adsabs/AstroCLIMB huggingface dataset:
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Column density of CH4 as a function of radius for the thermochemical models. The orange dashed line corresponds to the upper limit of the CH4 column derived from slab models, assuming the same emitting temperature and area as derived for C2H2.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Comparison of slab models of C4H2 emission with varying temperatures. The column density is the same for the three models, corresponding to 1014 cm−2. The emitting area is adjusted to match the peak of the emission feature. The best-fit model corresponds to the 150 K line.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Deprojected and azimuthally averaged radial intensity profiles of the RADMC-3D model and the DSHARP observation (J. Huang et al. 2018).
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Best-fit slab model for OH, shown in yellow. For reference, we show a 6000 K OH model in green, to point out the asymmetry observed in the prompt emission lines in the top panel. The gray lines in the subplots show the residuals for each region. The shaded zones show the ranges used to calculate the χ 2. Most of the emission from the composite model correspond to water lines, assuming the same parameters as the one retrieved in the 12–16 μm range.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Best-fit slab model fits to the data for the molecules detected. The H2O model is subtracted before fitting CO2, and the CO2 model is subtracted before simultaneously fitting C2H2 and HCN. The shaded region in each plot shows the regions used to calculate the χ 2. The gray subplots at the bottom show the residuals after subtracting the slab models.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: H2O abundance maps for all of the thermochemical models. The magenta line corresponds to the contribution function for the H2O transition in Table 5. The titles correspond to those in Table 4.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Same as Figure 20, but for C2H2 abundances. The cyan line corresponds to the contribution function for the C2H2 transition in Table 5.
Paper: JWST/MIRI Detection of a Carbon-rich Chemistry in the Disk of a Solar Nebula Analog
Authors: María José Colmenares, Edwin A. Bergin, Colette Salyk, Klaus M. Pontoppidan, Nicole Arulanantham, Jenny Calahan, Andrea Banzatti, Sean Andrews, Geoffrey A. Blake, Fred Ciesla, Joel Green, Feng Long, 凤 龙, Michiel Lambrechts, Joan Najita, Ilaria Pascucci, Paola Pinilla, Sebastiaan Krijt, Leon Trapman
Figure caption: Same as Figure 20, but for CO2 abundances. The orange line corresponds to the contribution function for the CO2 transition in Table 5.
Paper: Formation of Sub-Chandrasekhar-mass Black Holes and Red Stragglers via Hawking Stars in Ultrafaint Dwarf Galaxies
Authors: Andrew D. Santarelli, Matthew E. Caplan, Earl P. Bellinger
Figure caption: Maximal (purple, dashed–dotted), intermediate (blue, solid), and minimal (red, dashed) radiative efficiencies as a function of black hole mass. Maximal and minimal schemes are both constant, maximal being that given by E. P. Bellinger et al. (2023) and minimal corresponding to the canonical Bondi accretion scheme.
Paper: High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Figure caption: Visualization of how different secondary halo properties improve machine learning models of galaxy occupation over a mass-only approach. For each combination of galaxy sample and secondary halo property, we show ΔR 2, the fractional reduction in the mean-squared error for the predicted galaxy occupation over a mass-only prediction. We separate by central galaxies (left panel) and satellite galaxies (right panel). The rightmost column in each panel denotes the inclusion of all available secondary halo properties.
Paper: Formation of Sub-Chandrasekhar-mass Black Holes and Red Stragglers via Hawking Stars in Ultrafaint Dwarf Galaxies
Authors: Andrew D. Santarelli, Matthew E. Caplan, Earl P. Bellinger
Figure caption: Lifetime of Hawking stars in our MESA models with each accretion scheme as a function of stellar mass for PBH seed masses of 10−12  M ⊙ (left), 10−11  M ⊙, (middle), and 10−10  M ⊙ (right) for 1% (red), 10% (black), and 100% (gray) solar metallicity. The dashed black line indicates a Hubble time 13.7 Gyr where appropriate and is excluded from plots with lifetimes well below it. Note that the lifetimes for the minimal regime are in Myr as opposed to Gyr for the other two.
Paper: Formation of Sub-Chandrasekhar-mass Black Holes and Red Stragglers via Hawking Stars in Ultrafaint Dwarf Galaxies
Authors: Andrew D. Santarelli, Matthew E. Caplan, Earl P. Bellinger
Figure caption: Hawking star lifetimes as a percentage of the typical main-sequence lifetime, all as a function of both the stellar and PBH seed masses. The intermediate radiative efficiency scheme is used for the left plot, and the minimal for the right. Solid black lines indicate actual age in log scale, and the dotted black line is fit to the curve of 50% main-sequence lifetime. The maximal scheme can be found in Figure 4 of E. P. Bellinger et al. (2023).
Paper: High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Figure caption: Definition of different galaxy samples in the plane of sSFR and stellar mass. The BGS sample is defined by a variable cut in stellar mass, as indicated by the orange line. LRG are indicated by the two red lines, where we isolated galaxies below a certain sSFR and above a stellar mass. The cut for ELGs is represented by the galaxies above the horizontal blue line. To make low-sSFR visible in this plot, the sSFR of every galaxy was increased by ﹩{10}^{{ \mathcal N }(-12,0.25)}\,{\mathrm{yr}}^{-1}﹩, where ﹩{ \mathcal N }(-12,0.25)﹩ denotes a random variable drawn from a Gaussian with mean −12.0 and scatter 0.25.
Paper: High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Figure caption: Large-scale structure in TNG300 at coordinates z < 10 h −1 Mpc. The background color denotes the matter density in the TNG300-3 Dark simulation. Blue points denote the BGS-2 sample in TNG300-1, whereas red points are corresponding galaxies assigned to dark matter halos in TNG300-3 Dark by learning the galaxy–halo connection from TNG300-1.
Paper: Formation of Sub-Chandrasekhar-mass Black Holes and Red Stragglers via Hawking Stars in Ultrafaint Dwarf Galaxies
Authors: Andrew D. Santarelli, Matthew E. Caplan, Earl P. Bellinger
Figure caption: The ZAMS sound speed (left), density (middle), and opacity (right) for several Hawking star models. Models included are permutations of 100%, 10%, and 1% solar metallicity and PBH seed masses of 10−12 M ⊙ (solid), 10−11 M ⊙ (dashed), and 10−10 M ⊙ (dotted).
Paper: High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Figure caption: Effective volume increase for three different densities of our BGS sample, (BGS-1, -2, and -3) in ascending order of density, as well as ELG and LRG, are shown as a function of distance for six clustering measures: real-space correlation function (ξ, top left), galaxy–galaxy lensing (ΔΣ, top right), projected correlation function (w p, center left), redshift-space monopole (ξ 0, center right), quadrupole (ξ 2, bottom left), and hexadecapole (ξ 4, bottom right). The dashed lines indicate our machine learning method when only using halo mass to learn galaxy–halo connection, while the solid lines indicate that all halo properties were used.
Paper: High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Alexandra Doytcheva, Filomela V. Gerou, Johannes U. Lange
Figure caption: The ratio of the real-space correlation function ξ for BGS-3 in the gravity-only plus machine learning simulation compared to the original full-physics simulation. The gray line indicates if the halo mass alone, M tot, is used to learn the galaxy–halo connection. Furthermore, we show results if, additionally, the maximum circular velocity V max (light green), main subhalo mass M main (dark green), environment δ (turquoise), halo formation redshift z 50 (blue), or all available halo properties (red) are used. Error bars denote 68% credible intervals and are derived from jackknife resampling.
Paper: Multizone Modeling of Black Hole Accretion and Feedback in 3D GRMHD: Bridging Vast Spatial and Temporal Scales
Authors: Hyerin Cho, 혜린 조, Ben S. Prather, Kung-Yi Su, Ramesh Narayan, Priyamvada Natarajan
Figure caption: Radial profiles corresponding to four simulations of magnetized Bondi accretion (see legend in panel (a)) with a relatively small Bondi radius, R  B  ≈ 400 r  g . Red solid lines correspond to a run that is initialized with a weak magnetic field (β ≈ 108, Section 3.1) and simulated on a single zone, as in usual GRMHD simulations. Dashed orange lines correspond to the exact same problem simulated using our multizone technique, where we divide the simulation domain into multiple annuli and switch between them. Black solid lines (one-zone) and cyan dashed lines (four-zone) correspond to another pair of simulations that are initialized with a strong magnetic field (β ≈ 1, Section 3.2). In both simulation pairs, the one-zone and four-zone runs give closely similar results. The analytical Bondi HD solution is shown as thick gray lines and the Bondi radius is shown as a gray dashed vertical line. The two weak-field simulations closely follow the Bondi HD solution since the magnetic field is too weak to affect the dynamics. The two strong-field simulations deviate significantly from the Bondi solution. For all curves, negative values are shown dotted. The different panels correspond to the following: (a) t-averaged accretion rate ﹩\dot{M}﹩ in units of the analytical Bondi accretion rate ﹩{\dot{M}}_{B}﹩. The two strong-field simulations show a reduced accretion rate of ﹩\sim {10}^{-1}{\dot{M}}_{B}﹩. (b) t-, θ-, ϕ-averaged density ρ. The strong-field simulations have a shallower slope (ρ roughly ∝r −1) compared to the Bondi solution (slope −3/2). (c) t-, θ-, ϕ-averaged temperature T. (d) t-, θ-, ϕ-averaged radial component of the four-velocity −u  r . The weak-field simulations agree perfectly with the Bondi solution, but the strong-field simulations deviate significantly. (e) Inverse of the t-, θ-, ϕ-averaged β −1. For the strong-field runs, β is constant around ∼1 across a few orders of magnitude in radius and decreases toward a smaller radius. The radial scaling of r 3/2 expected for a weak-field simulation is shown by the green line. (f) energy outflow efficiency η. The weak-field runs show a negative efficiency, η ∼ −1/R  B  ∼ −4 × 10−4, consistent with Equation (11). The efficiency switches sign to positive, indicating active outward energy feedback, in the strong-field runs. The feedback efficiency is η ∼ 1% (indicated by a thick pink line for reference).
Paper: Multizone Modeling of Black Hole Accretion and Feedback in 3D GRMHD: Bridging Vast Spatial and Temporal Scales
Authors: Hyerin Cho, 혜린 조, Ben S. Prather, Kung-Yi Su, Ramesh Narayan, Priyamvada Natarajan
Figure caption: Schematics of the multizone method: The different colors represent the different zones being simulated. Radii are shown along the y-axis, with zone i extending from an inner radius of 8 i  r  g  to an outer radius of 8 i+2 r  g , where r  g  = GM •/c 2 is the gravitational radius. Runtime is shown along the x-axis (not to scale). The plot here corresponds to one “V-cycle,” advancing the entire domain forward by some time. A complete simulation consists of hundreds of V-cycles to allow full information exchange between the smallest and largest scales.
Paper: Multizone Modeling of Black Hole Accretion and Feedback in 3D GRMHD: Bridging Vast Spatial and Temporal Scales
Authors: Hyerin Cho, 혜린 조, Ben S. Prather, Kung-Yi Su, Ramesh Narayan, Priyamvada Natarajan
Figure caption: Time-averaged radial profiles of (a) density ρ, and (b) temperature T, for a GRHD spherically symmetric accretion simulation where large radii are initialized from a realistic galaxy simulation using the code GIZMO. Radii are expressed in gravitational radius r  g  units (lower x-axis) and in physical units scaled to M87 R M87 (upper x-axis). The GRHD simulation includes the external gravity of the galaxy, and the Bondi radius is R  B  ≈ 2 × 105 r  g  (indicated by the gray vertical line). The initial data from GIZMO are shown for r ≥ 106 r  g  by the thick blue lines. The Bondi analytical solution is shown by the thick gray lines. Results from our eight-zone GRHD simulation, shown by the red lines, match the Bondi solution inside the Bondi radius and closely follow the GIZMO profiles at larger radii. A similar run which is initialized with a strong magnetic field gives very different results, as shown in Figure 5.
Paper: Multizone Modeling of Black Hole Accretion and Feedback in 3D GRMHD: Bridging Vast Spatial and Temporal Scales
Authors: Hyerin Cho, 혜린 조, Ben S. Prather, Kung-Yi Su, Ramesh Narayan, Priyamvada Natarajan
Figure caption: Top: accretion rate (black) and magnetic flux parameter (blue) at the horizon r  H  as a function of time for a multizone GRMHD simulation initialized with GIZMO galaxy simulation data. Time is in units of the characteristic time t char(r) spent at each zone. The mean accretion rate, ﹩\overline{\dot{M}}\sim {10}^{-2}{\dot{M}}_{B}﹩, and the mean magnetic flux at the horizon, ﹩\overline{{\phi }_{b}}\sim 30﹩, are plotted in gray and blue horizontal lines, respectively. We average values over the last 1/5 of the simulations (pink background) when the simulation has reached a steady state. Bottom: depiction of the accretion flow across multiple radial scales from a single late-time snapshot of the multizone simulation. In each panel, the right half shows the distribution of the density ρ (color scale on the right) and the left half shows the distribution of plasma-β (color scale on the left). The black lines show the magnetic field. Note that turbulent fluctuations extend over 8 orders of magnitude in radius.
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: Left-hand panel: Residual velocity map of JO206 (blue-to-red colormap). We also show the original Hα emission (gray colormap) and the stellar disk (gray contour, M. Gullieuszik et al. 2020). Right-hand panel: VSF of the residual velocity map (blue, the blue-filled area indicates the 1σ uncertainty region), the one derived from the not-corrected one (orange), and the one derived in the stellar disk only (green). The gray-shaded area marks l < 1″, below which the signal is affected by the spaxel correlation (see Section 2.2). The continuous and dashed lines indicate the slopes expected in the case of, respectively, rotation (∝l) and turbulence (∝l 1/3).
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: Phase-space density plots derived from the Hα velocity map of JO206. Top panel: Initial velocity distribution with the median velocities (orange points) and the best-fitting velocity gradient. Central panel: Velocity distribution corrected for the systematic velocity. The colored points trace the rotating filaments, and the lines point out the corresponding average velocity. Bottom panel: Resulting velocity distribution corrected for the rotation of each filament.
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: Line-of-sight velocity in units of the hosting cluster velocity dispersion vs. projected position in units of R 200. The horizontal dashed line indicates ∣V gal∣ = σ cl.
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: Hα observed (top panel) and rotation+advection model (bottom panel) velocity map of JO206. We also show the original Hα emission (gray colormap), and the stellar disk (gray contour, M. Gullieuszik et al. 2020).
Paper: Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning
Authors: Jennifer I-Hsiu Li, Sean D. Johnson, Camille Avestruz, Sreevani Jarugula, Yue Shen, Elise Kesler, Zhuoqi (Will) Liu, Nishant Mishra
Figure caption: Left panels: the response functions (Equation (6)) in the six LSST bands of an AGN with M BH = 108 M ⊙, ﹩\dot{M}=1\,{M}_{\odot }\,{\mathrm{yr}}^{-1}﹩ and i = 30°. The centroid time delays of each response function are shown in the vertical lines and labeled at the top right corner of each panel. Right panels: an example of the simulated light curves (black dots from the second to the last panels). The shaded light curves are the best-fit models (see Section 4.2) from JAVELIN (orange) and CREAM (blue), and the top panel shows the best-fitted driving light curve by CREAM. The gray line in each panel shows the u-band light curve as a reference. The longer wavelength light curves are the convolution of the u-band light curve and the response functions.
Paper: Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning
Authors: Jennifer I-Hsiu Li, Sean D. Johnson, Camille Avestruz, Sreevani Jarugula, Yue Shen, Elise Kesler, Zhuoqi (Will) Liu, Nishant Mishra
Figure caption: An overview of the SBI setup of this work. The simulator includes the accretion disk model and light curve generation described in Section 2. We train a shallow embedding summary network and masked autoregressive flow (MAF) density estimator to evaluate the BH parameters (cosine of the accretion disk inclination cos(i), BH mass M BH, and accretion rate ﹩\dot{M}﹩) using the 180 × 7 light curve arrays (one array for time t and six arrays for the multiwavelength flux f, each with 180 time steps; Section 3.1). We implement the SBI framework on idealized (Section 4.1) and realistic (Sections 5.1 and 5.3) simulated light curves.
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: (a) Residual Hα velocity map and VSF for JO113 (top panels), JO147 (middle panels), and JO162 (bottom panels). (b) Residual Hα velocity map and VSF for JO171 (top panels), JO175 (middle panels), and JO204 (bottom panels). (c) Residual Hα velocity map and VSF for JW39 (top panels), JW56 (middle panels), and JW100 (bottom panels).
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: Velocity maps of the simulated tail, divided by simulation runs (from left- to right-hand, W0, W45, and W45-y) and epoch (from top to bottom, 100, 200, 300, and 400 Myr after the beginning of the stripping).
Paper: Investigating the Intracluster Medium Viscosity Using the Tails of GASP Jellyfish Galaxies
Authors: Alessandro Ignesti, Gianfranco Brunetti, Marco Gullieuszik, Nina Akerman, Antonino Marasco, Bianca M. Poggianti, Yuan Li, Benedetta Vulcani, Myriam Gitti, Alessia Moretti, Eric Giunchi, Neven Tomičić, Cecilia Bacchini, Rosita Paladino, Mario Radovich, Anna Wolter
Figure caption: (a) Observed (top panels) vs. modeled (bottom panels) Hα velocity maps for JO113, JO147, JO162, JO171, JO175, and JO204. (b) Observed (top panels) vs. modeled (bottom panels) Hα velocity maps for JW39, JW56, and JW100.