adsabs/AstroCLIMB huggingface dataset:
Paper: Searching for GEMS: TOI-6383Ab, a Giant Planet Transiting an M3-dwarf Star in a Binary System 
Authors: Lia Marta Bernabò, Shubham Kanodia, Caleb I. Cañas, William D. Cochran, Szilárd Csizmadia, Suvrath Mahadevan, Gudhmundur Stefánsson, Arvind F. Gupta, Andrew Monson, Henry A. Kobulnicky, Alexander K. Larsen, Ethan G. Cotter, Alexina Birkholz, Tera N. Swaby, Gregory Zeimann, Chad F. Bender, Scott A. Diddams, Jessica E. Libby-Roberts, Andrea S. J. Lin, Joe P. Ninan, Heike Rauer, Varghese Reji, Paul Robertson, Arpita Roy, Christian Schwab
Figure caption: Radius evolution in time of a planet with the mass of TOI-6383Ab, according to the model of S. Müller & R. Helled (2021). Top plot: evolution track of the planetary radius varying its heavy-element (core) mass, and keeping the atmospheric metallicity fixed at the TOI-6383A value. The horizontal dashed line indicates the measured radius of the planet. The gray region corresponds to the 16th and 84th quantiles on the planetary radius, while for the age we only have an indication that the star is not young (>1 Gyr). Colors indicate the core mass, which ranges between 7.5 and 50 M  E . Bottom plot: evolution track of the planetary radius varying the planetary atmospheric metallicity from 0.3% to a maximum of 6% as indicated by the colors (with a fixed core heavy-element mass of 20 M ⊕).
Paper: The Breakthrough Listen Search for Intelligent Life: Galactic Center Search for Scintillated Technosignatures
Authors: Bryan Brzycki, Andrew P. V. Siemion, Imke de Pater, Carmen Choza, Steve Croft, Vishal Gajjar, Jamie Drew, Brian C. Lacki, Danny C. Price, Sofia Z. Sheikh
Figure caption: Monte Carlo–sampled scintillation timescales for (l, b) = (1°, 0°) at the C band with N = 104. The dashed line indicates the median timescale, and the dotted lines indicate the first and third quartiles.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Relations between various physical properties of MCs. Panel (a) shows the velocity-integrated CO intensity (W CO) vs. average color excess (﹩\overline{E(G-{K}_{{\rm{S}}})}﹩) with a Pearson correlation coefficient of 0.62 and a p-value of 4.97 × 10−13. Panel (b) displays W CO vs. H I column density (N H I) with a Pearson correlation coefficient of 0.34 and a p-value of 2.50 × 10−4. Panel (c) illustrates ﹩\overline{E(G-{K}_{{\rm{S}}})}﹩ vs. N H I with a Pearson correlation coefficient of 0.38 and a p-value of 3.92 × 10−5. Panel (d) presents W CO vs. Galactocentric distances (R). The Sun’s position at R ∼ 8.34 kpc is marked by the red dotted line for reference. The subsequent panels (e) and (f) use the same position reference of the Sun. Panel (e) shows N H I vs. R. Panel (f) depicts ﹩\overline{E(G-{K}_{{\rm{S}}})}﹩ vs. R. Panel (g) examines the ratio N H/A  V  vs. Galactic longitude (l). Panel (h) investigates the ratio N H/A  V  vs. Galactic latitude (b).
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Ratio of the projected areas vs. radius. For each MC, blue dots represent the area ratio estimated using the CO data angular resolution, while orange crosses represent the ratio using the H I data angular resolution. Points above the horizontal line (ratio = 1) indicate that the projected area of the MC is larger than the single pixel area. The uncertainties in the area ratio are due to distance uncertainties.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Morphological identification maps of selected strongly correlated dust-CO clouds. For each cloud, panel (a) displays the velocity-integrated CO intensity map. The black, cyan, and yellow polygons represent the velocity-integrated CO intensity levels at 3σ, 6σ, and 9σ, respectively. The lines in the color bar indicate the CO integrated intensity at different levels. The red polygon highlights the dust contour boundary. Panel (b) represents the CO intensity integrated along the Galactic longitude l, panel (c) along the Galactic latitude b, and panel (d) shows the averaged CO line velocity-temperature profile for the respective MC, with the sky blue-dotted line indicating the v LSR range.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Upper panel: Histogram of the vertical distances from the Galactic plane Z, where the red line represents the posterior median, and the shaded areas represent the 1σ, 2σ, and 3σ credible intervals for the vertical distribution fit of the MCs, respectively. Lower panel: The dependence of N H/A  V  on the individual clouds at different Z. The median N H/A  V  values for discrete Z bins are marked with red crosses, with each bin spanning 20 pc.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Spatial distribution of the classified MCs in the Galactic X-Y plane, with the Galactic center at coordinates (X, Y) = (0, 0) kpc and the Sun at (X, Y) = (−8.34, 0) kpc. Concentric dotted circles denote distances of 1 kpc, 2 kpc, and 3 kpc from the Sun. The 112 strongly correlated dust-CO clouds are denoted by red circles, while the others are represented by orange circles. The size of each circle is proportional to the cloud’s physical size, and error bars indicate the uncertainty in distance. The underlying dust map is adapted from B.-Q. Chen et al. (2019).
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Best-fit correlation between the column density of hydrogen nuclei N H and visual dust extinction A  V  for the 112 strongly correlated dust-CO clouds, yielding an average GDR value of ﹩({2.80}_{-0.34}^{+0.37})\times {10}^{21}\,{\mathrm{cm}}^{-2}\,{\mathrm{mag}}^{-1}﹩. The best-fit GDR is represented by the red line, while the corresponding different credible intervals are depicted by the gray shading.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Variation of N H/A  V  for the individual clouds located at different Galactocentric distances R. The Sun’s location at R ∼ 8.34 kpc is indicated by the red dotted line for reference.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Quantile-Quantile (Q-Q) plot comparing posterior predictive ﹩{\hat{N}}_{{\rm{H}}}﹩ and measured N H. If the predictive and measured data followed the same distribution, the quantiles would align along the 1:1 line. Significant discrepancies are observed below the ∼2nd percentile.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: CO brightness temperature maps for Cloud 31, following the format of Figure 1.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Morphological identification maps of clouds classified as possibly correlated dust-CO clouds, following a similar format to Figure 4.
Paper: The Correlation Between Dust and Gas Contents in Molecular Clouds
Authors: Rui-Zhi Li, Bing-Qiu Chen, Guang-Xing Li, Bo-Ting Wang, Hao-Ming Ren, Qi-Ning Guo
Figure caption: Relationships among parameters in the hierarchical Bayesian model illustrated in this DAG.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: State variables half a scale height above the surface for selected lava planets. From top to bottom, the panels show pressure, wind speed, atmospheric temperature, and surface temperature. The solutions end when the flow no longer behaves as a continuous fluid and the calculations break down; this is essentially where the horizontal mass advection reaches near 0 and the atmospheric collapse is complete. K2-141b has the thickest atmosphere, as it receives the most stellar flux and therefore has the hottest surface temperature. However, K2-141b’s atmosphere is significantly cooler than HD213885b and HD20329b, as atmospheric temperature is largely dependent on stellar UV flux. The temperature jump for 55 Cnc-e at the edge of the atmosphere can be attributed to a dynamical effect described by A. P. Ingersoll et al. (1985), where the air viscosity begins to induce significant heating at certain pressure and temperature levels; the same phenomenon can be seen in T. Castan & K. Menou (2011).
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: Near-surface atmospheric temperature and pressure of our work compared the T–P profiles of M. Zilinskas et al. (2022). The T–P profiles from M. Zilinskas et al. (2022) are calculated from a representative surface equilibrium temperature of the planet. We plotted our result at the boundary layer matching the same representative surface equilibrium temperature. Our work is ultimately agnostic to the actual shape of the T–P profile, as long as the pressure integral up to the surface boundary layer in Equation (A11) matches that of an adiabat. Although the overall dynamics are different, the net radiative flux balances within the bulk of the silicate atmosphere are fairly similar in both studies, thus producing similar atmospheric equilibrium temperatures.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: The impact of clouds on lava planet surface temperature. The first column is the condensation rate. We zoom in on θ where condensation occurs. We impose the factors f  c  onto the condensation rate to create the optical depth plots shown in the second column, and the opacity is plotted in the third column. The final column is the surface temperature below the cloud deck. Clouds cool the surface significantly (up to 200 K), but the changes in atmospheric temperature are an order of magnitude smaller.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: We show the changes in atmospheric and surface temperature when latent heat is implemented self-consistently in our code. Latent heat cools the surface when evaporating and warms the atmosphere when condensing, because we assume that condensation occurs aloft. As predicted by E. S. Kite et al. (2016), temperature changes due to latent heating are negligible at the surface but warm the atmosphere a little. However, the changes are minor, with the exception of K2-141b, where atmospheric temperatures can warm by 10%. This is due to the relatively large condensation rate but relatively low radiative heating and cooling caused by orbiting around a cooler star.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: Simulated eclipse spectra of selected planets. The spectrum is calculated from the top-of-the-atmosphere emission, which ultimately depends on the temperatures of both the atmosphere and the surface. In strong SiO spectral features, emission originates from the upper layers of the atmosphere. For an adiabatic T–P profile, this leads to an extremely cold temperature reading and thus the eclipse spectra show deep absorption features. Although clouds do cool the atmosphere slightly, the changes in the eclipse depth are barely visible. Since clouds cool the surface more, the overall blackbody curve of the spectra is lowered and the difference between cloudy and cloudless is seen relatively most clearly on HD20329b and HD213885.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: Radiative budget of atmosphere of our five lava planets. The first column shows the absorption of stellar flux, the second shows the absorption of blackbody radiation from the surface, and the third shows radiative cooling. K2-141 is less UV-bright than the other stars, so the stellar absorption of its planet is mostly in the IR. Because of the high UV stellar absorption on HD213885b and HD20329b, their atmospheres become hot enough to emit significant UV radiation.
Paper: Clouds in Partial Atmospheres of Lava Planets and Where to Find Them
Authors: T. Giang Nguyen, Nicolas B. Cowan, Lisa Dang
Figure caption: Radiative cooling as a function of pressure and temperature: we calculate the radiative cooling and separate the fluxes into three channels: UV (left), visible (middle), and IR (right).
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Left: flare frequency distributions (FFDs) for each TESS sector, including completeness-corrected flare rates as determined in Section 3.1. Error bars indicate rate uncertainties, where larger energy points have larger errors due to Poisson number statistics. The solid lines represent the best-fit lines, with a fixed slope of α = −0.85. Sector 41 in green is a clear outlier from the other sectors. Right: quarter-by-quarter FFD for flares in the Kepler sample. The solid lines represent the best-fit lines, with a fixed slope of α = −0.85. Colors map to the time axis in Figure 4, where red is later quarters. It is of interest to note that while the energy ranges are similar between the two telescopes, the energies themselves are not directly analogous across telescopes due to bandpass differences.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Left: FFD intercepts β (Equation (3)) for both Kepler and TESS observations, with colors corresponding to Figures 4 and 5 for TESS. Error bars are derived from adding in quadrature the fitting error, and the statistical uncertainty derived in Section 5 (Figure 8). Right: integrated flare power log(L fl/L bol) for Kepler and TESS observations. There is a clear offset between the Kepler and the TESS data in both panels, which could be evidence for a long-term trend in flare rate, but is also sensitive to the differences in cadence, S/N, and bandpass between Kepler and TESS.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Seventeen quarters of 30 minutes cadence Kepler data for TIC-272272592, spanning over 1500 days. Each quarter is colored by time where increasing time corresponds to redder colors. High-amplitude flares occur throughout the light curve. Long-timescale changes in the amplitude of rotational variability are also apparent.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Left: results for drawing 10 yr baseline power-law distributions and subsampling TESS sectors. The 500 individual power-law draws are shown in cyan. For each trial, 100 single TESS sector redraws are shown in black. Shown in green is Sector 41, replotted from Figure 5 for direct comparison to the simulation. Right: the distribution of the Δ b 's for each of the sector redraws from the left panel.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Soft X-ray flare data from Solar Cycle 23 from RHESSI. The total number of flares per month is given (left), which shows a dramatic shift from activity maximum to minimum. The corresponding monthly flare frequency distribution (FFD) for Cycle 23 (right) is color-coded to match the monthly bins, and demonstrates a ∼2 orders of magnitude change in the specific flare rate over the solar cycle. Additionally, as highlighted by the cumulative FFD, the maximum energy of flares produced by the Sun changes by ∼2 orders of magnitude from solar maxima to minima (blue to red).
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Light curves for the five available TESS sectors of data for the active M dwarf TIC-272272592. Points are colored and sized by the probability of having a flare, as determined by stella. The location of the start time of each flare identified in our analysis is shown within each panel (green triangles). Noting the y-axis scale for the normalized flux, Sector 41 (center panel) shows a notable lack of large-amplitude flares compared with the other TESS sectors, while all other sectors have flares with amplitudes A ≥ 0.1.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: Width of the Δ b  distribution as a function of how many continuous sectors of data are sampled quantified by the 5th–95th percentile range. The orange line represents how many consecutive TESS sectors are equivalent to one Kepler quarter. We see an exponential decrease in width with the number of sectors, indicating that for stars with more sectors, better estimates of point-in-time flare rates can be measured.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption: FFD intercepts β (Equation (3)) as in Figure 6, but with values for the TESS sectors from the 2 minutes light curves binned down to 30 minutes cadence and flares identified using FBEYE. The offset between Kepler and TESS seen in Figure 6 is present, though not as prevalent when considering similar cadence and identifications methods. However, differences in bandpasses still makes direct comparison difficult.
Paper: Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592
Authors: Tobin M. Wainer, James R. A. Davenport, Guadalupe Tovar Mendoza, Adina D. Feinstein, Tom Wagg
Figure caption:  stella completeness determined in bins of flare energy from our injection and recovery tests within each TESS sector, as described in the text (colored points). A logistic function has been fit to each sector (solid lines).
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: Subsample of stars with effective temperatures within ±200 K of solar. (a) Activity vs. isochrone expectation age from L. Casagrande et al. (2011). (b) Our retrieved log N(Li) vs. isochrone age, with symbols as in Figure 3(c); (c) vs. isochrone age with Li abundances averaged over age bins of width 1 Gyr and plotted at the bin’s lower bound, with error bars representing ±1σ standard deviation computed from just these 148 stars; (d) vs. log R′HK with the Li abundances averaged over log R′HK of width 0.1 and plotted at the bin’s upper bound. In all panels the Sun’s values are shown as an orange dot. In (a) the blue dashed line is a linear regression with correlation coefficient R = 0.466. In (c) and (d) the points and triangles depict only the 27 program stars having very low activity log R′HK ≤ −5.0 (these are included in the averages).
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: (a) Comparison of our retrieved Mt. Wilson S-unit values with those from IF10, for 114 of our program stars. (b) For all program stars, the fraction of elapsed main-sequence lifetime as a function of activity derived in this work; blue circles are averages over bins of width 0.1 and plotted at the bin’s upper bound, and error bars represent ±1σ standard deviation. In (b) the Sun’s values are shown as an orange dot.
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: (a) Comparison of our retrieved log N(Li) with those of I. Ramírez et al. (2012), for 51 program stars, where the dashed blue line shows the expected equality. (b) Our log N(Li) retrievals for all 211 program stars as a function of effective temperature; (c) as a function of isochrone expectation age from L. Casagrande et al. (2011), with circles representing full retrievals and open triangles representing upper limits. In (b) and (c) the Sun’s values are shown as an orange dot.
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: Examples of cubic spline-corrected echelle spectra from order 85 for stars with contrasting Li 6708 Å feature strength: (a) HD 115383 with our retrieved log N(Li) = 2.77; (b) HD 163622 with log N(Li) = 2.38; (c) HD 147044 with log N(Li) = 2.08; (d) HD 76752 with inferred log N(Li) < 0.91; (e) examples of normalized order 144 spectra for a star in the normal solar cycling activity range, HD 147044 (retrieved log ﹩{R}_{\mathrm{HK}}^{{\prime} }﹩ = −4.92; black), and an active star HD 115383 (log ﹩{R}_{\mathrm{HK}}^{{\prime} }﹩ = −4.44; red).
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: Projected rotational velocity from J. A. Valenti & D. A. Fischer (2005) vs. our measured order 88 FWHM values. The blue dotted curve is our polynomial parameterization, and the two stars identified at the high FWHM end have v sin i from R. J. White et al. (2007). The red dotted curve depicts the linear model of K. G. Strassmeier et al. (1990).
Paper: Hamilton Echelle Spectrograph Observations of Solar Analog Field Stars: Lithium Abundance and Activity
Authors: Dan Lubin, Bradford P. Holden, Camille Stock, Carl Melis, David Tytler
Figure caption: Distribution of projected rotational velocity among the 211 program stars: (a) average FWHM values of six absorption lines in echelle order 88, corrected for instrument response function; (b) projected rotational velocity from the FWHM values using the parameterization described in the text.