adsabs/AstroCLIMB huggingface dataset:
Paper: Prebiosignature Molecules Can Be Detected in Temperate Exoplanet Atmospheres with JWST
Authors: A. B. Claringbold, P. B. Rimmer, S. Rugheimer, O. Shorttle
Figure caption: Detection thresholds for prebiosignature molecules with NIRSpec G395M as a function of the number of transits using simulated transmission spectra of a model Hycean exoplanet, with synthetic JWST noise from observing GJ 1132 at spectral resolution R = 100.
Paper: Prebiosignature Molecules Can Be Detected in Temperate Exoplanet Atmospheres with JWST
Authors: A. B. Claringbold, P. B. Rimmer, S. Rugheimer, O. Shorttle
Figure caption: Best-fit transmission spectrum to a simulated NIRSpec G395M observation of a Hycean atmosphere with TriArc-calculated detection threshold abundances of three prebiosignatures (HCN, C2H2, and SO2) assuming realistic JWST noise from observing three transits of GJ 1132b, calculated using the retrieval package of petitRADTRANS.
Paper: Prebiosignature Molecules Can Be Detected in Temperate Exoplanet Atmospheres with JWST
Authors: A. B. Claringbold, P. B. Rimmer, S. Rugheimer, O. Shorttle
Figure caption: Spectral precision of JWST instruments as a function of the wavelength, at resolution R = 100 when observing GJ 1132b for 6 hr per instrument, including three transits, at 80% full well saturation, simulated using PandExo.
Paper: FX UMa: A New Heartbeat Binary System with Linear and Nonlinear Tidal Oscillations and δ Sct Pulsations
Authors: Kun Wang, 坤 王, Anbing Ren, 安炳 任, Mads Fredslund Andersen, Frank Grundahl, Tao Chen, 涛 陈, Pere L. Pallé
Figure caption: The amplitude spectrum of the residual LC of FX UMa after subtracting the binarity-induced light variations. The red, green, and blue upside-down triangles mark the frequencies of the detected nonlinear TEOs, linear TEOs, and δ Sct pulsations, respectively. The remaining significant peaks are the combination frequencies, as summarized in Table 5.
Paper: FX UMa: A New Heartbeat Binary System with Linear and Nonlinear Tidal Oscillations and δ Sct Pulsations
Authors: Kun Wang, 坤 王, Anbing Ren, 安炳 任, Mads Fredslund Andersen, Frank Grundahl, Tao Chen, 涛 陈, Pere L. Pallé
Figure caption: The posterior distributions of the binary parameters optimized in the PHOEBE fits to the combined LC and RV observations.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: The resulting distributions of our recovery simulations plotted on top of the distributions from where the synthetic data is drawn for Kipping (2013) (a) and Bowler et al. (2020) (b). The original distributions are represented in red, and the obtained recovered distributions are presented in blue. The different columns are labeled with the distributions obtained for differing N values (i.e., number of planets in the sample, or number of points drawn from the true input distribution). The shaded regions represented in lighter blue and red represent the possible distributions encompassed by our confidence intervals. The purpose of this exercise is to validate our bootstrapping technique and check that we get matching results from the original input (or true) distributions.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: The weighted eccentricity distribution derived via our orbital fits for the sample of 21 companions using observable-based priors. The x-axis represents the eccentricity values, and the y-axis represents the normalized incidence of eccentricities. Here, the fits include both astrometric and RV data points.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Comparison of the obtained beta distribution for the entire sample with the distributions of Bowler et al. (2020) and Kipping (2013). We note that the distribution obtained by Kipping (2013) is for short-period warm Jupiters, whereas Bowler et al.’s (2020) distribution is for long-period extrasolar planets and brown dwarfs. Our sample closely resembles that used by the latter’s work, but with a lower high-eccentricity incidence—likely due to the change from uniform to observable priors.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Stability contour for the HD 1160 system obtained by binning the companions’ posteriors in eccentricity bins of 0.1 width. The colors represent the percent of the 1000 random draws for each bin that remain stable for the age of the system. HD 1160 b and c orbits are shown to disfavor very eccentric orbits by the stability constraint.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Inferred distribution for the sample of directly imaged companions. The beta fit results (68th percentile) from sampling the posteriors are presented in panel (a). The distribution (dark blue) is then plotted (panel (b)) with 100 distributions (light blue) that encompass our uncertainties. The lighter curves indicate the possible ranges of α and β from the fitting distribution.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Eccentricity posteriors of PZ Tel B with (red) and without (blue) the 2018 epoch.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Comparison of the eccentricity posteriors for three different prior + data configurations of HD 1160 b.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Sample of δ α and δ β for our distributions with the whole population compared to the distributions with the whole population not including 1RXS0342+1216 B (a). Gaussian kernel fit to the contour of our sampling (b). From this kernel, we integrate the distribution outside the contour of constant density that goes through the origin.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Same as Figure 14, but for PZ Tel B. Both fits include the 2018 epoch from Table 2. See discussion on this result in Section 4.1.2.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Same as Figure 14, but for HIP 65426 B.
Paper: The Orbital Eccentricities of Directly Imaged Companions Using Observable-based Priors: Implications for Population-level Distributions
Authors: Clarissa R. Do Ó, Kelly K. O'Neil, Quinn M. Konopacky, Tuan Do, Gregory D. Martinez, Jean-Baptiste Ruffio, Andrea M. Ghez
Figure caption: Same as Figure 14, but for HR 2562 B.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Steps to calculate the transmission spectra in the regions of Hα (left panels) and the He triplet (right panels). First row: sample spectrum before (in black) and after (in purple) telluric correction, in the rest frame of the observatory. Second row: telluric-corrected, normalized spectra stacked in the rest frame of the HAT-P-67 system. The horizontal dashed lines indicate the points of first and fourth contact. Third row: same as the second-row panels, but divided by the master stellar spectrum (i.e., the average post-transit spectrum). The slanted lines indicate the theoretical position of the hydrogen and helium lines in the rest frame of the exoplanet. Fourth row: same as the third-row panels, but shifted to the planetary rest frame.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Cross-correlation functions of Na I without and with the correction of the RM and CLV effects. The horizontal dashed lines indicate the points of first and fourth contact. The RM and CLV contributions are visible in the top plot as a dark slanted trace near the center of the image. This trace is also known as the Doppler shadow, and it is not visible in the lower plot.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Combined transmission spectrum in the planet’s rest frame near Hα (left) and the He triplet (right). The vertical dashed lines mark the wavelengths of the Hα and He triplet lines. For Hα we also plot a Gaussian fit. For the He triplet, we include the transmission spectrum produced by the retrieved Parker wind model.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Evolution of the stellar spectra in the metastable He triplet region during the observations. The first spectrum from the bottom is the median pre-transit spectrum, while the first spectrum from the top is the median post-transit spectrum. In the middle, with time increasing from bottom to top, we show the spectra during the transit, median-combined in groups of three for visualization purposes. The vertical dashed lines mark the location of the three components of the He triplet.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Cross-correlation and likelihood maps of the species we detected in the atmosphere of HAT-P-67b. For each species, we present the cross-correlation map (above) and likelihood map (below). The likelihood maps correspond to the value of α in our data cube that maximizes the scaled likelihood. In the likelihood maps, we also include an inset that zooms in on the region where the scaled likelihood peaks. Note that the insets do not have the same extent.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: The transmission spectrum of HAT-P-67b in the Ca II infrared triplet (IRT) lines and the effect of different degrees of ionization on the modeled line depths. The gray and black data points show the unbinned and binned spectra, respectively, average-combined in the planetary rest frame. The different colored lines show models computed at different atmospheric temperatures to simulate various levels of ionization.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Posterior distributions of the mass-loss rate, outflow temperature, and outflow line-of-sight bulk velocity assuming that the He signal in the transmission spectrum of HAT-P-67b is planetary in nature.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Transmission light curves of Hα and the He triplet in bandpasses of widths 2 Å and 3.3 Å, respectively. The dashed lines indicate the points of first and fourth contact of the white light transit, while the dotted lines mark the points of second and third contact. The horizontal line indicates the baseline, i.e., no absorption.
Paper: Transmission Spectroscopy of the Lowest-density Gas Giant: Metals and a Potential Extended Outflow in HAT-P-67b
Authors: Aaron Bello-Arufe, Heather A. Knutson, João M. Mendonça, Michael M. Zhang, Samuel H. C. Cabot, Alexander D. Rathcke, Ana Ulla, Shreyas Vissapragada, Lars A. Buchhave
Figure caption: Same as Figure 3, but for the species that are likely artifacts of the RM and CLV correction and not true atmospheric detections.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: A simulation of the RGB luminosity function consisting of 117,000 stars populating the magnitude range from M  I  = −2.0, to a sharp cutoff at M  I  = −4.0 mag defining the TRGB. The individual stars are assumed to be error-free in their individual magnitudes. Noise in the luminosity function is entirely due to Poisson noise in the counts in the individual bins. Output from the Sobel-Pascal-7 edge-detector is shown in the right panel. The black horizontal line, spanning the two panels, is the TRGB magnitude set to M  I  = −4.00 mag. The dashed black horizontal line in the right panel only is the mean value of the response-function. The inset “Simulated CCD Image” in the left panel shows the positions of all stars used in the simulation. Large yellow dots, circled in red and black, are all RGB stars from M  I  = −4.00 to −3.90 mag. Crowded stars brighter than the TRGB are shown as larger black filled circles. This plot is given to provide a visual impression of the self-crowding of stars near the TRGB that results in the small population of stars above the tip in this particular luminosity function.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Variations of the run of edge-detection errors with population size and with photometric errors, as a function of the GLOESS smoothing changing from here through Figure 17. The vertical axis gives the standard deviation of the measured TRGB magnitudes derived from 200 independent realizations of the luminosity function for each plotted point. The three line-linked symbols each track the decrease in the TRGB uncertainty as a function of increased sample size for a given mean error in the TRGB photometry. The horizontal axis is gives the population size normalized by the number of RGB stars in the 1 mag interval immediately below the TRGB. Filled circles have photometric errors of ±0.10 mag. Circled plus signs have ±0.05 mag errors. And circled dots represent error-free photometry. The amount of GLOESS smoothing increases by factors of 2 from this to the next two figures. Here, we show the results for a ±0.05, ±0.10, and ±0.20 mag smoothing. Ripples in these trends are not significant but due to small number statistics in the individual simulations.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: The direct dependence of the width of the Sobel Function response to variations in smoothing (comparing the left and middle panels) and insensitivity to population size (comparing the middle and right panels). The left panel shows the Sobel-filter tip-detection response to the TRGB luminosity function, which is shown rising diagonally across the top of the plot. The two thin vertical lines mark the input width (±0.025 mag) of the GLOESS smoothing function. They match the observed width of the Sobel-filter response-function. In the central panel, the GLOESS smoothing width has been doubled to ±0.050 mag, and the response-function is seen to have doubled as well. The final (right) panel shows the effect of reducing the luminosity function population by a factor of 10, keeping the GLOESS smoothing the same as the middle panel. The width of the response-function is unchanged, as shown by the predicted width based on the GLOESS smoothing.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to ±0.20 mag) at fixed, but intermediate smoothing (±0.10 mag) of a moderately large population of RGB stars (11,000). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Same as Figure 16 except this simulation involve slightly over 200,000 RGB stars in the frame. Crowding is now quite apparent in the inset CCD image, and has resulted in an appreciable population of crowded stars falling in number from the tip to 0.75 mag brighter where the self-crowding of two stars at the tip would appear as one unresolved source. For clarity, however, only one star in 10 is plotted. See text for additional details. The offset between the blue (input) line and the (red) output line in this and in the following three figures is a direct consequence of the crowding systematically producing more stars at a given magnitude by merging two or more fainter stars.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to ±0.20 mag) at fixed, but moderate smoothing (±0.10 mag) and for very large populations of RGB stars (120,000). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to ±0.20 mag) at fixed, but intermediate smoothing (±0.10 mag) of a moderately large population of RGB stars (12,000). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to 0.20 mag) at fixed, but slightly larger smoothing (0.05 mag) than previously discussed and for impoverished populations of RGB stars 124). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: Quantifying Uncertainties on the Tip of the Red Giant Branch Method
Authors: Barry F. Madore, Wendy L. Freedman, Kayla A. Owens, In Sung Jang
Figure caption: Six subpanels illustrating the effect of increasing the photometric noise, (from 0.00 to ±0.20 mag) at fixed, but intermediate smoothing (±0.10 mag) and an impoverished population of RGB stars (120). The lower portions of each of the six subpanels show the first-derivative edge-detector output in both its uncorrected (thin black lines) and its noise-weighted (thicker black line) forms. See text for a detailed discussion of the trends.
Paper: A Wide View of the Galactic Globular Cluster NGC 2808: Red Giant and Horizontal Branch Star Spatial Distributions
Authors: Christian I. Johnson, Annalisa Calamida, Justin A. Kader, Ivan Ferraro, Catherine A. Pilachowski, Giuseppe Bono, Alessandra Mastrobuono-Battisti, Armin Rest, Alfredo Zenteno, Alice Zocchi
Figure caption: The left panel shows the [Mg/Fe] vs. [Na/Fe] abundances from Carretta (2015) for NGC 2808 RGB stars. Each of the five populations identified by Carretta (2015) is shown as a different-colored filled circle. Cross-matched populations from our DECam C  ugi,DECam decomposition are shown as different-colored large open symbols. In general, we find that both studies cleanly separate primordial (P1) stars from those that formed from gas processed at higher temperatures. The C  ugi,DECam index generally finds that P3 stars have higher [Na/Fe] and lower [Mg/Fe] than those in the P2 group, but both populations are somewhat mixed. The mixing of P2 and P3 stars is likely because the Carretta (2015) sample mostly consists of bright RGB stars, where the C  ugi,DECam separation is small. Similarly, the right panel shows the same data but with the Carretta (2015) sample separated into only two groups, based on the reanalysis by Valle et al. (2022).