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Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets
<div>Repository: Turbulent Fluxes and Evaporation/Sublimation Rates on Earth, Mars, Titan, and Exoplanets</div> <div>Khuller & Clow (2024)</div> <div> </div> <div>Contents:</div> <div> </div> <div>1. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fitzpatrick et al. (2017)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_Fitzpatrick: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_Fitzpatrick: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled fluxes</div> <div>i) Modeled_DB_Abs_LE_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_Fitzpatrick: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_Fitzpatrick: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_Fitzpatrick: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>2. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. turbulent fluxes measured by Fairall et al. (1996); Fairall et al. (2003)</div> <div>a) Measured fluxes</div> <div>i) Measured_Abs_LE_COARE: Absolute value of measured latent heat fluxes in W/m^2</div> <div>ii) Measured_Abs_SH_COARE: Absolute value of measured sensible heat fluxes in W/m^2</div> <div>b) Modeled luxes</div> <div>i) Modeled_DB_Abs_LE_COARE: Absolute value of Dundas & Byrne (2010) modeled latent heat fluxes in W/m^2</div> <div>ii) Modeled_DB_Abs_SH_COARE: Absolute value of Dundas & Byrne (2010) modeled sensible heat fluxes in W/m^2</div> <div>iii) Modeled_KC_Abs_LE_COARE: Absolute value of Khuller & Clow modeled latent heat fluxes in W/m^2</div> <div>iv) Modeled_KC_Abs_SH_COARE: Absolute value of Khuller & Clow modeled sensible heat fluxes in W/m^2</div> <div> </div> <div>3. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rates measured by Clow et al. (1988)</div> <div>a) Measured sublimation rates</div> <div>i) Measured_dzdt_Clow: Measured sublimation rates in mm/day</div> <div> </div> <div>b) Modeled sublimation rates</div> <div>i) Modeled_DB_dzdt_Clow: Dundas & Byrne (2010) modeled sublimation rates in mm/day</div> <div>ii) Modeled_KC_dzdt_Clow: Khuller & Clow modeled sublimation rates in mm/day</div> <div> </div> <div>4. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. ice sublimation rate measured by Douglas & Mellon (2019)</div> <div>a) Roughness Lengths used</div> <div>i) z0_DM: Roughness lengths used in cm</div> <div> </div> <div>b) Modeled Sublimation Rates</div> <div>i) Modeled_DB_dzdt_u_001_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>ii) Modeled_DB_dzdt_u_005_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>iii) Modeled_DB_dzdt_u_010_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>iv) Modeled_DB_dzdt_u_015_DM: Dundas & Byrne (2010) modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div>v) Modeled_KC_dzdt_u_001_DM: Khuller & Clow modeled sublimation rates for u = 0.01 m/s in mm/day</div> <div>vi) Modeled_KC_dzdt_u_005_DM: Khuller & Clow modeled sublimation rates for u = 0.05 m/s in mm/day</div> <div>vii) Modeled_KC_dzdt_u_010_DM: Khuller & Clow modeled sublimation rates for u = 0.10 m/s in mm/day</div> <div>viii) Modeled_KC_dzdt_u_015_DM: Khuller & Clow modeled sublimation rates for u = 0.15 m/s in mm/day</div> <div> </div> <div>5. Comparison of Khuller & Clow model, Dundas & Byrne (2010) model vs. net ice sublimation inferred at Mars Phoenix landing site, by Smith et al. (2009)</div> <div>a) Parameters used</div> <div>i) z0_PHX: Roughness lengths used in cm</div> <div>ii) u_PHX: Wind speeds used in m/s</div> <div>iii) deltatheta1_PHX: Delta-surface temperatures used in K</div> <div> </div> <div>b) Modeled net sublimation</div> <div>i) Modeled_DB_deltatheta1_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied delta-surface temperatures in cm</div> <div>ii) Modeled_DB_u_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied wind speeds in cm</div> <div>iii) Modeled_DB_z0_sensitivity_PHX: Dundas & Byrne (2010) modeled sublimation rates for varied roughness lengths in cm</div> <div> </div> <div>6. Modeled vertical profiles of temperature and wind speed compared to Huygens probe measurements (Fulchignoni et al., 2005)</div> <div>i) Modeled_theta_Huygens_u_0_02: Temperature profile using 10-m wind speed of 0.02 m/s, in K</div> <div>ii) Modeled_theta_Huygens_u_1: Temperature profile using 10-m wind speed of 1 m/s, in K</div> <div>iii) Modeled_Z_Huygens: Altitude profile in m</div> <div> </div> <div>7. Modeled effect of various parameters on Mars ice sublimation rates</div> <div>a) Input data ranges</div> <div>i) Modeled_Mars_Theta2_warm: 3-m air temperatures for warm cases in K</div> <div>ii) Modeled_Mars_Theta2_cold: 3-m air temperatures for cold cases in K</div> <div>iii) Modeled_Mars_z0_range: Roughness lengths in cm</div> <div>iv) Modeled_Mars_P_range: Surface air pressure values in mbar</div> <div>v) Modeled_Mars_beta_range: Dimensionless gustiness parameters</div> <div> </div> <div>b) Modeled sublimation rates from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_DB_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_DB_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_DB_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_DB_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_DB_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>c) Modeled sublimation rates from Khuller & Clow model</div> <div>i) Modeled_KC_dzdt_Mars_warm_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_dzdt_Mars_warm_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_dzdt_Mars_cold_low_wind: Sublimation rate in cm/Mars yr using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_dzdt_Mars_cold_high_wind: Sublimation rate in cm/Mars yr using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_dzdt_Mars_warm_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vi) Modeled_KC_dzdt_Mars_cold_z0_var: Sublimation rate in cm/Mars yr using range of z0 values</div> <div>vii) Modeled_KC_dzdt_Mars_warm_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>viii) Modeled_KC_dzdt_Mars_cold_P_var: Sublimation rate in cm/Mars yr using range of P values</div> <div>xi) Modeled_KC_dzdt_Mars_warm_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div>x) Modeled_KC_dzdt_Mars_cold_beta_var: Sublimation rate in cm/Mars yr using range of beta values</div> <div> </div> <div>d) Modeled latent heat fluxes from Dundas & Byrne (2010) model</div> <div>i) Modeled_DB_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_DB_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_DB_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_DB_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_DB_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_DB_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_DB_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_DB_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_DB_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_DB_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div> </div> <div>e) Modeled latent heat fluxes from Khuller & Clow model</div> <div>i) Modeled_KC_LE_Mars_warm_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>ii) Modeled_KC_LE_Mars_warm_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>iii) Modeled_KC_LE_Mars_cold_low_wind: Latent heat flux in W/m^2 using 0.5 m/s wind speed at 3-m</div> <div>iv) Modeled_KC_LE_Mars_cold_high_wind: Latent heat flux in W/m^2 using 15 m/s wind speed at 3-m</div> <div>v) Modeled_KC_LE_Mars_warm_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vi) Modeled_KC_LE_Mars_cold_z0_var: Latent heat flux in W/m^2 using range of z0 values</div> <div>vii) Modeled_KC_LE_Mars_warm_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>viii) Modeled_KC_LE_Mars_cold_P_var: Latent heat flux in W/m^2 using range of P values</div> <div>xi) Modeled_KC_LE_Mars_warm_beta_var: Latent heat flux in W/m^2 using range of beta values</div> <div>x) Modeled_KC_LE_Mars_cold_beta_var: Latent heat flux in W/m^2 using range of beta values</div>
Data for "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback"
<p>Data supporting "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback" by Loftus*, Luo*, Fan, & Kite (2025). </p> <p>* Note, these authors contributed equally.</p>
Supporting data for: An exomoon survey of 70 cool giant exoplanets Kipping et al. (2021)
<p>In a recent research article (publisher information still to be finalised), our team conducted a survey of 70 cool giant transiting exoplanets for evidence of exomoons. To support that paper, we here include the data used to conduct that analysis. Files include the original photometric reduction used, detrending scripts and results, intermediate plots of detrended photometry, method marginalised photometry, isochrone analysis files, and posterior samples and supporting regression files from fits conducted using multimodal nested sampling.</p>
Data for blog post on dfm.io: "An experiment in open science: exoplanet population inference"
<p>The data set used be the blog post "An experiment in open science: exoplanet population inference" published at https://dfm.io/posts/exopop/</p>
In Search of the Edge: A Bayesian Exploration of the Detectability of Red Edges in Exoplanet Reflection Spectra
<p>This repository contains surface albedos for a paper submitted to AAS journals under the same title. Here we include a a realistic Earth-like surface albedo and the raw albedo files used for it's calculation.</p>
Data & model products from "Identification of carbon dioxide in an exoplanet atmosphere"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05269-w">https://www.nature.com/articles/s41586-022-05269-w</a><br> <br> OVERVIEW: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called “metallicity”), and thus formation processes of the primary atmospheres of hot gas giants. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS). The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide, and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.</p>
State of the Art in Exoplanets Observing Methods: Radial Velocity and Transit
<p>Recording of the presentation given at the Summer School</p>
Data for "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024)
<p>This archive contains the data and scripts needed to reproduce the analysis in "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024).</p> <p>Contents</p> <p>data/: Input data and posterior distributions of different model fits</p> <p>datasets/: Observational datasets</p> <p>figures/: Folder to save figures in</p> <p>fluxes/: Saved lightcurves for auxiliary plotting purposes</p> <p>archive_paper_plotter.ipynb: Example script to plot fitted eclipse maps</p> <p>eclipse_pixel_sampling.py: Script to fit eclipse map and test k-fold CV score</p> <p>paper_eclipse_suite.py: Script to use simulated or observational data to fit an eclipse map</p>
Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3
<p>Light curves and spectra of SPECULOOS-3 used in the research work presented in the Nature Astronomy article "Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3"</p>
Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite
<p>This repository contains the supplement materials for our paper :</p> <p>Jonathan H. Jiang, Xuan Ji, Nicolas Cowan, Renyu Hu, Zonghong Zhu, Empirical Predictions for the Period Distribution of Planets to be Discovered by the Transiting Exoplanet Survey Satellite, Submitted to AJ.</p> <p>Supplemental materials include:</p> <p>1.Data behind the Figures</p> <p>2.Simulation Results to estimate the error range</p> <p> </p>
Database of abstracts in publications on exoplanets from the NASA archive
<p>Collection of pre-processed data of the abstracts in publications on exoplanets, as collected by NASA. The dataframes are stored as JSON prepared for importing as Pandas dataframes.</p>
Overview exoplanets for ChatGPT and Zenodo
<p> <strong>This Excel sheet is produced based on the data from the NASA Exoplanet Archive. It is Reference [14] in Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT <span><a href="../records/12683899">https://zenodo.org/records/12683899</a> </span> </strong> <br><br>You find the calculations for the Preferred Distances of star systems in column N from cell 7 down. The largest planets are likely found at that preferred distance, based on NASA data. Open the Excel sheet and scroll down to row 195 to view the original data from NASA.<br><a href="http://exoplanetarchive.ipac.caltech.edu">http://exoplanetarchive.ipac.caltech.edu</a> </p> <p>In <em>'Seeking Evidence for the Cosmic Influx Theory (CIT)</em> <em>Unveiling a Universal Ether-like Energy Field spanning the vast scales of exoplanets down to the minute details of dew and rime)</em>', we find surprising confirmations from ChatGPT that many natural phenomena exemplify an influx of energy, converting the Big Bang (BB) concept into Continuous Creation (CC) This perspective represents a significant shift in how we view familiar phenomena—from rain to stardust, and from volcanoes to the spreading ocean floor.<br>Citation: Loeffen, R. (2024). Seeking Evidence for the Cosmic Influx Theory (CIT) Collaborating with ChatGPT<br><span><a href="../records/12683899">https://zenodo.org/records/12683899</a> </span> </p>
Additional figures: Obliquities of exoplanet host stars. Nineteen new and updated measurements, and trends in the sample of 205 measurements
<p>Here we provide some additional figures as supporting material to Knudstrup et al. (2024).</p> <p><strong>Phase-folded light curves: </strong>The best-fitting models are shown as the white line in the top panel and the residuals are given in the bottom panel.</p> <p><strong>HD 118203 b:</strong> TESS 2 min. cadence data shown as gray points with error bars. Black markers are binned data in ~10 min. intervals.</p> <p><strong>HD 149193 b:</strong> TESS light curves 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>K2-261 b:</strong> K2 light curve in black, TESS 20 sec. and 2 min. in light gray and gray, respectively. A light curve for each passband is shown.</p> <p><strong>K2-287 b:</strong> K2 30 min cadence shown in orange with CHEOPS 1 min. and ground-based 2 min. observations in light gray and gray, respectively. A light curve for each passband is also shown with a matching color. </p> <p><strong>KELT-3 b:</strong> TESS 2 min. cadence data shown in gray.</p> <p><strong>KELT-4Ab:</strong> TESS 2 min. cadence data shown in gray.</p> <p><strong>LTT 1445Ab:</strong> Black TESS 2 min. and gray TESS 20 sec.</p> <p><strong>TOI-451Ab:</strong> Unbinned 2 min. cadence data are shown in gray and in black the binned (~6 min.) data are shown.</p> <p><strong>TOI-813 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>TOI-892 b:</strong> TESS 2 min. and 30 min. cadence data are shown in gray and black, respectively.</p> <p><strong>TOI-1130 c:</strong> TESS 20 sec. and 30 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-50 b:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-59 b:</strong> TESS 2 min. cadence data shown in gray and ground-based 2 min. KeplerCam photometry in orange.</p> <p><strong>WASP-136 b:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-172 b:</strong> TESS 20 sec. and 30 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-173Ab:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-186 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>XO-7 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>WASP-148 b:</strong> The MuSCAT-2 photometry obtained simultaneous with our spectroscopic transit observations. The observations in the different <em>griz</em> filters are shown as blue, orange, green, and red markers, respectively.</p> <p><strong>WASP-26 b:</strong> Unbinned 20 sec. cadence data from TESS are shown in gray and in black the binned (~6 min.) data are shown.</p> <div> <div> <div> <p> </p> <p><strong>Peak of the stacked CCFs: </strong>Similar to Fig. A.8. we show the peaks of the stacked CCFs for KELT-4 and XO-7 created by assuming the best-fitting value of b from the RV-RM fit.</p> <p><strong>KELT-4A:</strong> The gray contours show a peak around (v sin i⋆,λ)=(6.0 km/s,90 deg). The red contours are the same as created from the posterior shown to the right in Fig. A.9.</p> <p><strong>XO-7: </strong> The gray contours show a peak around (v sin i⋆,λ)=(4 km/s,-60 deg). The red contours are the same as created from the posterior shown to the right in Fig. A.28.</p> <p> </p> </div> </div> </div> <p><strong>References:</strong></p> <div> <div> <div> <p>Knudstrup, E., Albrecht, S. H., Winn, J. N., et al., 2024, arXiv:2408.09793</p> </div> </div> </div>
Additional tables: Obliquities of exoplanet host stars. Nineteen new, updated measurements for the sample of 205 measurements and observed trends.
<p>Here we provide some additional tables as supporting material to Knudstrup et al. (2024).</p> <p>The tables are given as .tex files and can be compiled by including the additional files (.bib, .cls, etc.).</p> <p><strong>mcmc_post.tex</strong> contains a selection of the posteriors resulting from the MCMCs we carried out in Appendix A of Knudstrup et al. (2024).</p> <p><strong>literature.tex</strong> contains extensions to Tables A1 and A2 of Albrecht et al. (2022), which we have used for our analyses in Section 4 of Knudstrup et al. (2024).</p> <p><strong>dfm_post.tex</strong> contains the posteriors from the different runs presented in Section 4.1 of Knudstrup et al. (2024).</p> <p><strong>rvs.tex</strong> contains an example of the format for the radial velocities used in Knudstrup et al. (2024), which are available at CDS <a title="RVs" href="https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/690/A379" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h2>Update v3:</h2> <p>Added .csv files for the extensions to Tables A1 (<strong>planets.csv</strong>) and A2 (<strong>stars.csv</strong>) of Albrecht et al. (2022).</p> <p> </p> <p><strong>References:</strong></p> <div> <div> <div> <p>Albrecht, S. H., Dawson, R. I., & Winn, J. N. 2022, PASP, 134, 082001</p> <p>Knudstrup, E., Albrecht, S. H., Winn, J. N., et al., 2024, A&A, 690, A379</p> </div> </div> </div>
The SHERLOCK PIPEline: Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers
<p>The arrival of space missions such as Kepler/K2 and TESS has revolutionized exoplanets' detection, producing a vast amount of data that scientists need to analyze. This situation will keep increasing shortly thanks to the ESA PLATO mission, mainly devoted to finding Earth-like planets orbiting Sun-like stars, which will be launched in 2025-2026 (Rauer et al. 2014). This poster presents SHERLOCK, a robust and easy-to-use end-to-end pipeline to search for planet candidates, vet signals, perform a statistical validation, bayesian fit, and compute observational windows from ground-based facilities to trigger a follow-up campaign. All these steps are executed with just a few lines of code, which makes SHERLOCK an optimal ready-to-use code for any planetary search program.</p>
The Influence of H2O Pressure Broadening in High Metallicity Exoplanet Atmospheres: Absorption Cross-section dataset
<p>In this study, the pressure-broadened H<sub>2</sub>O absorption cross-sections (ACS) data are computed for two set of broadeners: 1) 85%H<sub>2</sub> and 15% He, and 2) 100% H<sub>2</sub>O (or 100% self-broadening) for 288 pressure-temperature grid points. Therefore, this dataset includes 576 files, and each file named based on its temperature, pressure, and broadener (H2HE or SELF).</p>
Products and Models for "Early Release Science of the Exoplanet WASP-39b with JWST NIRSpec PRISM"
<p>Associated publication: <a href="https://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41586-022-05677-y%2520&data=05%7C01%7Czafar%40jhu.edu%7C70cefcf501224b8d305708daed9f9533%7C9fa4f438b1e6473b803f86f8aedf0dec%7C0%7C0%7C638083566905963125%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gyY0O%2FyMHX0paZC60ZCiQAif%2F0O8K2QKAuuF9%2FjtRpA%3D&reserved=0">https://www.nature.com/articles/s41586-022-05677-y</a></p> <p> </p> <p>OVERVIEW: Transmission spectroscopy of exoplanets has revealed signatures of water vapor, aerosols, and alkali metals in a few dozen exoplanet atmospheres. However, these previous inferences with the Hubble and Spitzer Space Telescopes were hindered by the observations’ relatively narrow wavelength range and spectral resolving power, which precluded the unambiguous identification of other chemical species—in particular the primary carbon-bearing molecules. Here we report a broad-wavelength 0.5–5.5 µm atmospheric transmission spectrum of WASP-39 b, a 1200 K, roughly Saturn-mass, Jupiter-radius exoplanet, measured with JWST NIRSpec’s PRISM mode as part of the JWST Transiting Exoplanet Community Early Release Science Team program. We robustly detect multiple chemical species at high significance, including Na (19σ), H<sub>2</sub>O (33σ), CO<sub>2</sub> (28σ), and CO (7σ). The non-detection of CH<sub>4</sub>, combined with a strong CO<sub>2</sub> feature, favours atmospheric models with a super-solar atmospheric metallicity. An unanticipated absorption feature at 4 µm is best explained by SO<sub>2</sub> (2.7σ), which could be a tracer of atmospheric photochemistry. These observations demonstrate JWST’s sensitivity to a rich diversity of exoplanet compositions and chemical processes.</p>
Chemical Networks and Model Output and for "Evidence of Photochemistry in an Exoplanet Atmosphere"
<p>The volume mixing ratio output of the key sulphur species computed by photochemical models for producing Fig. 1 </p> <p>Synthetic spectra in Fig. 2</p> <p>The photochemical networks used in each model.</p>
Products and Models for "Early Release Science of the Exoplanet WASP-39b with JWST NIRISS"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05674-1">https://www.nature.com/articles/s41586-022-05674-1</a></p> <p> </p> <p>Transmission spectroscopy provides insight into the atmospheric properties and consequently the formation history, physics, and chemistry of transiting exoplanets. However, obtaining precise inferences of atmospheric properties from transmission spectra requires simultaneously measuring the strength and shape of multiple spectral absorption features from a wide range of chemical species. This has been challenging given the precision and wavelength coverage of previous observatories. Here, we present the transmission spectrum of the Saturn-mass exoplanet WASP-39b obtained using the SOSS mode of the NIRISS instrument on the JWST. This spectrum spans 0.6−2.8μm in wavelength and reveals multiple water absorption bands, the potassium resonance doublet, as well as signatures of clouds. The precision and broad wavelength coverage of NIRISS-SOSS allows us to break model degeneracies between cloud properties and the atmospheric composition of WASP-39b, favoring a heavy element enhancement ("metallicity") of ∼10−30× the solar value, a sub-solar carbon-to-oxygen (C/O) ratio, and a solar-to-super-solar potassium-to-oxygen (K/O) ratio. The observations are best explained by wavelength-dependent, non-gray clouds with inhomogeneous coverage of the planet's terminator.</p>
Research Compendium for Himes et al. (2023): "Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods"
<p>This archive is the Reproducible Research Compendium for</p> <p>Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods</p> <p>by Himes, Harrington, and Baydin (2023), published in The Planetary Science Journal.</p> <p>The compendium includes all the software, documentation, configuration files, plots, and data published in the paper. The compendium is under the Reproducible Research Software License; see LICENSE file. The README provides additional information and describes the contents of each compressed .tar.gz file.</p>
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Allen Brain Atlas
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