adsabs/AstroCLIMB huggingface dataset:
Paper: 3D Modeling of Moist Convective Inhibition in Idealized Sub-Neptune Atmospheres
Authors: Namrah Habib, Raymond T. Pierrehumbert
Figure caption: Domain-mean sensible and latent heat fluxes for the CM1 test case initialized on a superadiabatic profile calculated using Equations (16)–(17). Heat fluxes are averaged over the last 25 days of the simulation.
Paper: 3D Modeling of Moist Convective Inhibition in Idealized Sub-Neptune Atmospheres
Authors: Namrah Habib, Raymond T. Pierrehumbert
Figure caption: Domain-mean sensible and latent heat fluxes for the CM1 test case initialized on a moist adiabat calculated using Equations (16)–(17). Heat fluxes are averaged over the last 25 days of the simulation.
Paper: Stability of Adiabatic Processes and Evolution of Pickup Ion Dimensionality
Authors: G. Livadiotis, D. J. McComas
Figure caption: Polytropic indices characterizing (a) PUIs, using NH/SWAP, and (b) solar wind protons, using WIND/SWE datasets (SWAP uncertainties are smaller than the dot sizes). Both populations are nearly adiabatic. The values of standard deviation σγ are minimized near the adiabatic index (dash line), while the parabolic approximation near the minimum, according to Equation (14), is shown (red).
Paper: Reassessing the ZTF Volume-limited Type Ia Supernova Sample and Its Implications for Continuous, Dust-dependent Models of Intrinsic Scatter
Authors: Yukei S. Murakami, Daniel Scolnic
Figure caption: A different visualization of Figure 9, in a style similar to Figure 3. Top: measured residuals in 2D rolling bin (left panel of Figure 9; each dot corresponds to each grid point). Dot size represents the number of SNe in each bin. Bottom: color-logmass surface modeled by Host2D (right panel of Figure 9). The vertical line represents the knot location, at which two bilinear functions, the left and right sides, are connected.
Paper: Reassessing the ZTF Volume-limited Type Ia Supernova Sample and Its Implications for Continuous, Dust-dependent Models of Intrinsic Scatter
Authors: Yukei S. Murakami, Daniel Scolnic
Figure caption: An example of a 2D map created for Host2D. Left: the data map, calculated with a rolling bin + MC as discussed in Section 3.1. Bin locations with less than three SNe are excluded and marked in white. Right: the fitted Host2D map. The red dot represents the location of the spline knot that minimizes the χ2. The map is comprised of linear functions connected at the knot.
Paper: Stability of Adiabatic Processes and Evolution of Pickup Ion Dimensionality
Authors: G. Livadiotis, D. J. McComas
Figure caption: The PUI dimensionality ﹩{D}_{{\rm{K}},{\rm{PUI}}}(t)﹩ (a) and its rate ﹩d{D}_{{\rm{K}},{\rm{PUI}}}(t)/d\,\mathrm{ln}\,t﹩ (b) are plotted on a log timescale (t in days), showing a distinction between the four phases.
Paper: Stability of Adiabatic Processes and Evolution of Pickup Ion Dimensionality
Authors: G. Livadiotis, D. J. McComas
Figure caption: The PUI dimensionality ﹩{D}_{{\rm{K}},{\rm{PUI}}}(t)﹩, modeled as in Figure 2(a), and estimated from Voyager 2 data and Equation (23), plotted on a log timescale.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Original (top) and reconstructed (bottom) PAD spectrograms for the 2023 March 15 SEP event (Table 2, Event #138). The middle panels show three components (High: 120°–180°; Mid: 60°–120°; Low: 0°–60°) of the omnidirectional particle flux Iomni over time, aggregated across all energies and apertures, for the original EPI-Lo Level 3 data (red) and the reconstructed data (green). The data enclosed by the red rectangle represent the blob that was used for validation. The blue rectangle is the part featured in Figure 7. The time intervals where the background is shaded red in the Iomni plots are the times where the entire PAD coverage is missing for each corresponding angle range. The reconstruction metrics for this event are as follows: Blob Test—RMSE = 10.40%, MAE = 1.60%, r = 0.57, ρ = 0.59; Random Test—RMSE = 30.80%, MAE = 0.06%, r = 0.68, ρ = 0.84.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: A closer look at the Iomni(0°–60°) and Iomni(120°–180°) for Events #138 and #206 (Figures 6 and 11, respectively). The main plots include 6 hr intervals of the original and reconstructed (red for Igt and green for Irec) intensity values, while the blue time series in the middle represents the ratio between the reconstructed and ground-truth intensities Irec/Igt. The time intervals where the background is shaded red in the Iomni plots are the times where the entire PAD coverage is missing for each corresponding angle range. The right panel shows the contribution of each one of the three intensity components to the total Iomni calculated in Equation (B2).
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Comparison of the raw EPI-Lo Level 3 PAD data (top left) with the reconstructed outputs from three imputation methods used in this study: spline interpolation (top right), MISSForest (bottom left), and MICE–M-RNN (bottom right). Missing PAD values in the original dataset are shown in gray. The red rectangular region in each panel marks a subset of the data for which ground truth exists (visible in the top left panel) but was intentionally masked during training to evaluate model performance. All plots represent the 2-day interval (1-minute time cadence) selected for the 2024 March 11 SEP event observed by the PSP.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Boxplots showing the performance of three imputation methods—MISSForest (MISS), MICE, and MICE–M-RNN—evaluated across two validation schemes (Random Test and Blob Test, as described in Figure 4) using four metrics: percent RMSE, percent MAE, Pearson correlation (r), and Spearman correlation (ρ). Each boxplot represents the distribution of metric values across 28 distinct SEP events listed in Table 2. The green triangle indicates the mean, the yellow horizontal line represents the median, the box shows the interquartile range (IQR) from the 25th to the 75th percentile, and the whiskers extend to 1.5 times the IQR or the minimum/maximum values within that range. Outliers beyond this range are plotted as circles.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Schematic of the two different types of induced missing data masking we perform for validation. For the Random Test (left), we mask out 5% of the available PAD data randomly and spread in different parts of the spectrogram to capture the model’s performance in small-scale reconstructions. For the Blob Test (right), we mask out a contiguous area containing 5% of the available data in order to capture the model’s performance in large-scale reconstructions.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Original and reconstructed PAD spectrograms for the 2023 October 28 SEP event (Table 2, Event #185) produced using the MICE–M-RNN method. The reconstruction metrics for this event are as follows: Blob Test—RMSE = 12.58%, MAE = 0.10%, r = 0.73, ρ = 0.72; Random Test—RMSE = 28.13%, MAE = 0.58%, r = 0.55, ρ = 0.65.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Plots of the Spearman correlation ρ against the maximum intensity for each event reconstructed in this manuscript. Instead of points for each SEP event, the event number that corresponds to Table 2 is plotted. The dotted blue line corresponds to ρ = 0.3, which is commonly set as the Spearman correlation threshold for weak correlation. The SEP predictions are further split into confident (green; ρ > 0.3) and nonconfident (yellow; ρ < = 0.3). The red regression line captures the best fit, with the red shading being the 95% confidence interval.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Original (top) and reconstructed (bottom) PAD spectrograms for the 2023 September 16 SEP event (Table 2, Event #178). The reconstruction metrics for this event are as follows: Blob Test—RMSE = 44.94%, MAE = 4.27%, r = 0.48, ρ = 0.68; Random Test—RMSE = 57.49%, MAE = 1.48%, r = 0.63, ρ = 0.69.
Paper: Reconstructing PSP/ISOIS Pitch-angle-resolved Intensities Using Unsupervised Learning
Authors: Spiridon Kasapis, Manuel E. Cuesta, Leng Ying Khoo, Hameedullah A. Farooki, Sungmin Pak, Jamey R. Szalay, Mitchell M. Shen, Jamie Rankin, Dionysios Christopoulos, George Livadiotis, David J. McComas
Figure caption: Schematic of the PAD imputation workflow used in this study. The IS⊙IS EPI-Lo PAD fluxes are brought to logarithmic space before the ML methods perform the missing data imputation. Evaluation of the models is also performed in logarithmic space before the outputs are exponentiated to bring them back to the original distributions and physical units.
Paper: Disentangling Thermal and Abundance Characteristics in a Solar Flare Using SDO/AIA, Hinode/XRT, and MinXSS-1 Observations
Authors: Xiaoyan Xie, Anna Chen, Crisel Suarez, Katharine K. Reeves, Soumya Roy, Christopher S. Moore, Nishu Karna, Joy Velasquez
Figure caption: (a) EM versus temperature distribution for the plasma cloud and loops in AIA, where MinXSS low and high EM versus temperature are indicated by stars. (b) Synthetic spectra MinXSSAIA of the loops, plasma cloud, and flare (loop+plasma cloud) from AIA observations. As a reference, MinXSS observations are shown in (b). (c) Ratio of the plasma cloud synthetic spectrum to the loop synthetic spectrum.
Paper: Disentangling Thermal and Abundance Characteristics in a Solar Flare Using SDO/AIA, Hinode/XRT, and MinXSS-1 Observations
Authors: Xiaoyan Xie, Anna Chen, Crisel Suarez, Katharine K. Reeves, Soumya Roy, Christopher S. Moore, Nishu Karna, Joy Velasquez
Figure caption: Same as Figure 8 but displaying the DEM uncertainties with photospheric and hybrid abundance models.
Paper: Coronal Mass Ejections Deflected by Newly Emerging Flux: A Combined Analytic and Numerical Study
Authors: Yuhao Chen, Chengcai Shen, Zhixing Mei, Jing Ye, Jialiang Hu, Zehao Tang, Guanchong Cheng, Shanshan Xu, Abdullah Zafar, Yujia Song, Jun Lin
Figure caption: Schematic of the magnetic configuration produced by four components: the flux rope, the image of the flux rope, dipole 1 to generate the unchanged background field, and dipole 2 to model the NEF. The deflection angle θ is the angle between the CME direction and the solar radial direction. By convention, −90° < θ < 0 when the CME deviates clockwise from the radial direction, and 0 < θ < 90° when it deviates counterclockwise. Since we only consider outward propagation (vy > 0), θ is restricted to (−90°,  90°), eliminating any 180° ambiguity. This figure is reproduced from Y. Chen et al. (2024).
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Inferred evolution of the minimum secondary mass with primary mass for the POWER LAW (left), INCREASING PARABOLA (middle), and RELAXED PARABOLA (right) models. Each panel shows the resulting median and 90% credible interval on the minimum secondary mass, including and excluding GW190814. The gray shows the 99% credible interval for the prior for each model, with the lower edge in black. The interval is evaluated over m1 ∈ [2, 100] M⊙, where the plotted range extends to ﹩{m}_{1}\gt {m}_{{\rm{\max }}}﹩ for some individual hyperposterior samples. With GW190814, the entire distribution of ﹩{m}_{2}^{{\rm{\min }}}﹩ is lower, apart from the RELAXED PARABOLA model, which decreases to allow for the lower secondary mass at m1 ∼ 20 M⊙. Due to the driving of the prior, ﹩{m}_{2}^{{\rm{\min }}}﹩ then increases at higher m1 for this model. Otherwise, there is no strong evidence for an increasing ﹩{m}_{2}^{{\rm{\min }}}﹩ with m1 when accounting for model limitations.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Slices of the median and 90% interval for the mass-ratio distribution at primary masses of 10 (left), 20 (middle), and 40 M⊙ (right) for the POWER LAW (top), INCREASING PARABOLA (middle), and RELAXED PARABOLA (bottom) models, each with and without GW190814. The 99% credible interval of the prior at each slice is shown in gray. For the INCREASING PARABOLA and POWER LAW models, the distributions are different when including GW190814, while for the RELAXED PARABOLA model, the intervals are more consistent.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: The inferred primary-mass (top) and mass-ratio (bottom) distributions for the POWER LAW (left), INCREASING PARABOLA (middle), and RELAXED PARABOLA (right) models, each with and without GW190814. The solid line shows the posterior population distribution, and the shaded region is the 90% credible interval for each distribution. For the INCREASING PARABOLA and POWER LAW models, the distributions are different when including GW190814, while for the RELAXED PARABOLA model, the intervals are consistent.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Hyperposteriors on the key mass and mass-ratio hyperparameters with POWER LAW model, with and without GW190814. The ﹩{m}_{2}^{{\rm{\min }}}﹩ hyperparameter γ prefers high values, supporting a nonincreasing or shallowly increasing ﹩{m}_{2}^{{\rm{\min }}}﹩ with m1. The other hyperparameters show disagreement between the choice to include or exclude GW190814.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Hyperposteriors on the key mass and mass-ratio hyperparameters with the RELAXED PARABOLA model, with and without GW190814. The ﹩{m}_{2}^{{\rm{\min }}}﹩ hyperparameters ξ and ζ are consistent with zero without GW190814, but negative ξ is preferred when including GW190814. The other hyperparameters are in agreement between the two choices of observations.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Hyperposteriors on the key mass and mass-ratio hyperparameters with the INCREASING PARABOLA model, with and without GW190814. The ﹩{m}_{2}^{{\rm{\min }}}﹩ hyperparameters ξ and ζ are railing at 0, showing a preference for a nonincreasing ﹩{m}_{2}^{{\rm{\min }}}﹩ with m1. The other hyperparameters show disagreement between the choice to include or exclude GW190814.
Paper: Can Big Black Holes Merge with the Smallest Black Holes?
Authors: Storm Colloms, Zoheyr Doctor, Christopher P L Berry
Figure caption: Example evolution of ﹩{m}_{2}^{{\rm{\min }}}﹩ with m1 for the POWER LAW, INCREASING PARABOLA, and RELAXED PARABOLA model variations we consider for some fiducial hyperparameters within our prior range. The shaded region indicates where ﹩{m}_{1}\gt {m}_{2}^{{\rm{\min }}}\gt 0﹩.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: A multipanel plot showing the Li (left), C (middle), and Na (right) abundances of BH3⋆ (pink star) compared to the dSph Sculptor (blue triangles; D. Geisler et al. 2005), the UFD Reticulum II (upside-down purple triangles; A. P. Ji et al. 2016), BH3⋆’s host halo stream ED-2 (pink squares; E. Dodd et al. 2025), the GC M15 (green crosses; J. S. Sobeck et al. 2011), and other MW halo stars (gray circles; I. U. Roederer et al. 2014b). In all of the panels, [X/Fe] is on the y-axis, with the exception of the first panel which shows lithium in terms of absolute abundance. The dashed line at 0 represents the solar abundance of [X/Fe]. The left panel shows the Spite plateau annotated in purple, illustrating that BH3⋆ lies well below the Spite plateau at A(Li) ∼ 2.2 dex (F. Spite & M. Spite 1982). We separate dwarf stars (triangles) and giant stars (squares) in the left panel to show that metal-poor dwarf stars make up the Spite plateau and giant stars have largely undergone the burning of their lithium.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Left: The observed spectrum of BH3⋆ in black points, with a synthetic spectrum fit to an Eu abundance of [Eu/Fe] = 0.57 in pink and a ±0.1 dex region around this abundance (purple). Middle: The observed spectrum of the red giant in Gaia BH3 in black points, with a synthetic spectrum fit to a Th abundance of [Th/Fe] = 0.25 in pink and a ±0.1 dex region around this abundance (purple). This highlights the possibility of a Th detection at this line. Right: Observed spectrum (black points) compared to a synthesized spectrum with [U/Fe] ∼ −∞ (pink). Gray curves show synthetic spectra from [U/Fe] = −0.75 to 1.75 in 0.1 dex steps in the top panel, and the range of differences from the observed spectrum and synthetic spectra are shown in green in the bottom panel. No uranium feature is clearly detected. The nearby Fe I line at 3859.91 Å appears saturated, complicating the measurement of the U II 3859 Å line.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Similar to Figure 3 but for Fe-peak elements.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: We show three lines of different s-process elements, yttrium, barium, and cerium. In all of the panels, the black points are the observed spectrum, the pink lines represent our derived abundances of these respective elements, and the purple region is ±0.1 dex of the abundances. The green regions in the panels below represent the differences between the observed spectrum and the synthetic spectra.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: The r-process pattern plot for BH3⋆ (pink stars). Two scaled solar abundances are shown in gray (M. Asplund et al. 2009) and green (K. Lodders et al. 2025). The abundances from BH3⋆ generally follow the universal pattern of the r-process and thorium is denoted as an upper limit with a black arrow.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Similar to Figure 3 but light neutron-capture elements.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Similar to Figure 3 but heavy neutron-capture elements. We show our upper limit detection of Th in the final panel, denoting this abundance with a black arrow.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Similar to Figure 3 but for α-elements.
Paper: A Detailed Chemical Analysis of the Red Giant Orbiting Gaia BH3: From Lithium to Thorium 
Authors: Zoe Hackshaw, Keith Hawkins, Catherine Manea
Figure caption: Four representative regions of the continuum-normalized and RV-corrected spectrum of BH3⋆ including Ca II H & K, Hβ, the Mg I triplet, and the CH G band.
Paper: Molecular Gas and Star Formation in Dwarf Galaxies Observed by the Atacama Large Millimeter/submillimeter Array
Authors: Kijeong Yim, Soo-Chang Rey
Figure caption: (Left) Relationship between ΣSFR and ﹩{{\rm{\Sigma }}}_{{{\rm{H}}}_{2}}﹩ for the six dwarf galaxies. The dashed diagonal lines represent constant molecular gas depletion times (﹩{\tau }_{{\rm{H}}_{2}}﹩) of 0.1 and 1 Gyr for reference. The best-fit power-law index (N) is shown in the lower-right corner. (Right) SFE of the molecular gas (SFE﹩{}_{{{\rm{H}}}_{2}}﹩) as a function of radius normalized by the optical radius (R25). The molecular gas depletion time (﹩{\tau }_{{\rm{H}}_{2}}﹩), measured in Gyr, is indicated for each galaxy in the upper-right corner. The horizontal dotted line marks ﹩{\tau }_{{\rm{H}}_{2}}﹩ = 1 Gyr as a reference point.
Paper: Molecular Gas and Star Formation in Dwarf Galaxies Observed by the Atacama Large Millimeter/submillimeter Array
Authors: Kijeong Yim, Soo-Chang Rey
Figure caption: Radial surface density profiles of H2 (blue diamonds), stars (brown stars), and SFR (red circles), along with the individual contributions to the total SFR from NUV (orange triangles) and 12 μm (magenta squares). All profiles are measured in concentric elliptical annuli, each with a fixed radial width of 7﹩\mathop{.}\limits^{\unicode{x02033}}﹩5. The vertical error bars represent the standard deviation of data points in each annulus.
Paper: Molecular Gas and Star Formation in Dwarf Galaxies Observed by the Atacama Large Millimeter/submillimeter Array
Authors: Kijeong Yim, Soo-Chang Rey
Figure caption: Qtot as a function of radius, normalized by the optical radius R25 for the six dwarf galaxies. The horizontal line at Qtot = 1 marks the threshold for gravitational instability; regions below the line are dynamically unstable and susceptible to star formation.
Paper: Molecular Gas and Star Formation in Dwarf Galaxies Observed by the Atacama Large Millimeter/submillimeter Array
Authors: Kijeong Yim, Soo-Chang Rey
Figure caption: WISE 12 μm images and contours (black) for our dwarf galaxy sample, overlaid with contours from the GALEX NUV emission (blue). The lowest contour level for both 12 μm and NUV emissions is approximately 10σ. The PSF, with a Gaussian FWHM of ﹩7\mathop{.}\limits^{^{\prime\prime} }5﹩, is marked by the ellipse in the lower-right corner of each panel.