adsabs/AstroCLIMB huggingface dataset:
Paper: The James Webb Space Telescope Absolute Flux Calibration. II. Mid-infrared Instrument Imaging and Coronagraphy
Authors: Karl D. Gordon, G. C. Sloan, Macarena Garcia Marin, Mattia Libralato, George Rieke, Jonathan A. Aguilar, Ralph Bohlin, Misty Cracraft, Marjorie Decleir, Andras Gaspar, Sarah Kendrew, David R. Law, Alberto Noriega-Crespo, Michael Regan
Figure caption: Same as Figure 10 except plotted vs. subarray.
Paper: The James Webb Space Telescope Absolute Flux Calibration. II. Mid-infrared Instrument Imaging and Coronagraphy
Authors: Karl D. Gordon, G. C. Sloan, Macarena Garcia Marin, Mattia Libralato, George Rieke, Jonathan A. Aguilar, Ralph Bohlin, Misty Cracraft, Marjorie Decleir, Andras Gaspar, Sarah Kendrew, David R. Law, Alberto Noriega-Crespo, Michael Regan
Figure caption: Same as Figure 10 except plotted vs. integration time.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Violin plot for the physical properties of COCONUTS-2b determined from thermal evolution models (the left-hand column; Section 5.4.2) and various atmospheric models using the Gemini/F2 spectrum and W1/W2[3.6]/[4.5] photometry (the right-hand column; Section 4). The evolution-based parameter posteriors are obtained by concatenating the results from several hot-start and cold-start evolution models (Table 4). The plotted atmospheric models are ordered by their corresponding BIC values (low to high from left to right). The spectroscopically inferred masses of the following atmospheric models are between 20 and 70 M Jup, and thus outside the y-axis range: ATMO2020 NEQ strong, ATMO2020 NEQ weak, ATMO2020 CEQ, and Sonora Bobcat.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: The Exo-REM R ∼ 500 model spectra computed with cloud-free (blue) and cloudy (red) atmospheres. The left-hand column shows spectra with T eff = 500 K and varying ﹩\mathrm{log}(g);﹩ the integrated fluxes of these spectra over 0.34–250 μm are consistent within 0.004 dex. The right-hand column shows spectra with ﹩\mathrm{log}(g)=5.0﹩ dex and varying T eff. All spectra correspond to [M/H] = 0 dex and C/O = 0.4, consistent with the inferred values for COCONUTS-2b based on Exo-REM (Table 1).
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Comparison of the Gemini/F2 spectrum of COCONUTS-2b (blue) with the archival Magellan/FIRE spectrum (black) across the Y, J, H, and K bands, with 1σ flux uncertainties highlighted by colored shades. Both spectra are flux-calibrated using the same J-band magnitude (see Section 2). The maximum-likelihood PH  3  -free ATMO2020++ model spectrum (orange), as shown in Figure 4, is overlaid for comparison. The Magellan/FIRE spectrum exhibits an absorption feature in the H band (marked by an arrow symbol), which is absent in the Gemini/F2 spectrum with a higher S/N and spectral resolution.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Top panels: χ 2-based spectral fitting results using the LB23 Cloudy CEQ grid. The top left-hand panel presents the observed Gemini/F2 spectrum of COCONUTS-2b (blue) overlaid with the best-fit model spectrum (orange). The best-fitted physical parameters are labeled. The gray-shaded regions indicate the masked wavelength regions. The bottom left-hand panel presents the data-model residuals. The right-hand panel presents our computed χ 2 for a given set of grid parameters, including T eff, ﹩\mathrm{log}(g)﹩, [M/H], and cloud properties (AEE10 or E10). Bottom panels: Spectral fits with the LB23 Cloudy NEQ grid, with the same format as the top row.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Forward-modeling results based on models that include equilibrium chemistry and no clouds, with the same format as Figure 4.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Forward-modeling results based on models that include equilibrium chemistry and clouds, with the same format as Figure 4.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Posterior distributions of COCONUTS-2b’s spectroscopically inferred parameters using the PH  3  -free ATMO2020++ models. The results derived with the Y-band spectral fluxes included and excluded are shown in orange and gray, respectively. The 1σ and 2σ confidence intervals are highlighted.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Forward-modeling results based on models that include disequilibrium chemistry and no clouds, with the same format as Figure 4.
Paper: Disequilibrium Chemistry, Diabatic Thermal Structure, and Clouds in the Atmosphere of COCONUTS-2b
Authors: Zhoujian Zhang, 周健 张, Sagnick Mukherjee, Michael C. Liu, Jonathan J. Fortney, Emily Mader, William M. J. Best, Trent J. Dupuy, Sandy K. Leggett, Theodora Karalidi, Michael R. Line, Mark S. Marley, Caroline V. Morley, Mark W. Phillips, Robert J. Siverd, Joseph A. Zalesky
Figure caption: Forward-modeling results based on the Exo-REM models that include disequilibrium chemistry and clouds, with the same format as Figure 4.
Paper: Long-term Dynamical Stability in the Outer Solar System. II. Detailed Secular Evolution of Four Large Regular and Resonant Trans-Neptunian Objects
Authors: Marco A. Muñoz-Gutiérrez, Antonio Peimbert, Angeles Pérez-Villegas
Figure caption: Evolution of the main orbital parameters of 2010 EK139's 200 realizations. Similar to Figure 1, but with two highlighted realizations, in green and yellow for a, e, and i, to exemplify the variety of behaviors. The dashed red line in the top panel represents the nominal location of the 7:2 MMR.
Paper: Long-term Dynamical Stability in the Outer Solar System. II. Detailed Secular Evolution of Four Large Regular and Resonant Trans-Neptunian Objects
Authors: Marco A. Muñoz-Gutiérrez, Antonio Peimbert, Angeles Pérez-Villegas
Figure caption: Similar to Figure 2 but for 2010 EK139's angular elements and the Kozai constant, as well as the resonant argument of the 7:2 MMR, ϕ 7:2, presented in the top panel. As in Figure 10 we highlight two orbits, in green and yellow, to exemplify the variety of orbital behaviors.
Paper: Long-term Dynamical Stability in the Outer Solar System. II. Detailed Secular Evolution of Four Large Regular and Resonant Trans-Neptunian Objects
Authors: Marco A. Muñoz-Gutiérrez, Antonio Peimbert, Angeles Pérez-Villegas
Figure caption: Same as Figure 3 but for 2010 EK139's orbital density. In the bottom panel, the blue line shows the stability limit for large a objects determined by K. Batygin et al. (2021); the symbols show the location of Neptune and Uranus in the q vs. a plane.
Paper: CRIRES+ and ESPRESSO Reveal an Atmosphere Enriched in Volatiles Relative to Refractories on the Ultrahot Jupiter WASP-121b
Authors: Stefan Pelletier, Björn Benneke, Yayaati Chachan, Luc Bazinet, Romain Allart, H. Jens Hoeijmakers, Alexis Lavail, Bibiana Prinoth, Louis-Philippe Coulombe, Joshua D. Lothringer, Vivien Parmentier, Peter Smith, Nicholas Borsato, Brian Thorsbro
Figure caption: Example ESPRESSO and CRIRES+ exposures compared to cross sections of important refractory, volatile, and continuum species. The top panels show the signal-to-noise per pixel of a typical ESPRESSO (left) and CRIRES+ (right) exposure, with the regions of the data that are masked out during the analysis shown in gray. The bottom panel shows weighted cross sections of refractory elements Fe and Ni, volatile molecules H2O, CO, OH, and SiO, and continuum opacity sources ﹩{{\rm{H}}}_{\mathrm{bf}}^{-}﹩ and ﹩{{\rm{H}}}_{\mathrm{ff}}^{-}﹩ computed at a temperature of 2900 K. The black dashed lines show the wavelength bounds of the ESPRESSO (0.38–0.78 μm) and CRIRES+ K2166 (1.92–2.48 μm) data used in work. ESPRESSO is well suited for probing Fe and Ni, while CRIRES+ is sensitive to CO and H2O. However, CRIRES+ in the K-band mode is not particularly sensitive to other important O-bearing molecules such as OH or SiO. Combining observations in the optical and near-infrared can be a powerful method to constrain volatile-to-refractory abundance ratios.
Paper: CRIRES+ and ESPRESSO Reveal an Atmosphere Enriched in Volatiles Relative to Refractories on the Ultrahot Jupiter WASP-121b
Authors: Stefan Pelletier, Björn Benneke, Yayaati Chachan, Luc Bazinet, Romain Allart, H. Jens Hoeijmakers, Alexis Lavail, Bibiana Prinoth, Louis-Philippe Coulombe, Joshua D. Lothringer, Vivien Parmentier, Peter Smith, Nicholas Borsato, Brian Thorsbro
Figure caption: Orbital signature of WASP-121b as seen from ESPRESSO and CRIRES+. The panels show the normalized cross correlation as a function of phase for ESPRESSO (left) and CRIRES+ (right). Here, the signal from the thermal light of the dayside atmosphere of WASP-121b can be seen as a white streak closely following the expected orbital motion (dotted black line). The shaded gray area marks the secondary eclipse, when data collection is avoided as WASP-121b passes behind its host star and is no longer visible. The ESPRESSO observations, comprising eight visits totaling 28 hr, cover a greater portion of the orbit than the 5.6 hr of observations split over two epochs using CRIRES+. The side panels denote the average signal-to-noise per wavelength at each observed phase. For ESPRESSO, the observed planetary trace naturally appears stronger at orbital phases of higher SNR that are observed more than once, and appears weaker at phases of lower SNR (covered by only a single visit) or close to quadrature when the planetary acceleration is minimal. To avoid any contamination of the signal, residuals near the systemic velocity (around 38 km s−1) due to the host star also having Fe in its atmosphere are masked. For CRIRES+, the posteclipse signal appears slightly weaker than the preeclipse signal, but this may be a results of the lower SNR of those data due to worse weather conditions.
Paper: CRIRES+ and ESPRESSO Reveal an Atmosphere Enriched in Volatiles Relative to Refractories on the Ultrahot Jupiter WASP-121b
Authors: Stefan Pelletier, Björn Benneke, Yayaati Chachan, Luc Bazinet, Romain Allart, H. Jens Hoeijmakers, Alexis Lavail, Bibiana Prinoth, Louis-Philippe Coulombe, Joshua D. Lothringer, Vivien Parmentier, Peter Smith, Nicholas Borsato, Brian Thorsbro
Figure caption: Cross correlation detections of Fe, Ni, CO, and H2O in the dayside atmosphere of WASP-121b. Here, each panel depicts the two-dimensional cross correlation signal-to-noise map of a species relative to the expected K  p  and V sys (dashed white lines). Clear detections of Fe, Ni, CO, and H2O can be seen as white blobs near the expected orbital and systemic velocities of WASP-121b. The atomic metal (Fe and Ni) detections are driven by the ESPRESSO data at optical wavelengths while the molecule (CO and H2O) detections are primarily from the CRIRES+ data in the near-infrared. The “X” shape of the detections is a result of observations having been obtained both pre- and posteclipse, with the top left to bottom right diagonal being from the preeclipse data and the bottom left to top right diagonal being from the posteclipse data. While the Fe, Ni, and CO signals are well aligned with each other, H2O is slightly blueshifted in comparison, potentially because of a combination of winds and a molecular dissociation-driven asymmetric distribution of water in the dayside atmosphere of WASP-121b.
Paper: CRIRES+ and ESPRESSO Reveal an Atmosphere Enriched in Volatiles Relative to Refractories on the Ultrahot Jupiter WASP-121b
Authors: Stefan Pelletier, Björn Benneke, Yayaati Chachan, Luc Bazinet, Romain Allart, H. Jens Hoeijmakers, Alexis Lavail, Bibiana Prinoth, Louis-Philippe Coulombe, Joshua D. Lothringer, Vivien Parmentier, Peter Smith, Nicholas Borsato, Brian Thorsbro
Figure caption: Data detrending procedure for example spectral orders from the ESPRESSO (left) and CRIRES+ (right) data sets. The top left panel shows a segment of the extracted ESPRESSO spectral time series on 2021 January 11. Here, darker vertical lines depict telluric and stellar absorption lines while horizontal stripes are due to variations in throughput, for example due to passing thin clouds. The bottom left panel shows the data after the detrending steps (see Section 3) are applied to remove unwanted flux contributions from everything other than WASP-121b. The right panels shows the equivalent but for the CRIRES+ time series of 'A' nodding positions of 2022 February 13. White regions denote wavelengths masked due to strong telluric absorption. The random noise appearance and lack of any distinguishable patterns in the data residuals indicates that the PCA (with three principal components removed in this case) efficiently removes both stellar and telluric contributions. The bottom panels (“After detrending”) are single-order examples of the data product used for the cross correlation and retrieval analyses.
Paper: CRIRES+ and ESPRESSO Reveal an Atmosphere Enriched in Volatiles Relative to Refractories on the Ultrahot Jupiter WASP-121b
Authors: Stefan Pelletier, Björn Benneke, Yayaati Chachan, Luc Bazinet, Romain Allart, H. Jens Hoeijmakers, Alexis Lavail, Bibiana Prinoth, Louis-Philippe Coulombe, Joshua D. Lothringer, Vivien Parmentier, Peter Smith, Nicholas Borsato, Brian Thorsbro
Figure caption: Recovered cross correlation S/N as a function of number of principal components removed in the data detrending procedure. The black line shows the real signal from the atmosphere of WASP-121b observed with ESPRESSO (top) and CRIRES+ (bottom), while the blue lines correspond to recovered models injected at random combinations of K  p  and V sys away from the expected orbital position. The atmosphere of WASP-121b is strongly detected irrespective of the number of components removed, and behaves similarly to injected models at different orbital configurations. Nevertheless, removing at least a few principal components to correct for telluric/stellar residuals significantly helps to reveal any underlying signal in the data. However, removing too many can cause the PCA to ”eat away” at the planetary signal and do more harm than good. For the main retrieval analysis in this work, we remove three principal components from each data set, which best recovers both the real signals, and injected signals at surrounding orbital positions.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Full planet-like RV test cases used for evaluating the neural network’s ability to recover planet-like RVs. Purple points correspond to planets testing shorter- and longer-period orbits (keeping amplitude fixed at 1 m s−1), while red points correspond to planets testing for amplitude (keeping period fixed at 50 days).
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Performance results for the 20 planet test cases. The left column corresponds to the amplitude recovery test cases and the right column corresponds to the period recovery test cases. Purple crosses correspond to the multiline CNN (250 Top) RVs and the red circles correspond to the multiline CNN (250 Pruned) RVs. While both networks performed similarly on the period recovery test cases, the multiline CNN (250 Pruned) outperformed the multiline CNN (250 Top) on all amplitude test cases in terms of absolute error (middle left) and percent error (bottom left).
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Performance results for the 20 planet test cases using HARPS-N RVs in the heliocentric rest frame. The left column corresponds to the amplitude recovery test cases, and the right column corresponds to the period recovery test cases. This figure may be compared with Figure 8 to assess how much our approach improved planet detectability. We see a substantial reduction in error in recovered planet parameters in our multiline CNN approach when compared with these results, where we apply the same planet-fitting method to “uncorrected” RVs.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Two sets of results for the planet-like test cases. The left column corresponds to a planet with a 50 day period and 0.2 m s−1 semiamplitude, while the right column corresponds to a planet with a 250 day period and 1.0 m s−1 semiamplitude. The top row depicts power and frequency for the Lomb–Scargle periodograms fit to each planet-like test case. The red dotted line is the true period, while the red cross indicates the frequency of the peak power. The center row shows phase-folded diagrams, while the bottom row shows the time series plots, both produced following the MLE. Dark points in the phase-folded diagrams and time series plots are the predicted RVs made by the neural network—predicted RVs for the 1.0 m s−1 semiamplitude planet show distinct sinusoidal characteristics.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: A series of error analysis plots used to assess systemic issues associated with a trained neural network. Predicted RVs and the associated errors are from the multiline CNN (250 Top). The displayed plots are from test data; however, in the development of the neural network, the training and validation splits were referenced for informing necessary changes to preprocessing steps or network architecture design; test data are shown only for reporting in this context.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Distributions of coefficients for the fitted OLS model using the single-line CNNs’ predictions as inputs. The top distribution corresponds to the coefficients of the OLS model fit using all 4570 single-line CNNs’ predictions. The subsequent four distributions of fitted coefficients are results from multiple iterations of pruning, where any input with a coefficient approximately close to zero (<1 × 10−4) is removed.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Distributions of predicted RVs from the 4570 single-line CNNs for a sample of the injected RVs. The distributions for each set of predictions is shown as a boxplot—red vertical line is the median, box is the IQR, whiskers are [Q1 − 1.5 * IQR, Q3 + 1.5 * IQR], and the black points are any predictions outside the whiskers. The vertical green lines correspond to the true RV values. The distribution for each set of predicted RVs is centered around the true RV (green vertical line).
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: Line depth vs. line wavelength plots for the 4570 spectral lines used throughout this analysis. The left panel shows the 1000 lines, colored in red, resulting from pruning in Section 5.2, while the center panel shows the 250 lines resulting from further pruning. The right panel shows the 250 spectral lines with the lowest test RMSE from the single-line CNNs. The multiline CNN was trained once using the lines in the center panel and a separate time using the lines in the left panel.
Paper: Deep Learning–based Measurement of Planetary Radial Velocities in the Presence of Stellar Variability
Authors: Ian Colwell, Virisha Timmaraju, Hamsa Shwetha Venkataram, Alexander Wise
Figure caption: RMSE vs. wavelength for the center of each spectral line. The RMSE in the scatterplot is calculated on the test split. Histograms are provided for each axes to show (1) how spectral lines are distributed across wavelength given the line list we used and (2) performance distribution (RMSE) for the training, validation, and test splits.
Paper: The Impact of Observing Cadence and Undetected Companions on the Accuracy of Planet Mass Measurements from Radial Velocity Monitoring
Authors: Joseph M. Akana Murphy, Rafael Luque, Natalie M. Batalha
Figure caption: The results of our injection–recovery experiments on the synthetic data in the case of σ RV, inst = 1.2 m s−1 (i.e., the “HIRES” case for internal RV precision) and σ RV,astro = 0.4 m s−1 (i.e., the “quiet” case for stellar jitter). Each panel represents a combination of P (columns) and M p (rows) for the known planet, which is shown as the white star plotted at P = P known and K = K baseline and whose error bar (which is smaller than the size of the marker seen in some cases) represents ﹩{\sigma }_{{K}_{\mathrm{baseline}}}﹩. Filled squares represent individual injected companions that failed to trigger a detection from RVSearch. The color of each square represents the difference between K fit and K baseline for the known planet from a one-planet fit to the synthetic RVs that now contains the signal of the undetected companion. The contours represent loci of 90%, 50%, and 10% detection efficiency.