adsabs/AstroCLIMB huggingface dataset:
Paper: Mapping the SO2 Shoreline in Gas Giant Exoplanets
Authors: Ian J. M. Crossfield, Eva-Maria Ahrer, Jonathan Brande, Laura Kreidberg, Joshua Lothringer, Caroline Piaulet-Ghorayeb, Jesse Polman, Luis Welbanks, James Kirk, Diana Powell, Niloofar Khorshid
Figure caption: Same as Figure 4 but here showing the impact of varying the vertical diffusion coefficient, Kzz. Varying Kzz over 4 orders of magnitude hardly budges the SO2 shoreline for temperatures ≳600 K; at lower temperatures, sufficiently high Kzz may result in measurable SO2 for sufficiently high metallicities. Representative spectra show that increasing Kzz over this range results in a slightly more detectable SO2 signature at 7.4 μm. Note also the prominent ethylene (C2H4) feature at 10.5 μm.
Paper: Mapping the SO2 Shoreline in Gas Giant Exoplanets
Authors: Ian J. M. Crossfield, Eva-Maria Ahrer, Jonathan Brande, Laura Kreidberg, Joshua Lothringer, Caroline Piaulet-Ghorayeb, Jesse Polman, Luis Welbanks, James Kirk, Diana Powell, Niloofar Khorshid
Figure caption: Left: same as Figure 1 but here showing the impact of varying the incident XUV irradiation. Significantly higher or lower levels of XUV flux can somewhat shift the SO2 shoreline for temperatures ≲1400 K. Right: representative spectra showing that dramatically increasing the level of incident XUV results in lower levels of SO2 (as well as CH4).
Paper: Mapping the SO2 Shoreline in Gas Giant Exoplanets
Authors: Ian J. M. Crossfield, Eva-Maria Ahrer, Jonathan Brande, Laura Kreidberg, Joshua Lothringer, Caroline Piaulet-Ghorayeb, Jesse Polman, Luis Welbanks, James Kirk, Diana Powell, Niloofar Khorshid
Figure caption: Left: same as Figure 1 but here showing the impact of varying the atmospheric C/O ratio. Increasing or decreasing the C/O ratio can significantly shift the SO2 shoreline up or down, respectively. Right: representative spectra showing how decreasing the C/O ratio can significantly increase the atmospheric SO2 signature, especially at 7.4 μm.
Paper: Mapping the SO2 Shoreline in Gas Giant Exoplanets
Authors: Ian J. M. Crossfield, Eva-Maria Ahrer, Jonathan Brande, Laura Kreidberg, Joshua Lothringer, Caroline Piaulet-Ghorayeb, Jesse Polman, Luis Welbanks, James Kirk, Diana Powell, Niloofar Khorshid
Figure caption: Each chart shows the fractional contribution of the indicated species to the overall atmospheric sulfur budget at the noted temperature and metallicity. The calculation indicates an average from 10−5 to 10−2 bar.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The fitted spectra for the 12 regions. To highlight the turnover feature on the spectra, the brightness temperature is converted into the specific intensity via the Rayleigh–Jeans law. In each panel, the black line is the mean intensity of the region from ULSA sky model, and the red line is the spectrum evaluated with the median values of the fitted parameters. The orange and blue shaded regions indicate the 1σ and 2σ uncertainties, respectively.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The covariance ﹩{C}_{ii^{\prime} }^{d}﹩ expressed by Equation (20) for ﹩{\nu }_{f}={\nu }_{f^{\prime} }=1﹩ MHz.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The averaging errors described by Equation (11) compared with thermal noise.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: In one precession period, the antenna wire repeatedly points into same pixels on sky. This figure shows the number of pointing times for each sky pixel in lunar equatorial coordinate system. The sky is pixelized by the HEALPix scheme with Nside = 8.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: Our mock data sample for 416 pixels on the sky in the lunar equatorial coordinate system. Note that this is not the sky temperature distribution but the mean antenna temperature when antenna wire is pointing into the corresponding pixel.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The position of 12 regions in the Galactic coordinate system. The sequence of each region is marked in the figure.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The turnover frequency νt,j for the 12 regions on sky in the Galactic coordinate system.
Paper: Reconstructing the Anisotropic Ultralong Wavelength Spectra Using a Single Antenna on Lunar Orbit
Authors: Qige Ao, 琦格 奥, Furen Deng, 辅仁 邓, Yidong Xu, 怡冬 徐, Bin Yue, 斌 岳, Huanyuan Shan, 欢源 陕, Xuelei Chen, 学雷 陈
Figure caption: The sky map with a frequency of 5 MHz in the Galactic coordinate system.
Paper: Discovery of Changing-look Behavior in AGN NGC 3822: A Long-term Multiwavelength Study
Authors: Narendranath Layek, Prantik Nandi, Sachindra Naik, Birendra Chhotaray, Arghajit Jana, Priyadarshee P. Dash, Neeraj Kumari, C. S. Stalin, Srikanth Bandari, S. Muneer
Figure caption: Evolution of the line profiles in Hβ and Hα regions. All the spectra have been corrected by subtracting the host galaxy contribution. Observation dates are annotated on each spectrum for reference. For clarity, the spectra are plotted with vertical offsets.
Paper: Discovery of Changing-look Behavior in AGN NGC 3822: A Long-term Multiwavelength Study
Authors: Narendranath Layek, Prantik Nandi, Sachindra Naik, Birendra Chhotaray, Arghajit Jana, Priyadarshee P. Dash, Neeraj Kumari, C. S. Stalin, Srikanth Bandari, S. Muneer
Figure caption: Temporal evolution of the Eddington ratio (﹩{\rm{log}}{\lambda }_{{\rm{Edd}}}﹩; black) and the line-of-sight hydrogen column density (NH) (blue) for different observational epochs. The blue text along the axis (e.g., Sy 2, Sy 1.9, Sy 1) indicates the spectral classification of the source at specific epochs. The breaks in the X-axis represent observational gaps.
Paper: Discovery of Changing-look Behavior in AGN NGC 3822: A Long-term Multiwavelength Study
Authors: Narendranath Layek, Prantik Nandi, Sachindra Naik, Birendra Chhotaray, Arghajit Jana, Priyadarshee P. Dash, Neeraj Kumari, C. S. Stalin, Srikanth Bandari, S. Muneer
Figure caption: The UV flux variation observed during the outburst phase in 2022 to the post-outburst phase from 2023 to 2024. The fitted black dashed line indicates the decay profile.
Paper: Discovery of Changing-look Behavior in AGN NGC 3822: A Long-term Multiwavelength Study
Authors: Narendranath Layek, Prantik Nandi, Sachindra Naik, Birendra Chhotaray, Arghajit Jana, Priyadarshee P. Dash, Neeraj Kumari, C. S. Stalin, Srikanth Bandari, S. Muneer
Figure caption: The Hβ and [O III]λλ4959, 5007 (left panels) as well as Hα+[N II] regions (right panels) are shown for all the epochs of HCT (top six panels) and X-Shooter (bottom three panels) observations of NGC 3822. For both the spectral ranges, the green lines represent the observed spectra, the blue line is the fitted model, and the black line represents each narrow emission line. A clear Balmer broad component (in red) appears in the 2022 spectra.
Paper: Discovery of Changing-look Behavior in AGN NGC 3822: A Long-term Multiwavelength Study
Authors: Narendranath Layek, Prantik Nandi, Sachindra Naik, Birendra Chhotaray, Arghajit Jana, Priyadarshee P. Dash, Neeraj Kumari, C. S. Stalin, Srikanth Bandari, S. Muneer
Figure caption: Correlation between different spectral parameters.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Time evolution of the azimuthally averaged disk structure of our simulations for different planetary masses (each column corresponds to a particular simulation; see titles). Row (a) shows the evolution of the gas density. Row (b) depicts the evolution of the dust-to-gas ratio. Hatched areas mark regions in which the J. Lim et al. (2024) criterion for strong clumping via the streaming instability is fulfilled. Maximum particle sizes and size distribution power-law exponents are shown in rows (c) and (d), respectively. The thick dashed line marks the location of the planet. Thin dashed lines show the boundaries of the wave-damping zones.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Snapshot of our fiducial simulation with Mp = 0.4MJup, vfrag = 400 cm s−1 and α = 10−3 after 45,000 planetary orbits. We employ damping zones in the boundary layers, marked by the thin dashed lines.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Azimuthally averaged disk profiles for the four simulations with different planetary masses after 45,000 orbits (∼533,602 yr) of evolution. Panel (a) shows the radial gas density profiles, and panel (b) depicts the dust density profiles. Maximum particle sizes of the power-law size distributions are shown in panel (c). Thin dashed lines mark the analytically estimated fragmentation limit. The local power-law exponents of the dust size distributions are shown in panel (d).
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Comparison between the azimuthally averaged dust density and particle size profiles of our 2D fiducial simulation (solid lines) and two 1D models. Dashed lines show the 1D DustPy model in which the gas column density was interpolated from the 2D simulation results. Dotted lines correspond to a 1D DustPy model for which the gas density was evolved based on a viscosity profile with an inverse P. C. Duffell (2020) gap.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Azimuthally averaged disk profiles for the four simulations with different fragmentation velocities after 45,000 orbits (∼533,602 yr) of evolution. Panel (a) shows the radial gas density profiles, and panel (b) depicts the dust density profiles. Maximum particle sizes of the power-law size distributions are shown in panel (c). Thin dashed lines mark the analytically estimated fragmentation limit. The local power-law exponents of the dust size distributions are shown in panel (d).
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Azimuthally averaged dust size distribution of our fiducial simulation after 45,000 planetary orbits. The maximum particle size is shown in white. The planet is located at 5.2 au. We employ damping zones in the boundary layers, marked by the thin dashed lines.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Streamlines of dust in the vicinity of the planet in our fiducial simulation. Azimuthal velocities are calculated in the planet’s frame of reference. The color of the streamlines indicates the radial component of the flux. The white circles indicate the planet’s Hill radius (outer circle) and the gravitational smoothing length (inner circle). Most of the mass flow between the inner and outer disk occurs via the flows past the planet.
Paper: Fragmentation-limited Dust Filtration in 2D Simulations of Planet–Disk Systems with Dust Coagulation: Parameter Study and Implications for the Inner Disk’s Dust Mass Budget and Composition
Authors: Thomas Pfeil, Philip J. Armitage, Yan-Fei Jiang, 燕飞 姜
Figure caption: Initial distribution of the passive scalar for both dust fluids. This quantity represents the initial distribution of outer-disk material in our simulations.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: The same as Figure 1, having removed the WSA-GAMERA SS simulations, and with the addition of (a) WSA predictions with the outer coronal boundary at 21.5 Rs (red dots, left panel) and (b) WSA-GAMERA TD solar wind predictions with boundary velocities extracted based on the optimized WSA velocity formula at 5 Rs (green time series, right panel). The gray boxes indicate the arrival of the plasma parcels from the coronal region noted by the black cross in Figure 2. The solar wind structure within this box is a good example of how the height at which we extract the WSA boundary conditions can affect the predictions in the inner heliosphere for both the WSA and WSA-GAMERA models.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: Comparison of in situ solar wind observations (black) with TD and SS predictions from WSA-GAMERA between 2021 January and June. The blue color indicates the results of the default TD run, and the magenta color shows the results of the TD run with the optimized WSA velocity formula, while, for the light-green time series, we have employed the WSA boundary conditions at 5 Rs on top of the optimized WSA velocity formula. The updating frequency cadence of the magnetograms for the TD simulations is daily. The SS simulations are shown in red.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: WSA solar wind velocity boundary maps at 5 Rs (upper panel) and 21.5 Rs (lower panel). The slowest velocities are shown in the dark-blue color while the fastest velocities are in dark red. The black cross indicates STEREO-A’s projection at the outer coronal boundary of 5 Rs (i.e., the subsatellite point), the date that the solar wind left that region (2021 August 31). Both the black cross and the straight black line have been drawn to facilitate the reader perceiving the HCS flattening from 5 to 21.5 Rs.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: Upper panels: magnetic connectivity between the projection of STEREO-A’s location at the outer coronal boundary of 21.5 Rs (i.e., subsatellite points) and the solar wind source region at 1 Rs, for five consecutive days (2025 September 25–29). Lower panels: the same as the upper panels but showing the input photospheric magnetic field based on which the connectivity maps were created. The emergence of active regions from the east limb of the Sun from one day to another is circled in yellow.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: Comparison of in situ solar wind observations (black) with WSA and WSA-GAMERA predictions at Earth. Predictions from WSA, having employed the outer coronal boundary at 5 and 21.5 Rs, are shown in orange and red dots, respectively. SS simulations from WSA-GAMERA according to the optimized WSA velocity formula of Equation (2) and WSA boundary maps created at 5 Rs are shown in magenta. Blue and light-green time series are the same as in the right panel of Figure 3 but for Earth’s location.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: Histograms of velocity differences (upper row) and time differences (lower row) between the aligned points in the observed and modeled time series at Earth, as matched by DTW. The first column indicates the results of the default WSA-GAMERA TD run. The second column indicates the results from the WSA-GAMERA TD simulations that have been performed based on the optimized WSA velocity formula. The third column indicates the results from the WSA-GAMERA TD simulations that have been performed based on both the optimized WSA velocity formula and the WSA boundary conditions created at 5 Rs.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: The same as Figure 5 but for PSP.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: The same as Figure 7 but for STEREO-A.
Paper: Why Do Solar Wind Models Get It Wrong? Understanding the Capabilities of Time-dependent Solar Wind Simulations
Authors: Evangelia Samara, Elena Provornikova, C. Nick Arge, Andrew J. McCubbin, Viacheslav G. Merkin
Figure caption: The same as Figure 9 but for the time interval 2021 July–December.
Paper: The AGORA High-resolution Galaxy Simulations Comparison Project. VIII. Disk Formation and Evolution of Simulated Milky Way Mass Galaxy Progenitors at 1 < z < 5
Authors: Minyong Jung, Ji-hoon Kim, Thịnh Hũ'u Nguyễn, Ramón Rodríguez-Cardoso, Santi Roca-Fàbrega, Joel R. Primack, Kirk S. S. Barrow, Anna Genina, Pablo Granizo, Hyeonyong Kim, Kentaro Nagamine, Yuri Oku, Johnny W. Powell, Yves Revaz, Héctor Velázquez, Alessandro Lupi, Ikkoh Shimizu, Tom Abel, Oscar Agertz, Renyue Cen, Daniel Ceverino, Avishai Dekel, Chaerin Jeong, Lucio Mayer, Boon Kiat Oh, Thomas R. Quinn, Hyunmi Song
Figure caption: Radial stellar age profiles at z = 2.8 (top panels) and z = 1 (bottom panels) for the three kinematic stellar components—thin disk, thick disk, and spheroid—demonstrating the stellar age gradients. A vertical dotted line in each panel represent r1/2. A gray-shaded region represents the mergers at z ∼ 4.5 (top panels) and z ∼ 2.2 (bottom panels). The mean stellar age difference between the stars outside and inside r1/2 is shown as Δt in each panel. The names of codes with strong signatures of compaction are labeled in orange, while those with weaker signatures are in blue. Only radial bins containing more than 30 stellar particles are displayed. Thin disk stars show negative stellar age gradients after compaction in codes with compact stellar sizes (ART-I, GADGET-3, AREPO-T, and GIZMO). Note that six out of eight codes (seen in the upper panels) reach redshift z = 1 (bottom panels), while RAMSES and GIZMO end their simulations at z = 2 (see Sections 2.1 and 2.3). For more information, see Section 3.3.
Paper: The AGORA High-resolution Galaxy Simulations Comparison Project. VIII. Disk Formation and Evolution of Simulated Milky Way Mass Galaxy Progenitors at 1 < z < 5
Authors: Minyong Jung, Ji-hoon Kim, Thịnh Hũ'u Nguyễn, Ramón Rodríguez-Cardoso, Santi Roca-Fàbrega, Joel R. Primack, Kirk S. S. Barrow, Anna Genina, Pablo Granizo, Hyeonyong Kim, Kentaro Nagamine, Yuri Oku, Johnny W. Powell, Yves Revaz, Héctor Velázquez, Alessandro Lupi, Ikkoh Shimizu, Tom Abel, Oscar Agertz, Renyue Cen, Daniel Ceverino, Avishai Dekel, Chaerin Jeong, Lucio Mayer, Boon Kiat Oh, Thomas R. Quinn, Hyunmi Song
Figure caption: The upper panels show the mean radial positions of stellar particles from the galaxy center at z = 2.8 (solid lines) and at their time of formation (dashed lines). The three kinematic components—thin disk, thick disk, and spheroid—are displayed separately with the same color and shape convention as in Figure 7. A horizontal dashed line in each panel represents r1/2. The marginal plots at the bottom show the residuals between the stellar radii at z = 2.8 and their formation radii. Only stars formed in situ are considered. The lower panels present the same analysis at z = 1. A gray-shaded region represents the mergers at z ∼ 4.5 (top panels) and z ∼ 2.2 (bottom panels). The names of codes with strong signatures of compaction are labeled in orange, while those with weaker signatures are in blue. A positive residual (marginal plots) indicates outward movement of stars after their formation. Stars in the thin disk grow in an inside-out manner following compaction. Stars formed at earlier epochs tend to migrate outward in codes with larger stellar sizes (ENZO, RAMSES, and GEAR). Note that six out of eight codes (seen in the upper panels) reach redshift z = 1 (bottom panels), while RAMSES and GIZMO end their simulations at z = 2 (see Sections 2.1 and 2.3). For more information, see Section 3.4.
Paper: The AGORA High-resolution Galaxy Simulations Comparison Project. VIII. Disk Formation and Evolution of Simulated Milky Way Mass Galaxy Progenitors at 1 < z < 5
Authors: Minyong Jung, Ji-hoon Kim, Thịnh Hũ'u Nguyễn, Ramón Rodríguez-Cardoso, Santi Roca-Fàbrega, Joel R. Primack, Kirk S. S. Barrow, Anna Genina, Pablo Granizo, Hyeonyong Kim, Kentaro Nagamine, Yuri Oku, Johnny W. Powell, Yves Revaz, Héctor Velázquez, Alessandro Lupi, Ikkoh Shimizu, Tom Abel, Oscar Agertz, Renyue Cen, Daniel Ceverino, Avishai Dekel, Chaerin Jeong, Lucio Mayer, Boon Kiat Oh, Thomas R. Quinn, Hyunmi Song
Figure caption: Left: 3D axis ratios (b/a, c/a) of the target galaxy at z = 2.8 with dust extinction effects (filled markers). We also show the ratios based on stellar distribution without extinction (small, open markers). A gray-shaded region indicates the observational results in the same redshift bin at ﹩{\mathrm{log}}({M}_{\star }/{M}_{\odot })=10.0﹩–10.5 (V. Pandya et al. 2024). Middle: size–mass relation at z = 2.8 using the effective radius with dust extinction effects, Re, and the 3D stellar half-mass radius, r1/2 (filled and open markers, respectively). Solid and dotted lines represent the mean 3D size–mass relation at 2.5 < z < 3 (V. Pandya et al. 2024). Right: size–mass relation at z = 1, with the observational data in F. Buitrago & I. Trujillo (2024). The yellow star in the middle and right panels represents the median M⋆ and Re of MW-like progenitors at each redshift (V. Y. Y. Tan et al. 2024). For more information, see Section 3.5.