model errors at the nearest z from IllustrisTNG (crosses). The significant differences between the crosses and points demonstrate the substantial analytical error incurred by the models used to interpret these measurements. We emphasize that these represent the minimum possible error inherited from the model, including some instrumental effects for W22, C22, and C24. There are caveats that impact the interpretation of results from CHIME23 and P23 (see text and Table 4). Constraints on ΩHI from other H I measurement techniques and ΩHI values from IllustrisTNG are shown as black points and a yellow dashed line, respectively. ΩHI is extracted from the measured ﹩{{ \mathcal A }}_{{\rm{HI}}}﹩ (Equation (20)) using bHI and rHI−Gal from IllustrisTNG, which can differ from those used in the respective works (Table 4). We display the originally reported ΩHI values as open circles for completeness, but note that these are not directly comparable since each adopts different bHI and rHI−Gal values.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Accuracy of constraints on ΩHI at z = 0.5 (top) and z = 1 (bottom) inferred from fitting various models to H I power spectra (from left to right, H I × Blue, H I × Red, H I × Galaxy, and H I auto). In practice, models are fit to H I power spectra over a range of scales, and the resulting ΩHI value is expressed as a measurement at the average k in that range, keff. We vary the maximum k of the scales included in the fit to reflect that procedure, expressing the inferred ΩHI as a function of keff. These values are shown as a ratio over ΩHI calculated directly from IllustrisTNG, such that values near unity indicate better model performance. Darker colors correspond to more complex models, with the names corresponding to the shorthands and definitions provided in Figures 7–8.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: The 2D autopower spectra of all matter (top center), H I (top right), and blue (bottom left), red (bottom center), and all-galaxies (bottom right) at z = 1. Black dotted lines are concentric circles to help visualize the FoG effect, which vertically warps the isopower contours. This warping is strongest in red galaxies as compared to the other galaxy types, agreeing with observations (D. S. Madgwick et al. 2003). We find that H I appears to have stronger FoG suppression than matter, as found in VN18, although their relative strengths depend on scale (see text). Power spectra are slightly smoothed by a Gaussian filter (σ = 0.5) to see the contour lines more clearly, but the smoothed values are not used for the determination of the pairwise velocity dispersion and do not otherwise impact our conclusions.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Top: bias with respect to matter for various baryonic tracers. Blue (blue), red (red), and all-galaxies (gray) are shown with H I (brown) at z = 0 (left), z = 0.5 (center), and z = 1 (right). Large-scale bias values provided in Table 1. With respect to (w.r.t.). Bottom: the percentage error caused by assuming a constant bias with gray dotted lines at ±10%. The bias for all baryonic tracers at most redshifts differ from the assumed constant value by ≥10% by k ∼ 0.2–0.3 h cMpc−1. The exceptions are the z = 0 blue-galaxy bias and z = 1 H I bias, which arise from their low occupation of massive halos offsetting nonlinearities.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Accuracy of models of H I × Blue (left), H I × Red (center), and H I × Galaxy (right) at z = 1, compared to IllustrisTNG. Darker colors correspond to more complex models (see legend). Green areas show the typical scales of observations. The simplest prescription represents the model used to interpret recent observations, which errs by ∼25% even to the largest scales probed by TNG300. Better performing models stay near unity at smaller scales. As expected, more complex models capture more of the behavior of their respective cross-power spectra. However, even the full prescription for H I × Red errs by >50% at k ∼ 1 h cMpc−1.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Models of the z = 0.5 H I autopower spectrum, shown as a ratio over the computed values from TNG300. Colors range from lightest to darkest in order of simplest to most complex (see table above). Better performing models remain close to unity at smaller scales. For reference, the green area corresponds to the scale of the MeerKAT observations from P23. As expected, the more terms we add, the better the model performs, with the FoG term having the strongest impact.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Errors arising from how RSDs are modeled for each distribution in Figure 5, expressed as a ratio of the model power spectra (Pmodel) over the corresponding redshift-space power spectra from IllustrisTNG. Pmodel is obtained by substituting the fiducial σp and b values for each distribution into Equation (15), which is then integrated according to Equation (16). The gray-shaded region denotes scales that were not included in the σp fit or χ2 analysis (Table 3). The errors demonstrate that the models systematically overestimate the actual power spectrum until k ∼ 1 h cMpc−1.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: The redshift evolution of the bias for each baryonic tracer, with linear fits (dashed lines) to the data points to help visualize the trends. The precise values presented in these fits should be taken with caution with only three data points. Dotted lines show the expected bias evolution of a population only subject to gravitational effects (J. N. Fry 1996). Tracers associated with star formation (red, blue, H I) deviate from the passive evolution more than the all-galaxy bias, suggesting that quenching processes play a significant role in how they trace the matter distribution.
Paper: Lost in the FoG: Pitfalls of Models for Large-scale Hydrogen Distributions
Authors: Calvin K. Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro
Figure caption: Correlation coefficients of H I × Blue, H I × Red, and H I × Galaxy for z = 0 (left), z = 0.5 (center), and z = 1 (right). Large-scale values are presented in Table 2. H I correlates more strongly with blue galaxies than red, leading to a larger amplitude and less scale-independence in H I × Blue. H I × Galaxy lies between H I × Blue and H I × Red, representing an effective average of its two subpopulations. The slope of each correlation coefficient becomes steeper with time as more galaxies quench.
Paper: Preferential Accretion onto the Secondary Black Hole Strengthens Gravitational-wave Signals
Authors: Julia M. Comerford, Joseph Simon
Figure caption: Fraction of SMBH binaries as a function of q•,f, the final SMBH mass ratio when the binary enters the pulsar timing gravitational-wave band (∼milliparsec separations), for the population of 0.1 ≤ qgal ≤ 1 galaxy mergers. Solid lines show the median values over 1000 realizations, while the shaded regions show the 1σ errors. The no-accretion model (black line) is where the SMBH mass ratio is derived from the galaxy mass ratio and there is no further SMBH mass growth (q•,f = q•,i). The models of preferential accretion onto the secondary SMBH for observationally based estimates of 10% (blue line) and 20% (orange line) cumulative growth of the total SMBH mass M•,tot drive the fraction of SMBH binaries higher for higher SMBH mass ratios. The dotted vertical line at q•,f = 0.25 marks the dividing line between minor SMBH mergers (to the left) and major SMBH mergers (to the right). When compared to the no-accretion model, the model of 10% (20%) total SMBH mass growth increases the fraction of major SMBH mergers from 22% to 41% (57%).
Paper: Preferential Accretion onto the Secondary Black Hole Strengthens Gravitational-wave Signals
Authors: Julia M. Comerford, Joseph Simon
Figure caption: Probability distribution functions of the predicted GWB amplitude for models of no SMBH accretion (black), differential accretion with 10% total SMBH mass growth (blue), and differential accretion with 20% total SMBH mass growth (orange). The SMBH binary evolution timescale is set to 1.8 Gyr, the median timescale found for the differential accretion model we use here (P. C. Duffell et al. 2020; M. S. Siwek et al. 2020). The brown diamond data point shows the median measured value for the GWB amplitude found in NANOGrav’s 15 yr data set, with error bars showing the 90% credible region (G. Agazie et al. 2023a). The top panel (a) is only for major galaxy mergers (qgal ≥ 0.25), while the bottom panel (b) is for both major and minor galaxy mergers (qgal ≥ 0.1). While the no-accretion models are similar in the two panels, the other two models change significantly when minor galaxy mergers are included because preferential accretion can turn minor galaxy mergers into major SMBH mergers (see Figure 2). We note that the model with 10% total SMBH mass growth due to differential accretion with the inclusion of minor mergers has a median value that most closely matches the median value constrained from pulsar timing array data.
Paper: Preferential Accretion onto the Secondary Black Hole Strengthens Gravitational-wave Signals
Authors: Julia M. Comerford, Joseph Simon
Figure caption: Predicted values of the GWB amplitude for models with no SMBH accretion (black), as well as models of preferential accretion onto the secondary SMBH with total SMBH mass M•,tot growth of 10% (blue) and 20% (orange). Each line shows the median values over 1000 realizations, with the shaded regions denoting the 1σ errors. For clarity, we show only one shaded, 1σ error region per panel: zero (left; gray), 10% (middle; blue), and 20% (right; orange) total SMBH mass growth. The SMBH binary evolution timescale Tevol, which is the timescale for the SMBH binary to evolve from ∼kiloparsec separations to ∼milliparsec separations, is allowed to vary. The dotted vertical line illustrates Tevol = 1.8 Gyr, the median timescale found for the model of differential SMBH mass accretion that we use (P. C. Duffell et al. 2020; M. S. Siwek et al. 2020). The dashed brown horizontal line shows the median amplitude of the GWB found in NANOGrav’s 15 yr data set (the brown shaded area is the 90% credible region; G. Agazie et al. 2023a). We find that even small amounts (10%) of differential accretion onto the SMBH binary amplify the amplitude to values consistent with the current constraints from NANOGrav.