adsabs/AstroCLIMB huggingface dataset:
Paper: Inferring Exoplanet Disequilibria with Multivariate Information in Atmospheric Reaction Networks
Authors: Theresa Fisher, Hyunju Kim, Camerian Millsaps, Michael Line, Sara I. Walker
Figure caption: Output from the interpolation functions: contour plots of network measures and species abundance data as a function of the mean temperature of the input distribution and K zz. Metallicity is 1 solar. The x-axis is the mean temperature in kelvin of the Gaussian distributions analyzed, the y-axis is K zz, and the color scale represents the value of the plotted network measures or mixing ratio. These heat maps demonstrate that the interpolation function can yield useful insights into relationships between atmospheric parameters. For example, the abundance of CO appears to be positively correlated to high values of average shortest path length, whereas CH4 appears to be negatively correlated. NH3's relationship appears much less clear but does have a weak positive correlation with the average edge betweenness centrality. For the full set of contour plots detailing our results across multiple metallicity values, see Figures A1–A10 in the Appendix. For the description of the network measures in the figure, see Table 1 and Section 2.2.
Paper: Toward a Data-driven Model of the Sky from Low Earth Orbit as Observed by the Hubble Space Telescope
Authors: Sarah E. Caddy, Lee R. Spitler, Simon C. Ellis
Figure caption: All-sky maps in Galactic coordinates from sampling our sky model. The models are trained on the entire data set and are used to predict the sky for a set of constraints that we expect to have minimal contributions from earthshine, moonshine, and sunshine (constraints given in Section 3.2). The sky surface brightness predictions for each filter are compared to log-scaled COBE/DIRBE data (top panel). The agreement between the zodiacal feature (S-shaped diffuse feature going from lower left to top right in each map) for filters with the most training data (F606W and F814W) predicted in our models and the COBE/DIRBE data indicates our model can recover the sky background. Filters F435W and F850LP have a smaller training data set and so do not recover as much of the structure present in the COBE/DIRBE data.
Paper: Toward a Data-driven Model of the Sky from Low Earth Orbit as Observed by the Hubble Space Telescope
Authors: Sarah E. Caddy, Lee R. Spitler, Simon C. Ellis
Figure caption: Assessing the performance of the XGBoost machine-learning model (see Section 2.4) for various training sample sizes. Model performance is the mean percentage difference in flux between the measured and predicted sky. The mean percentage difference is calculated from 10 training runs each using a random training sample of the size indicated. The shaded regions reflect the standard deviation of the 10 training runs. The horizontal line in each panel is the model performance when the maximum sample size is used. By a sample size of 2000, the filters from each model have approximately the same model performance of ∼10%. When the maximum sample size is used, the best-performing filter is F814W with a performance average of 3.90%, reflecting the filter’s largest sample size. This is also an indication that performance can be improved even further with a larger training set.
Paper: Toward a Data-driven Model of the Sky from Low Earth Orbit as Observed by the Hubble Space Telescope
Authors: Sarah E. Caddy, Lee R. Spitler, Simon C. Ellis
Figure caption: Residuals for the predicted sky from the Leinert and XGBoost models subtracted from the observed F814W nighttime sky. Data were restricted to minimize stray-light contributions using Sun altitude < −10° and limb angle >20°. Blue distributions to the right of each figure show the relative smoothed kernel density estimation distribution of the residuals. The XGBoost model is able to predict the sky to within an average of β of 3.08% of the true sky. The Leinert model shows offsets where the model has overestimated the observed sky near the ecliptic plane. This offset may reflect inherent biases in the model’s interpretation of the structure of the inner solar system interplanetary dust cloud.
Paper: Inferring Exoplanet Disequilibria with Multivariate Information in Atmospheric Reaction Networks
Authors: Theresa Fisher, Hyunju Kim, Camerian Millsaps, Michael Line, Sara I. Walker
Figure caption: Solid lines represent the atmosphere at K zz of zero, dashed lines an atmosphere at K zz of 1010. Top: the abundance of key molecular species as a function of pressure, at 1200 K and 1 solar metallicity. Bottom: network topological measures—average clustering coefficient, and average neighbor degree (see Table 1 and Section 2.2)—as a function of pressure, at an irradiation temperature of 1200 K and 1 solar metallicity. Network topological measures vary as physical conditions, such as pressure, change and capture bulk features of planetary atmospheric chemistry.
Paper: Kinematics of the Central Stars Powering Bowshock Nebulae and the Large Multiplicity Fraction of Runaway OB Stars
Authors: Henry A. Kobulnicky, William T. Chick
Figure caption: A histogram of bias-corrected 2D peculiar velocities of 455 O stars from the Galactic O-Star Catalog (Maíz Apellániz et al. 2016). Vertical dashed gray lines indicate the v 2D runaway thresholds of 25 and 30 km s−1.
Paper: Kinematics of the Central Stars Powering Bowshock Nebulae and the Large Multiplicity Fraction of Runaway OB Stars
Authors: Henry A. Kobulnicky, William T. Chick
Figure caption: Polar representation of the Galactic locations of GOSC O stars (blue ×) and SBN central stars (red dots), with the Sun at the center and Galactic Center toward the right.
Paper: The First Fall is the Hardest: The Importance of Peculiar Galaxy Dynamics at Infall Time for Tidal Stripping Acting at the Centers of Groups and Clusters
Authors: Rory Smith, Paula Calderón-Castillo, Jihye Shin, Mojtaba Raouf, Jongwan Ko
Figure caption: Central panel: 120 Mpc h−1 cosmological box of clusters (red symbols) and groups (green symbols). Blue points indicate zoomed-in clusters (labeled “A”–“D”). Surrounding panels: zoom-in of four representative clusters (labeled “Cluster A–D”) with a deep-pink sphere indicating their z = 0 position (size equal to the cluster R 200). Blue lines indicate the orbital trajectories for all their infallers, colored darker blue according to proximity to the final cluster position.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Left: temperature differences for sight-line pairs through the LIC as a function of angular separation in degrees between the sight lines. The horizontal red line is the mean uncertainty of temperature measurements. Right: mean temperature differences for sight-line pairs through the LIC in 10° angular bins. The vertical lines show the variance of the temperature differences in each angular bin and the star symbols are the mean value in each bin. The blue line is a least-squares fit to the mean temperature differences as a function of angular separation.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line turbulent velocities for the full data set (left) and the LIC-only data set (right) vs. angle from the magnetic field direction. The data are plotted in 20° bins. Asterix symbols indicate averages in each bin, and the vertical lines indicate the variance in the data. Asterix symbols with no vertical lines indicate only one datum in the bin.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line turbulent velocities for the full data set (left) and the LIC-only data set (right) vs. angle from ϵ CMa. The solid lines are least-squares linear fits to the data.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line temperatures for the full data set (left) and the LIC-only data set (right) vs. angle from ϵ CMa. The solid lines are least-squares linear fits to the data.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line temperatures vs. turbulent velocities for the full data set (left) and the LIC-only data set (right). The solid lines are least-squares linear fits to the data.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of temperatures (left) and turbulent velocities (right) vs. angle from the He I inflow direction (l = 3.°5, b = 15.°2).
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line temperatures (left) and turbulent velocities (right) vs. distance to the star. The solid lines are least-squares linear fits to the data.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of temperatures for the full data set (left) and the LIC-only data set (right) vs. the log of the neutral hydrogen column density. The solid lines are least-squares linear fits to the data.
Paper: Inhomogeneity within Local Interstellar Clouds 
Authors: Jeffrey L. Linsky, Seth Redfield, Diana Ryder, Adina Chasan-Taber
Figure caption: Plots of sight-line temperatures (left) and turbulent velocities (right) vs. angle from the center of the LIC. The solid lines are least-squares linear fits to the data.
Paper: HD-TESS: An Asteroseismic Catalog of Bright Red Giants within TESS Continuous Viewing Zones
Authors: Marc Hon, James S. Kuszlewicz, Daniel Huber, Dennis Stello, Claudia Reyes
Figure caption: Diagnostic plot for HD 269847 (TIC 277099588) showing (a) an échelle diagram of the (b) oscillation spectrum of the star, (c) the collapsed échelle diagram computed as the summation of power across each row in the échelle diagram, and (d) the autocorrelation function, constructed by convolving the oscillation spectrum in (b) with itself. The échelle diagrams are replicated up to (ν  modulo Δν/)Δν = 1.25 to better visualize the repetition of the ridges across radial orders. The blue horizontal line in panels (a) and (b) corresponds to ﹩{\nu }_{\max }﹩, while the green vertical line in panels (a) and (c) corresponds to the position of the radial mode ridge in the échelle diagrams, also known as the offset ϵ. The vertical lines in panel (d) correspond to spacings of 1 and 2 Δν, and are where peaks are expected if there is strong regularity in the oscillation spectrum given a candidate Δν value.
Paper: HD-TESS: An Asteroseismic Catalog of Bright Red Giants within TESS Continuous Viewing Zones
Authors: Marc Hon, James S. Kuszlewicz, Daniel Huber, Dennis Stello, Claudia Reyes
Figure caption: Asteroseismic measurements for the 1709 stars in the HD-TESS catalog. On the abscissa is the frequency at maximum oscillation power (﹩{\nu }_{\max }﹩), while the ordinate ﹩{\nu }_{\max }^{0.75}/{\rm{\Delta }}\nu \sim {M}^{0.25}{T}_{\mathrm{eff}}^{-0.375}﹩ is an asteroseismic proxy for stellar mass. The quantity ﹩\widehat{M}﹩ is the mass proxy computed assuming T eff = 5000 K, shown here to provide an approximation of the range of red giant masses reported in this work. In red are hydrogen shell-burning giants ascending the giant branch (RGB), while in blue are core helium-burning giants (CHeB). For comparison, corresponding measurements for Kepler red giants (Yu et al. 2018) are shown in gray. The inset shows measurements for low-luminosity TESS giants in our sample.
Paper: HD-TESS: An Asteroseismic Catalog of Bright Red Giants within TESS Continuous Viewing Zones
Authors: Marc Hon, James S. Kuszlewicz, Daniel Huber, Dennis Stello, Claudia Reyes
Figure caption: Stellar radius vs. effective temperature for stars with spectral type G-K from the Henry Draper catalog that have been observed for at least six sectors in Cycles 1+2 from TESS and therefore lie near or within the TESS CVZs. The temperatures and radii in this plot are from the TESS Input Catalog (Stassun et al. 2019). Stars in our sample are those that lie between the two lines in this diagram, whereby the upper line corresponds to a 40 R ⊙ radius cutoff, while the lower line corresponds to a fiducial boundary separating early subgiants from the base of the red giant branch (see text). The blue points correspond to stars that show a power excess corresponding to red giant oscillations in their power spectrum.
Paper: Untangling the Galaxy. IV. Empirical Constraints on Angular Momentum Evolution and Gyrochronology for Young Stars in the Field
Authors: Marina Kounkel, Keivan G. Stassun, Luke G. Bouma, Kevin Covey, Lynne A. Hillenbrand, Jason Lee Curtis
Figure caption: Sky map of sources in McQuillan et al. (2014) and Reinhold & Hekker (2020) catalogs in Galactic coordinates that satisfy the criteria of fit. All of the stars are color-coded by their estimated age, as inferred from the empirical gyrochrone relations given in Equation (2). The large panels zoom in on certain fields containing notable clusters and the Kepler field (not drawn to scale).
Paper: HD-TESS: An Asteroseismic Catalog of Bright Red Giants within TESS Continuous Viewing Zones
Authors: Marc Hon, James S. Kuszlewicz, Daniel Huber, Dennis Stello, Claudia Reyes
Figure caption: (Left) Radius–magnitude distribution of oscillating giants before (blue) and after (red) vetting of their large frequency separations Δν as described in Section 3.1. Similar to Figure 1, the radii used in the plot are adopted from the TESS Input Catalog. The plot is aligned such that more luminous giants (with smaller ﹩{\nu }_{\max }﹩) are lower, consistent with how the ﹩{\nu }_{\max }﹩-magnitude distribution for giants is commonly visualized (e.g., Stello et al. 2017; Zinn et al. 2020; Hon et al. 2021).
Paper: HD-TESS: An Asteroseismic Catalog of Bright Red Giants within TESS Continuous Viewing Zones
Authors: Marc Hon, James S. Kuszlewicz, Daniel Huber, Dennis Stello, Claudia Reyes
Figure caption: Cumulative histograms of atmospheric parameters sources for HD-TESS giants as a function of TESS magnitude. The number associated with each source in the legend indicates its priority in the selection, while the quantity N enumerates the total number of stars from that particular source. Sources 1–4 provide measurements using spectroscopy, while sources 5 and 6 apply spectrophotometric methods and color-photometry relations, respectively.
Paper: Untangling the Galaxy. IV. Empirical Constraints on Angular Momentum Evolution and Gyrochronology for Young Stars in the Field
Authors: Marina Kounkel, Keivan G. Stassun, Luke G. Bouma, Kevin Covey, Lynne A. Hillenbrand, Jason Lee Curtis
Figure caption: Top: median spot fraction size at different temperature and age bins. The white contours correspond to the levels of 1.0%, 2.6%, 4.5%, 5.9%, and 7.3%. Bottom: same plot, but the colors correspond to the standard deviation of the light curve (σ var), with Baraffe et al. (2015) isochrones plotted on top of them in white.
Paper: Untangling the Galaxy. IV. Empirical Constraints on Angular Momentum Evolution and Gyrochronology for Young Stars in the Field
Authors: Marina Kounkel, Keivan G. Stassun, Luke G. Bouma, Kevin Covey, Lynne A. Hillenbrand, Jason Lee Curtis
Figure caption: Rate of loss of angular momentum L (top) and specific angular momentum H (bottom), as a function of temperature and age, assuming the empirical relation in Figure 10. The black line tracks the temperature at which the greatest loss rate occurs for a given age.
Paper: Mass Upper Bounds for Over 50 Kepler Planets Using Low-S/N Transit Timing Variations
Authors: Jared C. Siegel, Leslie A. Rogers
Figure caption: Here, we diagram our method of deriving mass constraints from low-S/N TTV data. For this illustration, we adopt the orbital periods, initial transit times, and stellar mass of KOI-245 (Kepler-37). In the left column, we present the outer two planets’ simulated TTV time series under three different equal-mass configurations; both planet masses are fixed to either 1 M ⊕ (purple), 10 M ⊕ (light blue), or 40 M ⊕ (dark blue). The TTVs are calculated via TTVfaster. The shaded gray regions demarcate the standard deviation of the observed TTVs (see Figure 4 for the observed TTV time series). On the right panel, we present ﹩{\phi }_{i}({\vec{{\rm{\Omega }}}}_{j})﹩ for KOI-245.02 (the middle planet) as a function of mass. ﹩{\phi }_{i}({\vec{{\rm{\Omega }}}}_{j})﹩ is the TTV variance for the ith planet given a system with dynamical properties ﹩{\vec{{\rm{\Omega }}}}_{j}﹩. TTVs are again calculated via TTVfaster. Planet eccentricities are fixed to either zero (dark red) or 0.05 (light red), and the planet masses are again set equal; phases are drawn uniformly between 0 and 2π. The observed TTV variance for KOI-245.02 is shown as a dashed horizontal line.
Paper: Mass Upper Bounds for Over 50 Kepler Planets Using Low-S/N Transit Timing Variations
Authors: Jared C. Siegel, Leslie A. Rogers
Figure caption: The excess scatter ratio distributions for systems with a single transiting planet known to date (blue) and systems with multiple known transiting planets (green) both peak near 1 with a skew toward higher values. The mean excess scatter ratio of the planets in single-planet systems is shown as a vertical blue dashed line, and the mean excess scatter ratio of the planets in multiplanet systems is shown as a vertical green dashed line.
Paper: Mass Upper Bounds for Over 50 Kepler Planets Using Low-S/N Transit Timing Variations
Authors: Jared C. Siegel, Leslie A. Rogers
Figure caption: Via hierarchical Bayesian inference, the distribution of V ⋆,i,j  for the sample of Kepler systems with only one confirmed planet is well described by a log-normal distribution with μ ⋆ = 3.08 ± 0.14 log(min2) and σ ⋆ = 2.15 ± 0.09 log(min2). In green we present the posterior distribution of μ ⋆ from MCMC sampling of Equation (12), and in purple we present the posterior for σ ⋆.
Paper: celmech: A Python Package for Celestial Mechanics
Authors: Sam Hadden, Daniel Tamayo
Figure caption: Left panel: comparison of the evolution of the middle orbit’s eccentricity in our example between N-body (blue) and increasingly sophisticated celmech models. The orange curve incorporates the most important terms to second order in the eccentricities and inclinations, while the green curve additionally incorporates a correction from osculating to mean variables (see text). Right panel: comparison of the evolution of the evolution of the middle orbit’s inclination between N-body (blue) and a second-order (orange) and third-order (green) celmech model (both corrected to mean variables).
Paper: celmech: A Python Package for Celestial Mechanics
Authors: Sam Hadden, Daniel Tamayo
Figure caption: Left panel: initial orbital configuration in the reference x − y plane (inclinations to this plane are small ≲ 2°). Right panel: comparison of the evolution of the middle planet’s orbital eccentricity between a celmech model including the leading-order 3:2 MMR terms in the disturbing function between the inner two planets (orange, Equation (1)) to an N-body integration (blue).
Paper: Extremely Inclined Orbit of the S-type Planet γ Cep Ab Induced by the Eccentric Kozai–Lidov Mechanism
Authors: Xiumin Huang, Jianghui Ji
Figure caption: Distribution of the SMAs of binaries and planets in S-type systems. The blue dots represent the S-type planets observed by transit, while the red triangles represent those observed by RV. γ Cep Ab is marked by the yellow pentagram.
Paper: Carbon Abundance of Stars in the LAMOST-Kepler Field
Authors: Athira Unni, Mayank Narang, Thirupathi Sivarani, Manoj Puravankara, Ravinder K Banyal, Arun Surya, S. P. Rajaguru, C. Swastik
Figure caption: Observed spectra of Sun (blue) and the synthetic spectra (red). Top panel shows the best fit with input parameters from LAMOST. Bottom-left panel shows an enlarged view of the CH G-band region. Bottom right panel shows the χ 2 variation with [C/H] for obtaining the best-fit [C/H] value for the Sun with T eff = 5774 K and S/N = 76.
Paper: Mass Production of 2021 KMTNet Microlensing Planets. I
Authors: Yoon-Hyun Ryu, Youn Kil Jung, Hongjing Yang, Andrew Gould, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Kyu-Ha Hwang, In-Gu Shin, Yossi Shvartzvald, Jennifer C. Yee, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Top panel: light curve and model for KMT-2021-BLG-0748 color-coded by observatory. Bottom panel: four zoom-ins of 1.7 days centered on the “bump” anomaly, which is traced by three points. Locals 1 and 2 are preferred by Δχ 2 ∼ 10 over Locals 3 and 4 mainly because the overall light-curve normalization favors the first point on the bump (from KMTS) being brighter than the second (from KMTC). Hence, the χ 2 difference is not fully reflected in the residuals of the bump region. See Figure 7 for the origin of this difference.(The data used to create this figure are available.)
Paper: Mass Production of 2021 KMTNet Microlensing Planets. I
Authors: Yoon-Hyun Ryu, Youn Kil Jung, Hongjing Yang, Andrew Gould, Michael D. Albrow, Sun-Ju Chung, Cheongho Han, Kyu-Ha Hwang, In-Gu Shin, Yossi Shvartzvald, Jennifer C. Yee, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge
Figure caption: Caustic geometries for the four Locals of KMT-2021-BLG-0748. Locals 1 and 2 are a classic example of the inner/outer degeneracy (Gaudi & Gould 1997), i.e., the source passes just inside the inner cusp of the planetary caustic in Local 1 and just inside the outer cusp in Local 2. The source is smaller than the caustic separation, which induces “dimples” in the first two zoom-in panels of Figure 6. Morphologically, Locals 3 and 4 are “satellite” solutions of Locals 1 and 2, in which the source passes closer to the cusp. However, the source is about 3 times larger, which induces smooth bumps (i.e., no dimples) in the last two zoom-in panels of Figure 6. And this change induces large changes in s and q.
Paper: Carbon Abundance of Stars in the LAMOST-Kepler Field
Authors: Athira Unni, Mayank Narang, Thirupathi Sivarani, Manoj Puravankara, Ravinder K Banyal, Arun Surya, S. P. Rajaguru, C. Swastik
Figure caption: Distribution of S/N, T eff, and log g of the final sample (4800 ≤ T eff ≤ 6500 K and log g ≥ 4.0). The small-planet host with planet radius R  p  ≤ 4 R ⊕, giant-planet(R  p  > 4R ⊕) host stars and field stars are in green, red and blue respectively.
Paper: Carbon Abundance of Stars in the LAMOST-Kepler Field
Authors: Athira Unni, Mayank Narang, Thirupathi Sivarani, Manoj Puravankara, Ravinder K Banyal, Arun Surya, S. P. Rajaguru, C. Swastik
Figure caption: Observed spectra (blue) and synthetic spectra (red). Top panel shows the best fit for a star of T eff = 4800 K and S/N = 75.07. Bottom-left panel shows for the best fit for a star with T eff = 6496 K and S/N = 44.10.
Paper: Carbon Abundance of Stars in the LAMOST-Kepler Field
Authors: Athira Unni, Mayank Narang, Thirupathi Sivarani, Manoj Puravankara, Ravinder K Banyal, Arun Surya, S. P. Rajaguru, C. Swastik
Figure caption: The entire sample from the LAMOST-KEPLER field is shown as a density plot. And the selected samples within the restricted stellar parameters for the current study is shown as an inset. The black and red dots indicate the field and the planet host stars, respectively.