adsabs/AstroCLIMB huggingface dataset:
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Demonstration of fitting the HOD of H I galaxies using the galaxy two-point correlation function. Three example M HI bins are shown. The panels in the top row show the measured and modeled correlation functions (blue circles and red curves, respectively). The bottom panels show the measured HOD for centrals and satellites (pink circles and blue squares, respectively) against that predicted (green/black curves for centrals/satellites) by our best-fitting set of “hyperparameters” obtained from fitting the correlation functions.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Measured H I mass function of DARK SAGE (points) compared to the mass function predicted (lines) by integrating over our best-fit conditional HOD (with parameters given in the last two columns of Table 1). Blue points and curves are the total mass function, which we further split into centrals (yellow) and satellites (pink). The error bars are tiny.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Halo biasing parameter as a function of ν = δ  c /σ(m). The blue dots are the measured halo biasing parameters of the DARK SAGE simulation, the red curve is the model fit to the measurements, and the gray curve is the fit from Tinker et al. (2005).
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Mean H I content of halos (inside 2R vir) as a function of the virial mass. Compared are the direct output of DARK SAGE, the prediction from our H I-conditional HOD (fitted to DARK SAGE data but not this relation in particular), and two observational data sets based on the ALFALFA survey (Obuljen et al. 2019; Guo et al. 2020).
Paper: Orbital Dynamics Landscape near the Most Distant Known Trans-Neptunian Objects
Authors: Kathryn Volk, Renu Malhotra
Figure caption: Semimajor axis vs. ψ Poincaré maps in the circular planar restricted three-body problem (left panel) and in simulations with all four giant planets on their present orbits and test particles on inclined orbits (right panel) for a region of phase space near the orbit of TNO 148209 (2000 CR105). The large black dots connected by black vertical lines show the TNO’s best-fit orbit (middle circle) along with 1σ and 3σ uncertainties in a; the uncertainty in ψ is negligible. The plots show the semimajor axis vs. ψ for test particles (different particles shown in different colors) when they pass through perihelion. To best highlight the resonant structures, particles that remain in resonance with Neptune for more than 1000 resonant cycles are plotted with the largest, most opaque points; particles librating for 750–1000 resonant cycles are plotted in slightly smaller, slightly more transparent points, as these particles’ paths temporarily stick near the resonant islands before dispersing into the nonresonant phase space. Other, generally nonresonant, chaotic particles are shown in small, more transparent points. The resonant boundaries obtained from the three-body problem (the librating resonant test particles with the largest a and ψ variations) are shown as black curves in both panels for the labeled MMRs with Neptune; the simplified problem captures the widths of the strongest resonances quite well.
Paper: Orbital Dynamics Landscape near the Most Distant Known Trans-Neptunian Objects
Authors: Kathryn Volk, Renu Malhotra
Figure caption: Poincaré maps for test particles on orbits similar to those of the observed high-a, high-q TNOs listed in Table 1. Each panel shows semimajor axis vs. ψ at each perihelion passage for test particles with identical q, i, and Ω to the object in the panel title; the test particles are given an expanded range of initial a and ψ. Test particles evolve under the influence of all four giant planets, and ψ is defined as λ − λ N. Each individual test particle is plotted in a different color. Test particles librating in N:1, N:2, N:3, N:4, or N:5 resonances for at least 1000 resonant cycles are shown by the largest, most opaque points; particles that librate for 750–1000 resonant cycles are shown by slightly smaller, slightly less opaque points. Shorter-term resonant particles, higher-order (higher than N:5) resonant particles, and nonresonant particles are plotted with the smallest, most transparent points. The black line and circles in each panel show the observed object’s best-fit orbit (middle) and 1σ and 3σ uncertainties (taken from JPL Horizons); in cases where the uncertainties in a are large and/or span a large number of resonances, only the 1σ range is shown (the uncertainties in ψ for the observed objects are very small). The first six panels are sorted from weakest to strongest resonances, which roughly correlates with increasing q (though not strictly increasing q due to the wide range of semimajor axes). Note that for 2010 GB174, only the N:1, N:2, and N:3 resonances within the object’s 1σ uncertainty range were simulated, and for 2015 RX245 and 2016 SD106, only the N:1 and N:2 resonances were simulated; this is due to the large orbital uncertainties these objects span and the diminishing stability seen for the higher-order resonances in these regions. A representative subset of the simulation data underlying these figures is available on GitHub (https://github.com/katvolk/TNO-res-maps).
Paper: Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning
Authors: Bendict Lawrance, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, Sumiaya Rahman
Figure caption: (a) Poor quality image observed by K-Cor at 20:06:38 UT on 2017 February 2. (b) Replaced K-Cor-like AI-generated image by this model. Note: the yellow arrow in the south polar region indicates the coronal ray-like structure and HS indicated the helmet streamer.
Paper: Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning
Authors: Bendict Lawrance, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, Sumiaya Rahman
Figure caption: Distribution of the target and the AI-generated polar coronal hole sizes.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: We have quantified how accurate our GNN, that has been trained on galaxy catalogs to predict its power spectrum, extrapolates by using it to predict the power spectrum of point distributions with very different clustering properties: Poisson process (top), Neyman–Scott (middle), and Soneira–Peebles fractal hierarchical clustering (bottom). The left column shows examples of the graphs for these point distributions, while the right column displays the true power spectra (solid) vs. the prediction of the model (dotted). As can be seen, the model is not very accurate at predicting the correct amplitude of the power spectrum on large scales. We believe that this may happen because the model has learned some correlations that are only present in the galaxy catalogs it was trained on. See the text for details.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: Robustness test. We have tested the networks, trained on galaxy catalogs from the IllustrisTNG/SIMBA simulations and on catalogs from the SIMBA/IllustrisTNG simulations, showing the results in the left/right columns. As can be seen, our models are not robust in any of the considered scenarios. This can be due to the intrinsic differences that the distinct subgrid models imprint in the galaxy properties, abundance, and clustering. See the text for details.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: We have trained GNNs to infer the value of σ 8 from galaxy catalogs that include galaxy positions, M *, R *, Z *, and ﹩{V}_{\max }﹩ on simulations from the IllustrisTNG (left) and SIMBA (right) suites. As can be seen, our models are not able to infer the value of σ 8 accurately.
Paper: Chemical Composition of B-type Stars from LAMOST DR5
Authors: Zhicun Liu, Wenyuan Cui, Chao Liu, Sofya Alexeeva, Jianrong Shi, Gang Zhao
Figure caption: The comparison of our effective temperatures, surface gravities, and projected rotational velocities for 36BFL stars with those from the literature. The color symbols represent the stars from different sources. The uncertainties of our stellar parameters (ΔT eff = 1000 K, ﹩{\rm{\Delta }}\mathrm{log}\,g﹩ = 0.25, Δ ﹩v\sin \,i﹩ = 30 km s−1) are represented by horizontal error bars. The vertical error bars correspond to uncertainties of stellar parameters from the literature (see Table 2).
Paper: Chemical Composition of B-type Stars from LAMOST DR5
Authors: Zhicun Liu, Wenyuan Cui, Chao Liu, Sofya Alexeeva, Jianrong Shi, Gang Zhao
Figure caption: Distribution of spectral types of 97 OBA-type supergiants in LAMOST DR5. The luminosity tags are represented by different colors. There is a bimodal distribution in spectral types.
Paper: Chemical Composition of B-type Stars from LAMOST DR5
Authors: Zhicun Liu, Wenyuan Cui, Chao Liu, Sofya Alexeeva, Jianrong Shi, Gang Zhao
Figure caption: Comparison of TLUSTY model spectra (red dashed lines) with the LAMOST spectra (black lines) for five stars with different stellar parameters. Their stellar parameters (T eff, log g, and ﹩v\sin \,i﹩) are also marked in the figure.
Paper: Chemical Composition of B-type Stars from LAMOST DR5
Authors: Zhicun Liu, Wenyuan Cui, Chao Liu, Sofya Alexeeva, Jianrong Shi, Gang Zhao
Figure caption: Flow-process diagram for estimating the stellar parameters of B-type stars in LAMOST DR5. See Section 3.2 for more details.
Paper: Chemical Composition of B-type Stars from LAMOST DR5
Authors: Zhicun Liu, Wenyuan Cui, Chao Liu, Sofya Alexeeva, Jianrong Shi, Gang Zhao
Figure caption: Distribution of signal-to-noise ratio in the g band (S/N  g ) of the spectra of 103 B-type stars in LAMOST DR5.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: HOD of H I galaxies as measured from the output of DARK SAGE. The top panel showcases the HOD of central galaxies: that is, the probability that a halo of a given mass (per the x-axis) will host a central galaxy of a given H I mass. The middle panel showcases the HOD of satellite galaxies: that is, the expected number of satellites within a given H I mass bin to exist in a halo of a given mass. The color of the curves in the top and middle panels indicate the H I mass of the galaxies, corresponding to the bar of the same color in the bottom panel.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: The f cs and f ss for five example halo mass bins. The green, pink, blue, yellow, and purple colored lines are for halo mass bins centered on M = 11.31, 12.13, 12.95, 13.77, and 14.59, respectively. The dashed curves depict the NFW profile for these masses. The solid curves showcase the measurements from DARK SAGE. All these curves (both solid and dashed) are normalized to integrate to one in the interval x ∈ [0, 1].
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: The dashed, dashed–dotted, and solid curves depict the Fourier transformation of the NFW profile calculated using Equation (B4). The dots, stars, and triangular markers showcase the Fourier transformation of the measured galaxy radial distribution from the DARK SAGE simulation. The blue color is for halo mass M = 11, the brown color is for halo mass M = 12, and the pink color is for halo mass M = 13.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Fits of our model HOD, Equations (3a) and (3b), to the measured HOD. Three example M HI bins are shown. The black and green curves are the best-fit satellite and central models, respectively, while the blue squares and pink circles show the measurements from these two classes of galaxy.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Best-fit values of HOD parameters M  c , M  a , ϕ  c , and ϕ  a  as functions of the H I mass.
Paper: H I HOD. I. The Halo Occupation Distribution of H I Galaxies
Authors: Fei Qin, Cullan Howlett, Adam R. H. Stevens, David Parkinson
Figure caption: Comparison between the ﹩{\xi }_{{gg}}^{\mathrm{mea}}﹩ of DARK SAGE (blue dots) and the observational measurements from Martin et al. (2012; gray squares).
Paper: Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe
Authors: A. Fraser-McKelvie, L. Cortese
Figure caption: Mass distributions of the the total volume-weighted MaNGA kinematic sample (black) and the contribution from dynamically cold disks (blue), intermediate systems (green), and slow rotators (red). The full sample is shown in the top panel, the middle panel shows the mass distribution for star-forming galaxies only (ΔMS < −0.5), and the bottom panel shows the passive population. Dynamically cold disks dominate the mass budget for star-forming galaxies, but intermediate systems dominate in the passive sample. Slow rotators account for an appreciable amount of mass only for passive galaxies with ﹩\mathrm{log}{M}_{\star }[{M}_{\odot }]\gt 11﹩.
Paper: Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe
Authors: A. Fraser-McKelvie, L. Cortese
Figure caption: Histograms of dynamically cold disks (left), intermediate systems (middle), and slow rotators (right) in four bins of stellar mass as a function of distance from the star-forming main sequence (ΔMS). The dividing line between passive and star-forming galaxies used in this work is denoted at ΔMS = −0.5 as a gray dashed line. The overall distribution of MaNGA galaxies for a given stellar mass bin is shown in gray.
Paper: Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe
Authors: A. Fraser-McKelvie, L. Cortese
Figure caption: Volume weighting diagnostics. On the left, we plot the volume weights assigned to each galaxy in our kinematic sample by Wake et al. (2017; mauve) and those recalculated based on completeness as a function of stellar mass and redshift (dark purple). The difference in volume correction is most apparent where we are least complete: at low stellar masses. In the right panel, we plot a histogram of the MaNGA volume-corrected stellar mass function (mauve) and that made using the volume corrections calculated for this work (dark purple). We show that our technique for volume weighting recovers the mass function well for stellar masses of ﹩\mathrm{log}{M}_{\star }[{M}_{\odot }]\gt 9.75﹩. In both panels, the shaded gray region indicates the stellar mass range excluded from this work.
Paper: Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe
Authors: A. Fraser-McKelvie, L. Cortese
Figure caption: Examples of SDSS gri images (with the MaNGA field of view overlaid in magenta) and stellar V/σ maps of three passive dynamically cold disk galaxies (left) and passive intermediate systems (right) in the sample. While the visual morphologies are very similar, there are obvious differences in the V/σ maps.
Paper: Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning
Authors: Bendict Lawrance, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, Sumiaya Rahman
Figure caption: Comparison between the target K-Cor white-light image and the AI-generated images between 1.11 and 1.25 Rs based on single- and multichannel inputs at 17:33:41 UT on 2019 November 11. Note: the solar coronal images are considered in polar coordinates (Azimuth angle) starting from the westward equatorial region for this study. Helmet streamers (HSs) and pseudostreamer (PS). The red box represents the region not well identified by Model 1.
Paper: Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning
Authors: Bendict Lawrance, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, Sumiaya Rahman
Figure caption: Comparison of polar coronal hole sizes of the (a) target K-Cor observation on 2018 November 27 at 19:38:52 UT, the (b) corresponding AI-generated image, and the intensity map of (c) target K-Cor and (d) AI-generated images.
Paper: Beyond Galaxy Bimodality: The Complex Interplay between Kinematic Morphology and Star Formation in the Local Universe
Authors: A. Fraser-McKelvie, L. Cortese
Figure caption: Completeness fractions for star-forming (left) and passive (right) samples as a function of redshift and stellar mass. These fractions are used to calculate amended volume weights for the incomplete MaNGA sample used in this work.
Paper: Generation of Solar Coronal White-light Images from SDO/AIA EUV Images by Deep Learning
Authors: Bendict Lawrance, Harim Lee, Eunsu Park, Il-Hyun Cho, Yong-Jae Moon, Jin-Yi Lee, Shanmugaraju A, Sumiaya Rahman
Figure caption: Flowchart of our deep learning model (cGAN) used in this study.
Paper: Orbital Dynamics Landscape near the Most Distant Known Trans-Neptunian Objects
Authors: Kathryn Volk, Renu Malhotra
Figure caption: Poincaré maps of Neptune’s 20:1 MMR (for q  =  44 au) in the circular planar restricted three-body problem (top left) compared to simulations with increasing complexity. In the top right panel, test particles evolve on orbits under the influence of the Sun plus a coplanar Neptune with an eccentricity of 0.02 (and a fixed orbital orientation); this slightly increases the “fuzziness” of the particle paths in the a–ψ plane. In the middle left panel, we have added perihelion precession to Neptune’s orbit using a J2 term; this again slightly increases the fuzziness. In the middle right panel, we have added Jupiter to the system at its current eccentricity (both planets and the test particles remain coplanar); this significantly blurs the distinction between separate particle paths as Jupiter causes Neptune’s semimajor axis and eccentricity to vary over time. In the bottom left panel, we include all four giant planets (at their current eccentricities) on coplanar orbits with each other and the test particles; the perturbations from the additional planets reduce the extent of the stable symmetric libration zone. In the bottom right panel, the giant planets are all on their current, mutually inclined orbits, and the test particles are initialized on orbits inclined by ∼23° (inclination and node matching TNO 148209).
Paper: Orbital Dynamics Landscape near the Most Distant Known Trans-Neptunian Objects
Authors: Kathryn Volk, Renu Malhotra
Figure caption: Time-averaged ψ distribution for clones of the 23 observed TNOs listed in Table 1 (thick purple histogram) and the subset of those TNOs that overlap with the Brown & Batygin (2021) TNO sample (nine of their 11 total TNOs; green histogram) from a 40 Myr integration of clones sampling each object’s orbit-fit uncertainty; each TNO’s current, observed ψ value is shown as a dot near the top of the plot (with arbitrary shifts in the y-position to distinguish objects with similar ψ). We would expect the histograms to be flat if Neptune’s resonances did not have a significant influence on the evolution of the simulated orbits. The notable features near ∼90° and ∼270° are due to clones of the TNOs interacting with the N:1 and N:2 resonant libration centers near those values.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: We have trained GNNs to investigate whether they can learn to compute the power spectrum of a distribution of points, using IllustrisTNG and SIMBA galaxy catalogs at different redshifts as the training data set. The top panel shows with solid lines power spectra from galaxy catalogs in the test set, while the predictions of the model are displayed with dotted lines. The bottom panel shows the average relative error as a function of k. As can be seen, the GNNs are able to learn to compute the power spectrum with a relatively high accuracy, especially at small scales.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: We have trained GNNs to perform likelihood-free inference on the value of Ωm from galaxy catalogs that contain: (1) only galaxy positions (top row), (2) galaxy positions, stellar masses, radii, and metallicities (middle row), and (3) galaxy positions, stellar masses, radii, metallicities, and ﹩{V}_{\max }﹩ (bottom row). The left column shows the results of the models that have been trained and tested on catalogs from IllustrisTNG simulations, while the right column displays the equivalent for the SIMBA simulations. The numbers in the legend indicate the value of R 2, the mean relative error (ϵ), and χ 2 as defined in Equation (18). As can be seen, in all cases our models are able to infer the value of Ωm accurately. As expected, when considering more galaxy properties, the constraints improve.
Paper: Orbital Dynamics Landscape near the Most Distant Known Trans-Neptunian Objects
Authors: Kathryn Volk, Renu Malhotra
Figure caption: Estimated observable ϖ distribution for clones of the 23 observed TNOs listed in Table 1 (thick purple histogram) and the subset of those TNOs that overlap with the Brown & Batygin (2021) TNO sample (nine of their 11 total TNOs; green histogram) assuming that the true, intrinsic ϖ is uniform and the population has the time-averaged ψ distribution relative to Neptune as shown in Figure 3. Each TNO’s actual observed ϖ value is shown as a dot near the top of the plot (with arbitrary shifts in the y-position to distinguish objects with similar ϖ). We assume an object is observable if at any point over a 10 yr time span its estimated apparent brightness is 24.5 mag or brighter. See Section 4.2 for details.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: 1D and 2D distributions of galaxy properties in the IllustrisTNG (left) and SIMBA (right) simulations. Each point represents a galaxy, and it is color coded according to the value of Ωm. The considered galaxy properties are: the stellar mass, M *, stellar half mass–radius, R *, stellar metallicity, Z *, and maximum circular velocity, ﹩{V}_{\max }﹩. As can be seen, in the CAMELS simulations, galaxy properties occupy different regions in the feature space depending on the value of Ωm. Hence, galaxy properties may be used to used to infer the value of the cosmological parameters.
Paper: Learning Cosmology and Clustering with Cosmic Graphs
Authors: Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
Figure caption: The input to our model is a graph, that we build from a given galaxy catalog. In the graph, the nodes represent galaxies, and two nodes are linked by an edge if their distance is smaller than the linking radius r. This figure show threes graphs from three different CAMELS–IllustrisTNG simulations where r = 1.25 h −1 Mpc. Each box represents the galaxy catalog of a simulation at z = 0. Box units are in h −1 Mpc.