adsabs/AstroCLIMB huggingface dataset:
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Maps of temperature and wind structure at several pressure levels (top to bottom: lowest to highest pressure) for each of the active magnetic drag cases studied (from left to right: increasing magnetic field strength). Each plot has the substellar point centered and the thickness of the wind vectors scale with the relative wind speeds; maximum wind speeds for each map are reported in Table 2. The effects of our active magnetic drag are most readily seen at the lowest pressure, where all the magnetic cases show exclusively north–south flow at the dayside, as the magnetic treatment has removed all the east–west momentum. As the pressure increases, the magnetic drag timescale also increases, reducing its effects. The stronger magnetic fields will have the shortest timescales and thus have the effects of magnetic drag present in deeper levels.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Left: orbital phase curves of total (bolometric) thermal emission for all of the models in this paper. Right: amplitude (﹩({F}_{\max }-{F}_{\min })/{F}_{\max }﹩) and phase of peak flux for each model’s phase curve. For our active magnetic drag cases, increasing the magnetic field strength increases the amplitude of the phase curve and causes it to peak closer to a phase of 0.5 (which corresponds to secondary eclipse; not shown). This reflects the diminished day–night heat transport and disrupted eastward flow, due to the influence of the drag. The uniform drag timescale cases show similar behavior. The longer uniform drag timescale case has a similar hot spot shift to our nondragged, 0 G case but an amplitude more similar to that of our 0.3 G model, while the shorter drag timescale has a phase curve amplitude and peak between the 0.3 and 3 G active drag models.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Temperature–pressure profiles for the nonmagnetic model of WASP-76b. The rainbow lines show equatorial profiles (with longitude measured from the substellar point) while the gray curves show profiles from the entire planet. From right to left, the black dotted curves show the substellar, global, and nightside averaged analytic 1D profiles calculated via Guillot (2010). The dashed black line is the profile used to initialize conditions globally. A strong temperature inversion is present on the day side, which becomes nonexistent on the unilluminated nightside. The large day-to-night temperature contrast is also apparent.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Maps of temperature and wind structure at the same pressure levels as Figure 3 but comparing the uniform drag timescale cases. The differences in atmospheric circulation caused by the uniform drag timescale are more subtle but most prominently seen in the weakening of the equatorial jet in the lower atmosphere.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Longitudinally averaged east–west winds for the active magnetic drag cases studied. The black contours denote the boundary between positive (eastward) and negative (westward) winds. When the active magnetic drag is incorporated, the jet strength immediately diminishes, especially in the upper atmosphere. The stronger the magnetic field strength, the weaker the jet and the more it is constrained only to deeper pressure levels.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Longitudinally averaged east–west wind speeds for the uniform drag timescale cases. The 107 s uniform drag timescale weakens the equatorial jet, while the shorter, 104 s timescale completely eliminates it. In this shorter timescale we see instead weaker, higher latitude jets, which also begin to appear in the 107 s drag timescale case.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Temperature–pressure profiles for different uniform drag timescales. The shortest uniform drag timescale case (104 s) exhibits a stronger thermal inversion ending deeper in the atmosphere and a more uniform lower atmosphere compared to the drag-free case. Additionally, the shortest uniform drag timescale causes cooler nightside temperatures, as it diminishes all of the temperature-homogenizing flow instead of just the east–west component, as in our active magnetic drag models. The 107 s uniform drag timescale case shows these same effects but to a lesser degree.
Paper: Exploring the Effects of Active Magnetic Drag in a General Circulation Model of the Ultrahot Jupiter WASP-76b
Authors: Hayley Beltz, Emily Rauscher, Michael T. Roman, Abigail Guilliat
Figure caption: Temperature–pressure profiles for our active magnetic models. The rainbow hues denote equatorial profiles, while the gray curves show profiles for the rest of the planet. All models show strong day–night contrasts and temperature inversions at the daysides. The increase in the magnetic field strength causes the nightside temperatures to decrease and the lower atmospheres to become more uniform.
Paper: Near-infrared Spectra of the Inflated Post-common Envelope Brown Dwarf NLTT 5306 B
Authors: Cam Buzard, Sarah L. Casewell, Joshua D. Lothringer, Geoffrey A. Blake
Figure caption: Our NIRSPEC data are shown in blue (2019 October 17) and green (2020 January 7 and 2021 January 7), shifted into the brown dwarf reference frame, assuming a K BD of 333 km s−1. In gray are the sky emission spectra from each epoch of data shifted in the same way as the data and vertically offset for clarity. The pink shading represents the positions of two K i doublets. Notably, the K i doublet at ∼1.16–1.17 μm is present in our data. The longer-wavelength doublet coincides with a noisier portion of our spectrum.
Paper: Near-infrared Spectra of the Inflated Post-common Envelope Brown Dwarf NLTT 5306 B
Authors: Cam Buzard, Sarah L. Casewell, Joshua D. Lothringer, Geoffrey A. Blake
Figure caption: Normalized log likelihood functions generated from the cross-correlation between the conditional best-fitting model from each model grid to our data, shown with jackknife error bars. The three panels represent fits to the full data set, to only the dayside epochs, and to only the nightside epochs, respectively. The blue curves come from Sonora 2021 models and the green curves are from irradiated models. The vertical white range shows the prior on K BD given that the companion is a brown dwarf and does not show even a grazing transit.
Paper: Near-infrared Spectra of the Inflated Post-common Envelope Brown Dwarf NLTT 5306 B
Authors: Cam Buzard, Sarah L. Casewell, Joshua D. Lothringer, Geoffrey A. Blake
Figure caption: Comparison between the Sonora (solid curves) and irradiated (dashed curves) model fits to our data. The orange curves were fit to dayside epochs and the purple curves were fit to nightside epochs. Since the two model grids cover very different ranges of effective temperatures and surface gravities, we plot their normalized probability distribution functions on separate y-axes.
Paper: Near-infrared Spectra of the Inflated Post-common Envelope Brown Dwarf NLTT 5306 B
Authors: Cam Buzard, Sarah L. Casewell, Joshua D. Lothringer, Geoffrey A. Blake
Figure caption: Same as Figure 4, but showing the results of the irradiated model fits. We convert the model internal temperature and heat-redistribution parameters to brown dwarf effective temperatures using Equation (1) and assuming a Bond albedo of 0.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Comparison of faculae in condensed plage areas, faculae in the diffuse network, and sunspot filling factors (%) with ΔRV model (m s−1) and ﹩| {\hat{B}}_{\mathrm{obs},\mathrm{active}}| ﹩, the unsigned flux due to active regions. We see the strongest correlation between f plage and ﹩| {\hat{B}}_{\mathrm{obs},\mathrm{active}}| ﹩ (Spearman coefficient of 0.97) and note the low percentage of sunspots on the surface as this time span falls during the minimum of Solar Cycle 25. The maximum sunspot percentage on the solar surface in this time span is 0.026% while faculae cover between 1.5%–3% of the solar disk.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Comparison of NEID RVs derived using different cross-correlation line masks (columns) compared to the SDO unsigned magnetic flux (top row) and the bulk NEID RVs (bottom row). Spearman correlation coefficients are shown in the upper left in each panel along with fitted slope values and uncertainties on both measurements. First column: Correlations for RVs calculated using activity-sensitive lines from Wise et al. (2018). There is moderately strong correlation with the NEID pipeline RVs, but only a weak correlation with unsigned magnetic flux. Second column: Correlation for RVs derived using only Fe I lines in the ESPRESSO G2 mask, showing stronger correlation with both the bulk NEID RVs and the unsigned magnetic flux. Third column: Correlation for RVs derived using deep lines in the ESPRESSO G2 mask, showing very strong correlation with the bulk RVs. Fourth column: Correlation for RVs derived using shallow lines in the ESPRESSO G2 mask, showing the strongest correlation with the bulk RVs. All error bars report only photon noise, excluding any instrument systematics or additional stellar jitter (5–7 cm s−1).
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Corner plot showing correlation between space-based magnetic observables and ground-based CCF metrics derived using the full G2 ESPRESSO mask. The time frame for the data set shown is 2020 December–2021 May. Days of high activity (measurable spots) are shown as dark pink dots, and days of quiet Sun are shown in light lavender. The correlation coefficients are listed in the legends in each subplot for the full data set (top value), high-activity days (middle), and quiet-Sun days (bottom), respectively. When comparing the space- and ground-based data, we see the strongest correlation between unsigned magnetic flux, ﹩| {\hat{B}}_{\mathrm{obs}}| ﹩, and the CCF amplitude and integrated area measurements.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Ground-based RV measurements before and after correction for correlation with unsigned magnetic flux. For these “good” weather days, where there was low measurable cloud cover at WIYN, the correlation between RV NEID and ﹩| {\hat{B}}_{\mathrm{obs}}| ﹩ is 0.43, while the correlation between ΔRV model and RV NEID is 0.29, and the average daily binned error for RV NEID is ∼5 cm s−1. We linearly fit the correlation between ﹩| {\hat{B}}_{\mathrm{obs}}| ﹩ and RV NEID and subtract this from RV NEID to reduce the scatter due to the unsigned magnetic flux. The scatter reduces from ∼80 cm s−1 to ∼65 cm s−1 after this correction.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Calculated RVs for the eight-hour Mercury transit on 2016 May 9. Top panel: Unsigned magnetic flux over the period of the transit. Second panel: Photometric velocity from the 2016 transit. Third panel: Convective velocity from the 2016 transit. Fourth panel: Model RV variation constructed from SDO/HMI data during the planet’s transit. For all panels, the red diamonds show the 5.25 minute binned RVs (averaging out p-mode oscillations) while the background gray signal shows the 2 minute cadence of our calculations. Even with the low level of underlying surface activity, the integrated RV model is dominated by ﹩{\rm{\Delta }}{\hat{v}}_{\mathrm{conv}}﹩.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Calculated RVs for the Venus transit from 2012 June 5 to June 6. Top panel: Unsigned magnetic flux over the period of the transit. Second panel: Photometric velocity component from the Venus 2012 transit. Third panel: Convective velocity component from the Venus 2012 transit. Fourth panel: Model RV variation constructed from SDO/HMI data during the planet’s transit using the SolAster pipeline as outlined in Section 2.7. For all panels, the red diamonds show the 5.25 minute binned RVs (averaging out p-mode oscillations) while the background gray signal shows the 2 minute cadence of our calculations.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Calculated RVs and magnetic observables for the Mercury transit on 2019 November 11. Top panel: Unsigned magnetic flux over the period of the transit showing the low level of activity during this time period. Second panel: Photometric velocity component from the Mercury 2019 transit. Third panel: Convective velocity component from the Mercury 2019 transit. Fourth panel: Model RV variation constructed from SDO/HMI data during the planet’s transit using the SolAster pipeline as outlined in Section 2.7. For all panels, the red diamonds show the 5.25 minute binned RVs (averaging out p-mode oscillations) while the background gray signal shows the 2 minute cadence of our calculations.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Comparison of the two CCF metrics (amplitude and integrated area) that correlate most strongly with unsigned magnetic flux as a function of the line depth (deep having CCF mask weights ≥0.5, and shallow having weights <0.5). We show a comparison of the percent change of these metrics against the unsigned magnetic flux. The correlation coefficients are listed in the legend for the full data set (top value), high-activity days (middle), and quiet-Sun days (bottom), respectively. The dark red dots show days of high activity, while the light lavender points are quiet-Sun days.
Paper: Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
Authors: Tamar Ervin, Samuel Halverson, Abigail Burrows, Neil Murphy, Arpita Roy, Raphaelle D. Haywood, Federica Rescigno, Chad F. Bender, Andrea S. J. Lin, Jennifer Burt, Suvrath Mahadevan
Figure caption: Periodogram showing the contributions due to the small active regions (green) and large active regions (blue) in comparison with model (red) and ground-based RV measurements (black). The small active regions do not contribute to power on rotation timescales, supporting results of Milbourne et al. (2019), while large active regions (spots/plage) show measurable power at periodicities near P rot.
Paper: Precise Dynamical Masses of ε Indi Ba and Bb: Evidence of Slowed Cooling at the L/T Transition
Authors: Minghan Chen, Yiting Li, Timothy D. Brandt, Trent J. Dupuy, Cátia V. Cardoso, Mark J. McCaughrean
Figure caption: Age posterior of ε Indi A based on the Bayesian activity-age method of Brandt et al. (2014). Our analysis does not use a directly measured rotation period for ε Indi. The median and 1σ uncertainties are shown by the gray dotted lines; they correspond to ﹩{3.48}_{-1.03}^{+0.78}﹩ Gyr.
Paper: Precise Dynamical Masses of ε Indi Ba and Bb: Evidence of Slowed Cooling at the L/T Transition
Authors: Minghan Chen, Yiting Li, Timothy D. Brandt, Trent J. Dupuy, Cátia V. Cardoso, Mark J. McCaughrean
Figure caption: Pixel scale and PA zero-point calibrations for the NACO S13 camera, derived using the binary HD 208371/2 as measured by Gaia EDR3.
Paper: Precise Dynamical Masses of ε Indi Ba and Bb: Evidence of Slowed Cooling at the L/T Transition
Authors: Minghan Chen, Yiting Li, Timothy D. Brandt, Trent J. Dupuy, Cátia V. Cardoso, Mark J. McCaughrean
Figure caption: Residual altitude separation of ε Indi Ba and Bb in each band compared to the mean of all bands, before (top panel) and after (bottom panel) applying a correction for differential atmospheric refraction.
Paper: Predicting the Exoplanet Yield of the TESS Prime and Extended Missions through Years 1–7
Authors: Michelle Kunimoto, Joshua Winn, George R. Ricker, Roland K. Vanderspek
Figure caption: The actual TESS Prime Mission TOI yield (blue) compared with our predictions (black), as a function of spectral type (top left), planet radius (top right), orbital period (bottom left), and TESS magnitude (bottom right). Analogous to Figure 6, we only include TOIs with R  p  < 4 R ⊕ in the magnitude plot. The actual yield consists of TOIs from the Prime Mission TOI catalog (Guerrero et al. 2021) that are not false positive or false alarms, orbit stars in our stellar sample, and have orbital periods and radii within our simulated ranges. The predicted yield shows the mean and standard deviation of our simulations, with FFI detections filtered by S/N > 9 and T < 10.5 mag to simulate the detection criteria of the QLP. Our simulations are able to closely reproduce the TOI yield across spectral type, planet radius, orbital period, TESS magnitude, and overall number of planets.
Paper: Predicting the Exoplanet Yield of the TESS Prime and Extended Missions through Years 1–7
Authors: Michelle Kunimoto, Joshua Winn, George R. Ricker, Roland K. Vanderspek
Figure caption: The results of our simulations having used the Fressin et al. (2013) occurrence rate for AFGK stars and an S/N ≥ 7.3 detection threshold, both of which have been used by all previous works (with the exception of Ballard 2019, who only simulated M dwarfs). Plots of the period−radius distribution after each TESS mission stage for a single simulation are shown on the left, analogous to Figure 5, while a comparison with the actual TESS Prime Mission TOI yield (purple) is shown on the right, analogous to Figure 12. The histograms show the mean and standard deviation from 10 simulations. These simulations do not reproduce the distribution of planets by spectral type; significantly over- and underestimate yields for sub-Neptunes and giants, respectively; and tend to predict planets at higher orbital periods.
Paper: Predicting the Exoplanet Yield of the TESS Prime and Extended Missions through Years 1–7
Authors: Michelle Kunimoto, Joshua Winn, George R. Ricker, Roland K. Vanderspek
Figure caption: Planets in and near the optimistic (blue shaded region) and conservative habitable zones (green), as defined by Kopparapu et al. (2013), for one simulation. New planets discovered in the PM, EM1, and EM2 are plotted in blue, orange, and red, respectively. Small planets with R  p  < 2 R ⊕ are denoted with a star.
Paper: Predicting the Exoplanet Yield of the TESS Prime and Extended Missions through Years 1–7
Authors: Michelle Kunimoto, Joshua Winn, George R. Ricker, Roland K. Vanderspek
Figure caption: Number of new planets detected in EM2 as a function of planet radius (top), orbital period (middle), and host star spectral type (bottom), from FFI observations only (left) or 2-minute observations only (right). In EM2, 4007 ± 178 (97.9%) new planets will be unique to the FFIs and only 28 ± 5 (0.68%; mostly small planets around M dwarfs) will be unique to 2-minute observations.
Paper: Predicting the Exoplanet Yield of the TESS Prime and Extended Missions through Years 1–7
Authors: Michelle Kunimoto, Joshua Winn, George R. Ricker, Roland K. Vanderspek
Figure caption: New EM2 planets with R  p  < 4 R ⊕ for one simulation, plotted by RV semiamplitude and host star V magnitude. Only planets orbiting stars with V < 14 mag are shown here. Small planets with R  p  < 2 R ⊕ are denoted with a star.
Paper: The η Aquilae System: Radial Velocities and Astrometry in Search of η Aql B
Authors: G. Fritz Benedict, Thomas G. Barnes, Nancy R. Evans, William D. Cochran, Richard I. Anderson, Barbara E. McArthur, Thomas E. Harrison
Figure caption: The K-band LL of Benedict et al. (2007) () with the results of Step 3 () in Table 12 and Step 4 () in Table 13 for η Aql and ζ Gem now included at log P values of 0.8559 and 1.0065, respectively. M  K  values derived from Gaia EDR3 parallaxes are denoted (∗). The slope and intercept of the linear fit are those previously reported. For ζ Gem a model (Step 3) with no EDR3 priors produces a parallax and M  K  agreeing with the LL. Exactly the same model with no EDR3 priors applied to η Aql yields a highly discrepant M  K , with a residual (ΔM  K  = +1.06) falling outside the range of the residual plot. Including (Step 4) Gaia EDR3 priors for η Aql parallax and proper motion yields agreement with the LL (Table 13).
Paper: The η Aquilae System: Radial Velocities and Astrometry in Search of η Aql B
Authors: G. Fritz Benedict, Thomas G. Barnes, Nancy R. Evans, William D. Cochran, Richard I. Anderson, Barbara E. McArthur, Thomas E. Harrison
Figure caption:  η Aql results from Step 3. Right: η Aql position residuals plotted against TJD, re-determining coefficients A–F in Equations (4)–(7) while solving for η Aql parallax and proper motion. Compared to Figure 8, allowing η Aql to assist in determining A–F has significantly reduced the η Aql rms residuals from (x, y) = (1.8, 1.6 mas) to (0.8, 1.0 mas). Left: reference star residuals. Compared to Figure 7, including the η Aql measurements has increased the reference star residuals rms from (x, y) = (1.1, 0.8 mas) to (1.4, 1.2 mas). These model inputs yield an η Aql parallax, ϖ = 6.55 ± 0.25 mas, which significantly differs from the Gaia EDR3 value, ϖ = 3.67 ± 0.19 mas.