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29 results for “Exoplanet Atmospheres”
"Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets" Data Repository
<p>This repository contains the data files, analysis Jupyter notebooks and figures from Thompson et al. 2023 "Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets"</p>
sunset: A database of synthetic atmospheric-escape transmission spectra for nearly every transiting exoplanet
<div> <div> <p><strong>This sunset version belongs to the A&A paper. The sunset database belonging to the arXiv pre-print can be found as version 1 of this Zenodo repository.</strong></p> <p>This repository contains the sunset database of atmospheric-escape transmission spectra for most currently known transiting exoplanets. This database is described in Linssen et al. (2025). The complete zipped (unzipped) database is ~5GB (~28GB). To prevent a huge download just to access a specific single planet model, we have uploaded sunset in a few different batches. The "zip_dictionary.txt" file lists each planet and which zip batch it is in. </p> <p>For each planet, there are three files:<br>- The "info" file contains warnings that pertain to that planet specifically (for general warnings that apply to each planet, see Linssen et al. 2025). It also lists the used planetary parameters, and the transit depth, equivalent width, S/N prefactors and transmission spectroscopy metrics for a few spectral lines. Finally, it gives simple step-by-step instructions on how to reproduce the model results using sunbather.<br>- The "spectrum_sparse" file contains the transmission spectrum. In principle, the spectrum runs from 911 to 11,000 angstroms in 1,000,000 bins (translating to R~400,000). However, in large portions of this wavelength grid, there are no spectral lines and the transit spectrum is simply equal to the continuum. To keep the file size to a minimum, we have removed those continuum regions from the spectrum, resulting in a "sparse" spectrum.<br>- The "structure" file contains the radial atmospheric structure profiles of the density, velocity, temperature and mean molecular weight.</p> <p>Additionally, this repository includes "included_lines_by_species.txt" and "included_lines_by_wavelength.txt", which list all the spectral lines that are present in the transmission spectra. Lines are labeled by the specific ion that they originate from, as well as the energy level. The energy level is expressed as a number, where 1 is the ground state, 2 is the first excited state, etc. Translating this energy level into the atomic configuration can be done by looking in the sunbather source code: in the /sunbather/src/sunbather/RT_tables/ folder, each ion has a file such as "Fe+_levels_processed.txt", which lists the energy levels and their atomic configurations.</p> <p>Finally, there is a large tabular file called "sunset_overview.csv". This file includes the NASA Exoplanet Archive parameters of each exoplanet. Additionally, there are some columns that we added, with calculated variables such as the atmospheric mass-loss rate, the Parker wind temperature, and line depths, equivalent widths, S/N prefactors and TSM metrics for various spectral lines. See the file header for explanation of each column. The file can easily be read in Python using pandas.read_csv("sunset_overview.csv", comments="#")</p> </div> </div>
ExoCAM: A 3D Climate Model for Exoplanet Atmospheres :: Model data and supplementary figures and analysis
<p>This repository contains 3D GCM model output data from the paper, "ExoCAM: A 3D Climate Model for Exoplanet Atmospheres", which is published in the Planetary Science Journal: Trapppist Habitable Atmospheres Intercomparison Special Issue. The model data includes mean climate states for the standard THAI simulations of TRAPPIST-1e, simulations using an upgraded radiative transfer, along with a large variety sensitivity experiments considering common tuning parameters of sub-grid scale cloud and convection physics. In total 43 simulations are included. Also included here are a variety of multi-panel contour plots showing basic results from all simulations as supplemental figures.</p> <p>https://iopscience.iop.org/article/10.3847/PSJ/ac3f3d</p>
Supplementary Information: Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST
<p>Supplementary information containing additional figures of the journal article 'Investigating the detectability of hydrocarbons in exoplanet atmospheres with JWST' by D. Gasman, M. Min, and K. L. Chubb, published in Astronomy & Astrophysics (2022).</p>
Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).
<p>Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).</p>
Exoplanet atmosphere evolution: emulation with neural networks: supplementary data
<p>Supplementary data for 'Exoplanet atmosphere evolution: emulation with neural networks'. Includes MCMC chain for Bayesian Hierarchical Model (BHM) including samples of core mass for all planets, as well 5 hyper parameters (see paper for details). Additionally, a machine readable version of Table 1 is made available.</p>
PICASO 3.0 Atmospheric Models of WASP-39 b for the JWST Transiting Exoplanet Community Early Release Science Program
<p><strong>OVERVIEW</strong></p> <p>The exoplanetary atmospheric models used in the recent <a href="https://www.nature.com/articles/s41586-022-05269-w">discovery of CO<sub>2 </sub>in WASP- 39 b's atmosphere</a> by the JWST transiting exoplanet community early release science program are presented here. These models are also being used to analyze multiple observations of WASP 39-b obtained using various JWST instruments and observational modes by the transiting exoplanet ERS team. The 1D Radiative-Convective-Thermochemical Equilibrium (RCTE) atmospheric models were computed using the open-source 1D climate model <a href="https://natashabatalha.github.io/picaso/">PICASO 3.0</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>). These atmospheric models were then post-processed with condensation clouds using the open-source cloud model <a href="https://natashabatalha.github.io/virga/">VIRGA</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>). The atmospheric models were also post-processed with the 1D photochemical network code <a href="https://github.com/exoclime/VULCAN">VULCAN</a> (<a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a>) to explore photochemistry in WASP-39 b's atmosphere.</p> <p><strong>1D RCTE CLOUD-FREE MODELS</strong></p> <p>The base 1D RCTE grid includes atmospheric metallicity points at 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 50.0, and 100.0x solar values. The Carbon-to-Oxygen (C/O) ratio value is varied between four values - 0.23, 0.46, 0.69, and 0.92. The intrinsic temperature of the planet has been varied across 100, 200, and 300 K, whereas two values of the heat redistribution factor - 0.4 and 0.5 are included. A heat redistribution factor of 0.5 corresponds to the case of full heat redistribution. With these grid points, the grid includes a total of 8x4x3x2= 192 different models.</p> <p>These models are in the "RCTE_cloud_free.zip" folder. The naming scheme of these files is "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_.nc" where [T_int] represents the intrinsic temperature of the planet, [MH] is the log<sub>10 </sub>of the atmospheric metallicity relative to solar, [CtoO] is the C/O ratio relative to solar, and [rfacv] is the heat-redistribution factor. So, a metallicity value of 0.3xsolar will have a [MH] value of -0.5, and a C/O 0.46 is considered 1xsolar and will correspond to [CtoO]=1. [T_int] and [rfacv] can assume values described in the previous paragraph.</p> <p><strong>1D RCTE CLOUDY MODELS</strong></p> <p>The base 1D RCTE cloud-free models were post-processed to include condensation cloud species Na<sub>2</sub>S, MnS, and MgSiO<sub>3</sub>. The cloud structure and optical property calculations were performed using the VIRGA model where the sedimentation efficiency <em>f<sub>sed </sub></em>and the vertical eddy diffusion coefficient (<em>K<sub>zz</sub></em>) are free parameters. For the cloudy models, 5 <em>f<sub>sed </sub></em> values - 0.6, 1, 3, 6, and 10 were used along with 3 different values of log<sub>10</sub><em>K<sub>zz </sub></em>- 5, 7, 9, and 11, where <em>K<sub>zz </sub></em>is in cm<sup>2</sup>/s. These models are included in the "RCTE_cloudy.zip" folder following the naming structure "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc" where two other variables are added in the name - [log10Kzz] and [fsed]. Both of these variables can take values listed here.</p> <p><strong>PHOTOCHEMICAL CLOUD-FREE MODELS</strong></p> <p>A much smaller subset of the base 1D RCTE models were post-processed with the 1D photochemical network code VULCAN to simulate the effects of vertical mixing and photochemistry in WASP-39 b's atmosphere. log<sub>10</sub><em>K<sub>zz </sub></em>was varied again between the 5, 7, 9, and 11 for this purpose. These files are named as "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz].nc" and can be found in the "photochem_cloud_free.zip" folder.<br> <br> <strong>PHOTOCHEMICAL CLOUDY MODELS</strong></p> <p>The photochemical models were post-processed with clouds to simulate a cloudy atmosphere with disequilibrium chemistry. The <em>f<sub>sed </sub></em> and log<sub>10</sub><em>K<sub>zz </sub></em> grid system for the RCTE cloudy models has been used again for these models as well. These files are in the "photochem_cloudy.zip" folder and are named according to the format "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc".</p> <p><strong>FILE FORMATTING AND USAGE</strong></p> <p>All the files are released in the <a href="https://docs.xarray.dev/en/stable/">xarray</a> format. Each model has one single xarray file containing all metadata of that model. This metadata includes the input parameters used to compute the model, for example, the metallicity, C/O ratio, and intrinsic temperature. The temperature-pressure (<em>T(P)</em>) profile and the volume mixing ratio profiles of all the different gases in each model is also included in the metadata. The computed transmission spectrum of the model planet from 0.3-6 microns is included in the same file as well. The spectrum is calculated with resampled opacities at a spectral resolution of 60,000, but they should be re-binned at a spectral resolution of 3000 or less for comparison with observed data. For cloudy models, the wavelength dependant optical depth, asymmetry parameter, and single scattering albedo for each atmospheric layer are included in these xarray files.</p> <p>We refer to this <a href="https://natashabatalha.github.io/picaso/notebooks/codehelp/data_uniformity_tutorial.html#Reading/interpreting-an-xarray-file">PICASO tutorial</a> for reading/writing these xarray files. The spectrum from these xarray files can be easily extracted using the following code.</p> <pre><code class="language-python">import xarray as xr path = "path/to/files" ds_sm = xr.open_dataset(path+"profile_eq_planet_300_grav_4.5_mh_+2.0_CO_2.0_sm_0.0486_v_0.5_.nc") # for spectrum wavelength = ds_sm['wavelength'].values transit_depth = ds_sm['transit_depth'].values # for T(P) profile temperature = ds_sm['temperature'].values pressure = ds_sm['pressure'].values</code></pre> <p><a href="https://github.com/natashabatalha/picaso/blob/master/docs/notebooks/fitdata/GridSearch.ipynb">This tutorial</a> shows how to use these models to analyze the NIRSpec Prism observations of WASP-39 b, which led to <a href="http://www.nature.com/articles/s41586-022-05269-w">CO<sub>2 </sub>detection</a>. Please note that the folders must be unzipped before using them with this notebook.</p> <p><strong>CREDITS</strong></p> <p>If you use these modeling products in your work, please cite this zenodo repository along with the following papers depending on the part of the grid being used:</p> <p>1) RCTE_cloud_free.zip</p> <p> <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>2) RCTE_cloudy.zip</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>3) photochem_cloud_free.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a> </p> <p>4) photochem_cloudy.zip</p> <p> <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a></p> <p> </p>
Research Compendium for Harrington et al. (2021): "An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b"
<p>This archive is the Reproducible Research Compendium for<br> <br> An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b<br> <br> by Harrington et al. (2021), published in The Planetary Science Journal.<br> <br> BART is an atmospheric parameter retrieval code. It infers the properties of planetary atmospheres from spectroscopic observations. 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>
Supplementary Figures for "Patchy Forsterite Clouds in the Atmospheres of Two Highly Variable Exoplanet Analogs" (Vos+ 2023)
<p>Supplementary figures for "Patchy Forsterite Clouds in the Atmospheres of Two Highly Variable Exoplanet Analogs" (Vos et al. 2023; doi: 10.3847/1538-4357/acab58). The figures show posterior distributions from the retrievals for models that were not chosen as the preferred model. </p>
Data associated with: Haze evolution in temperate exoplanet atmospheres through surface energy measurements
Open the record for dataset details and reuse information.
Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.
<p>NetCDF dataset for paper titled: Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.</p> <p> </p> <p>Contains:</p> <p>1. Heng benchmark GCM data</p> <p>2. Rauscher benchmark GCM data</p> <p>3. HD209 model using semi-grey RT</p> <p>4. HD 209 model using non-grey picket fence RT</p> <p>5. HD 209 model using corr-k RT scheme</p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres - II. Cloud and chemistry connections in directly imaged sub-Jupiter exoplanets
<p>Gifs of GCM output from the paper, model is Teff = 1000 K, log g = 3, M/H = 1. </p><p>The atmos_daily_2980.nc file contains the GCM NETCDF output at 2080 days.</p>
Supplementary Material: The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra
<p>Supplementary material for "The Importance of Optical Wavelength Data on Atmospheric Retrievals of Exoplanet Transmission Spectra" DOI: <a href="https://ui.adsabs.harvard.edu/link_gateway/2024arXiv240307801F/doi:10.48550/arXiv.2403.07801" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2403.07801</a></p> <p>Contents of this record:</p> <ul> <li>The retrieved atmospheric parameters for the population (see supplementary_material.pdf).</li> <li>The retrieval statistics per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns (see supplementary_material.pdf).</li> <li>Planet specific retrieved spectra for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> <li>Retrieved parameter cornerplots per planet for the wavelength ranges 0.3-4.5, 0.6-4.5, and 1.1-4.5 microns.</li> </ul> <p>(NOTE: the retrieval model and priors for the results displayed in this record are specified in tables 2 and 3 of the paper.)</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>
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>
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>
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>
Reproduction package for the paper "Expanding the inventory of spectral lines used to trace atmospheric escape in exoplanets"
<p>This is a basic reproduction package for the paper "Expanding the inventory of spectral lines used to trace atmospheric escape in exoplanets" by Linssen & Oklopčić (2023). It provides the full transmission spectra of all simulations presented in the paper, as well as the data products necessary to reproduce the figures of the paper.</p>
Photochemical model output data associated with: How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres
Open the record for dataset details and reuse information.
The Impending Opacity Challenge in Exoplanet Atmospheric Characterization
<p>Cross-section required for running retrieval with https://github.com/disruptiveplanets/tierra.</p> <p>These cross-sections were generated using https://github.com/disruptiveplanets/TierraCrossSection</p> <p> </p> <p> </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.