adsabs/AstroCLIMB huggingface dataset:
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Vertical temperature inference including uncertainties and biases on the temperature and surface elevation measurements. The gray lines show 1000 samples drawn from the posterior fits in each annulus. The black dashed line shows Tgas, and the large colored dots show the true τ = 2/3 surface and Tgas(r, z) for each emission line. The colored points with error bars show the measured surface elevations and brightness temperatures. The error bars on z and T show the 68% confidence interval in each bin.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Extracted and corrected temperature measurements for J = 2–1 emission cubes (points) compared to the true gas temperatures at the location of the τ = 2/3 surface (dashed lines). The temperature extraction and correction steps were performed using the surfaces extracted from the noisy emission cubes, which are shown in Figure 7.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: The true gas temperature at the CO J = 2–1 emission surface (black) compared to the brightness temperature extracted from the model image cube (blue) and the extracted brightness temperature corrected according to Equation (16) (green) and Equation (18) (orange). The points and uncertainties are extracted from the noisy image cube.
Paper: Measuring the Two-dimensional Thermal Structures of Protoplanetary Disks
Authors: Anna J. Fehr, Sean M. Andrews
Figure caption: Results of the vertical temperature inference step, using the extracted emission surfaces and temperatures. The black lines show the measured emission surface in each annulus. Panels are as in Figure 3.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Schematic of the aggregate particle options as implemented into Virga. (a) Spheres grow in radius as larger particles are needed to balance higher convective velocities in Equation (1). (b) If the user prescribes a fixed monomer radius rmon: as particles grow, only the number of monomers increases. However, to allow smaller particle solutions in lighter convective velocity situations, very small particles are treated as single monomers until they are large enough to have a radius equal to rmon (this region is shown to the left of the dashed line). (c) If the user prescribes a fixed number of monomers Nmon, the radius of each of the monomers increases as particles grow. Similarly to case (b), smaller particle solutions in lighter convective velocity situations are allowed by treating aggregates as collections of smaller numbers of monomers (with the smallest possible physical monomer radius, shown left of the dashed line) until the set Nmon is reached.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Fall speeds, vfall, of compact spheres with radius r compared to aggregate particles (in terms of their equivalent compact mass–radius ragg,m) of varying fractal numbers (Df) at a single layer (pressure of 0.1 bar) of an example model atmosphere. At the radius where the fall speed is equal to the convective velocity (wconvect; dashed gray line), a particle of that radius would be stable in that atmospheric layer under the Virga parameterization. Lower Df (i.e., more linear) aggregates grow to larger radii before reaching high fall speeds. The top row shows our implementation where we set the size of the monomer radius rmon; the bottom row shows when instead we set the number of monomers, Nmon. For low fractal dimensions, the aggregate fall speed is tied to the monomer radius fall speed (see the text).
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: The variety of different radii definitions that are used in this paper for spherical and nonspherical shapes. To illustrate the definitions, in the example above all three shapes have identical mass, but the characteristic radius rc (used for calculating the optical properties and the fall velocity) is different for each shape. The characteristic radius rc for a sphere is r. The characteristic radius for an aggregate is Ragg, which is taken to be equal to the radius of gyration Rgyr (and as shown above, this varies depending on the distribution of mass). Ragg is much larger for linear, lacy shapes compared to more compact shapes even when the masses of the two aggregates are the same. The compact radius ragg,m of the aggregates would be equal to r because the masses of these three shapes are the same (i.e., if the aggregates were compressed into spheres, all three spheres would have the same radius r). The monomer radius rmon is also shown. The radius rw (see Equation (16)) represents the characteristic radius where vfall = w* and this would therefore be different for all three shapes.
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: The extinction opacity of aggregate particles of KCl as a function of wavelength for different monomer sizes rmon with an aggregate radius of Ragg = 10 μm, computed by MMF with Optool. The dotted gray line shows extinction for a spherical particle with a radius of 10 μm. From left to right we show fractal dimensions Df of 1.2, 2.0, and 2.8. Our middle panel with Df = 2 reproduces Figure 4 of K. Ohno et al. (2020).
Paper: Fractal Aggregate Aerosols in the Virga Cloud Code. I. Model Description and Application to a Benchmark Cloudy Exoplanet
Authors: Sarah E. Moran, Matt G. Lodge, Natasha E. Batalha, Kazumasa Ohno, Sanaz Vahidinia, Mark S. Marley, Hannah R. Wakeford, Zoë M. Leinhardt
Figure caption: Comparison of Qscat shown for a fractal dimension Df = 1.8 over a grid of particle sizes using different optics tools, i.e., MMF via Optool  (C. Dominik et al. 2021), the discrete dipole approximation (DDA) via CORAL  (M. G. Lodge et al. 2024a), as well as Mie theory from both. At a fraction of the computational cost, MMF is much closer to the “reality“ of DDA compared to Mie theory for fractal aggregates.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Density (top) and enclosed mass (bottom) profiles from the Einasto (red lines) and NFW (black lines) posterior distributions. The dotted lines in both panels mark the solar distance from the Galactic center. The top panel shows that the posterior distribution of the NFW density profiles is consistent with the fiducial NFW profile (blue lines) from M. Cirelli et al. (2011), with a cuspy central density. On the other hand, the Einasto density profiles are cored and differ significantly from the NFW at the inner Galaxy. The bottom panel shows literature constraints (gray circles; see, e.g., E. V. Karukes et al. 2020; W. Wang et al. 2020 and references therein) of the enclosed mass from globular clusters, dwarf galaxies, and stellar streams. The NFW profiles are qualitatively consistent with the literature, while the Einasto profiles flatten out at r > 30 kpc, suggesting strong tension with results from other probes.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the axis-asymmetric potential experienced by star particles (top) and the stellar mass (bottom) in the galactic plane for m12f. In the top panel, we show the difference between the total potential from simulation and the AGAMA best-fitted axisymmetric potential. Such color-coding highlights the axis-asymmetric components in the potential. The cyan mask and dashed lines mark the six slices of stars selected to compute rotation curves described in Section 4.3. The qualitative resemblance between the top and bottom panels indicates that the axis-asymmetric fluctuation in the potential is partially driven by structures in the stellar mass distribution.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the simulation snapshot curves (set 2) within 250 Myr from z = 0. From left to right, the three plots show the results for different simulated galaxies at z ∼ 0 (snapshot 600): m12i, m12f, and m12m, respectively. For each galaxy, the upper panel shows the derived rotation curve as a function of radius from the galactic center based on stars in snapshots from 580 to 600 (z ∼ 0.02–0.0). Similar to Figure 4, the lower panel shows the scatter (km s–1) in these curves. The light-gray curves represent measured curves from earlier snapshots. To mitigate existing axis asymmetry and highlight the time evolution, we average the curve across azimuthal angles for a given snapshot.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Best-fit model rotation curves using the NFW (top black lines) and Einasto (bottom red lines) profiles fitted on measurements from X. Ou et al. (2024; red circles), with systematic uncertainties derived from m12i in this study. Literature values for the rotation curve are taken directly from Y. Huang et al. (2016), H.-F. Wang et al. (2023), and Y. Zhou et al. (2023). We emphasize that we only fit the measurements from X. Ou et al. (2024; red circles), with the added systematic uncertainties from m12i in this study. Compared between the two panels, the extrapolated model rotation curves from NFW (black lines) are more consistent with the rotation curve measurements from other probes at R > 30 kpc.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the axis-asymmetric potential experienced by star particles (top) and the stellar mass (bottom) in the galactic plane for m12i (left) and m12m (right). The AGAMA-fitted axisymmetric component is subtracted to highlight the azimuthal variation in potential. The cyan mask at the center of the top panel indicates the region not employed in the rotation curve calculation owing to known nonaxisymmetric potential driven by the galactic bar. The dashed lines mark the different azimuthal slices used to compute the rotation curves. The deviation in gravitational potential from complete axisymmetry in the top panel matches qualitatively with the stellar mass distribution in the stellar disk.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between the simulation snapshot curves (set 2) within 250 Myr from z ∼ 0.1 for m12f. The light-gray curves represent measured curves from earlier snapshots. To mitigate existing axis asymmetry and highlight the time evolution, we average the curve across azimuthal angles for a given snapshot. The more significant scatter is driven by the merger event as described in Section 4.4.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Posterior distribution of parameters for the Einasto (top), gNFW (bottom left), and NFW (bottom right) profile fit. The red line marks the median of the distribution, with the shaded region representing the 16th to 84th percentile. The gNFW inner slope parameter β and scale radius r posterior are non-Gaussian and poorly constrained.
Paper: Decoding the Galactic Twirl: The Downfall of Milky Way–Mass Galaxy Rotation Curves in the FIRE Simulations
Authors: Xiaowei Ou, 筱葳 欧, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
Figure caption: Comparison between Einasto (left) and NFW (right) profile fits to the measurements from X. Ou et al. (2024; red circles) with systematic uncertainties derived from m12i in this study (black error bars). Systematic uncertainties from X. Ou et al. (2024; blue shaded region) are included for comparison.
Paper: Diffusion Coefficients of WIND/STICS Protons from Diffusive Shock Acceleration with Finite Escape Boundaries
Authors: Johann R. Mejia-Ott, Brent M. Randol
Figure caption: Diffusion coefficient vs. energy per charge for the shock on 2002 April 23, obtained from fitting the VDF to an exponential in position.
Paper: Diffusion Coefficients of WIND/STICS Protons from Diffusive Shock Acceleration with Finite Escape Boundaries
Authors: Johann R. Mejia-Ott, Brent M. Randol
Figure caption: Diffusion coefficient vs. energy per charge for the shock on 2001 April 4, obtained from fitting the VDF to an exponential in distance. Here, the escape length L is fixed at 106 km both upstream and downstream.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: The positional distributions of starspots and model-fitted light curves of sector 14 are shown here. The first and third rows represent the spot distribution on the stellar surface reconstructed from the light curve, and the second and fourth rows show the contribution of Spot1 and Spot2 to the light curve and the fitted model. The blue and orange curves are the light curves due to Spot1 and Spot2, respectively, and the red curve is the resultant curve from modeling. For example, the light curve up to the first six rotations is shown here. In the spot distribution plots, latitude ranges from −90° to 90° and longitude from −180° to 180°.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: The PDCSAP light curves (cyan) and detrended light curves (blue) of each sector of TIC 272272592 are shown here. The detected flare events, identified using ALTAIPONY software, are marked by orange dashed lines. The TESS corresponding sector is indicated in the figure’s title.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: Distribution of estimated peak blackbody temperature and peak emitting area of each flare event using a semiempirical grid.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: The light curves of TIC 272272592 of each sector are shown here. TIC ID and sector number are specified in the figure’s title. The different color within the light curves of each sector reflects different rotational cycles of TIC 272272592. The last plot is the Lomb–Scargle periodogram of TIC 272272592, and the estimated rotation period is shown by a red dashed line, labeled at the corner.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: Probability distribution of the inclination angles is shown here for TIC 272272592 (blue histogram). The red curve represents the smoothed probability density posterior, and the black dashed line denotes the maximum of the posterior. The estimated inclination angle of the star is shown in the top-right corner.
Paper: Probing Starspot Dynamics on the Active M Dwarf TIC 272272592: A Multiyear TESS Study
Authors: Rajib Kumbhakar, Soumen Mondal, Samrat Ghosh, Diya Ram, Dorothy M. Mwanzia
Figure caption: The cyan curve represents the phase light curve of the combined sectors 15 and 41 of TIC 272272592. The black line indicates the mean model (using 5 minute binning) of the light curve, and the red dashed line shows the estimated maximal amplitude, used for spot modeling in the BASSMAN software.
Paper: Estimate of Current Mass of the Large Magellanic Cloud from the Orphan–Chenab Tidal Stream
Authors: Hiroka T. Warren, Heidi Jo Newberg, Autumn G. Guffey, JiaZhao Lin, Eric J. Mendelsohn, Kevin Roux
Figure caption: N-body simulations of the OCS with Plummer LMC with Erkal2019, Salas2019, Mendelsohn2022, and MMWPotential2014 MW potential models (from top to bottom). In addition to our best-fit OCS (blue), we included streams with an LMC mass that differs from the best-fit mass by ±3.0 × 1010 M⊙. The one with the higher LMC mass is colored orange, and the lower mass is colored green. The best-fit LMC mass using Erkal2019, Salas2019, and MMWPotential2014 potentials is MLMC = 5.5 × 1010 M⊙. The best-fit LMC mass using Mendelsohn2022 is MLMC = 6.0 × 1010 M⊙. Note that we only changed the LMC mass in 5.0 × 109 M⊙ increments. The range and mean of deviations of the data points from the center of the simulation are given in the bottom right corner for the ϕ2 vs. ϕ1 panels and the bottom left corner for the d vs. ϕ1 panels. Note that the simulated stream with the Mendelsohn2022 potential is not well fit to data; the stream center in ϕ2 vs. ϕ1 largely deviates from data near ϕ1 = −80°. In the d vs. ϕ1 panel, it also misses data points at ϕ1 < −70° although the 1σ errors are higher for these data points. This slight discrepancy could mean that the Mendelsohn2022 is not as good a representation of the MW as the other three potentials, a suggestion that is supported by the rotation curves in Figure 7.
Paper: Estimate of Current Mass of the Large Magellanic Cloud from the Orphan–Chenab Tidal Stream
Authors: Hiroka T. Warren, Heidi Jo Newberg, Autumn G. Guffey, JiaZhao Lin, Eric J. Mendelsohn, Kevin Roux
Figure caption: Stream fit with Hernquist LMC under the gravitational influence of four different model MW potentials: Erkal2019, Salas2019, Mendelsohn2022, and MMWPotential2014 (from top to bottom). In addition to our best-fit OCS (blue), we included streams with the LMC of ±3.0 × 1010 M⊙ (orange and green). We obtained MLMC =  9.5 × 1010 M⊙ for Erkal2019, Salas2019, and Mendelsohn2022, and 9.0 × 1010 M⊙ for MMWPotential2014. The range and mean of deviations of the data points from the center of the simulation are given in the bottom right corner for the ϕ2 vs. ϕ1 panels and the bottom left corner for the d vs. ϕ1 panels. Note that the simulated stream with the Mendelsohn2022 potential is again not well fit to data. The stream center in ϕ2 vs. ϕ1 largely deviates from data near ϕ1 ∼ −80°. In d vs. ϕ1, it also misses data points at ϕ1 < −80° although the 1σ errors are higher for these data points. This slight discrepancy could be because this potential differs from the other three in the Galactocentric radius range 5 < r < 20 kpc, as shown in Figure 7.
Paper: Estimate of Current Mass of the Large Magellanic Cloud from the Orphan–Chenab Tidal Stream
Authors: Hiroka T. Warren, Heidi Jo Newberg, Autumn G. Guffey, JiaZhao Lin, Eric J. Mendelsohn, Kevin Roux
Figure caption: Best-fit OCS, when varying the mass enclosed within 8.7 kpc according to the upper and lower uncertainty bounds ((1.7 ± 0.7) × 1010 M⊙ according to R. P. van der Marel & N. Kallivayalil 2014). From top to bottom, the panels show the best-fit Plummer LMC using the upper bound mass within 8.7 kpc, the best-fit Plummer LMC using the lower bound mass within 8.7 kpc, the best-fit Hernquist LMC using the upper bound mass within 8.7 kpc, and the best-fit Hernquist LMC using the lower bound mass within 8.7 kpc. We fitted the simulated OCS to data using the Erkal2019 MW potential, and the best-fit parameters are listed in Table 3. The Plummer streams can be compared with the top two panels of Figure 8, and the Hernquist streams can be compared with the top two panels of Figure 11. The range and mean of deviations of the data points from the center of the simulation are given in the bottom right corner for the ϕ2 vs. ϕ1 panels and the bottom left corner for the d vs. ϕ1 panels. With the Hernquist LMC, the estimated LMC mass is more sensitive to the value of M(<8.7 kpc); note that the change in central mass changes the LMC mass from 7.5 × 1010 M⊙ to 2.2 × 1011 M⊙ for the Hernquist LMC, and from 5.5 × 1010 M⊙ to 6.5 × 1010 M⊙ for the Plummer LMC. In both cases, the scale radius of the profiles was adjusted to fit M(<8.7 kpc), so the different mass estimates can equivalently be thought of as a consequence of changing the scale radius or of changing M(<8.7 kpc).
Paper: Estimate of Current Mass of the Large Magellanic Cloud from the Orphan–Chenab Tidal Stream
Authors: Hiroka T. Warren, Heidi Jo Newberg, Autumn G. Guffey, JiaZhao Lin, Eric J. Mendelsohn, Kevin Roux
Figure caption: Enclosed mass of our best-fit Plummer LMC (blue) and Hernquist LMC (red). The dashed vertical lines show the shortest distance from the simulated OCS to the LMC for each profile. The black dots are the enclosed masses from R. P. van der Marel & N. Kallivayalil (2014), which we used to calculate the scale radius of the LMC, and L. L. Watkins et al. (2024) (M(13.2 kpc) = 2.66﹩{}_{-0.36}^{+0.42}﹩ × 1010 M⊙). We also calculated the enclosed mass from Gaia Collaboration et al. (2018) (M(4 kpc) = (6.72 ± 0.40) × 109 M⊙). The gray dashed line at 28 kpc shows the point where both values agree again with M(<r) = 4.7 × 1010 M⊙. For the same mass within 28 kpc, the Hernquist profile has a much larger total mass (at large radius).
Paper: Estimate of Current Mass of the Large Magellanic Cloud from the Orphan–Chenab Tidal Stream
Authors: Hiroka T. Warren, Heidi Jo Newberg, Autumn G. Guffey, JiaZhao Lin, Eric J. Mendelsohn, Kevin Roux
Figure caption: Acceleration of the LMC as a function of its radius for each OCS progenitor model in Figures 8 and 11 (solid lines shows the best-fit LMC, and dashed and dotted lines show the LMC of ±3.0 × 1010 M⊙ with a Plummer profile (blue) and a Hernquist profile (red)). The vertical lines show the shortest distance from the simulated OCS to the LMC for each profile using the Erkal2019 MW potential, and the large dots show the acceleration at closest approach for each case. Note that the blue (Plummer) dots have a much larger difference in acceleration at closest approach than the red (Hernquist) dots do for the same change in LMC mass. Also, note that the best-fit Plummer and Hernquist profiles here have similar acceleration at closest approach.