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98 results for “Exoplanets”
Products and Models for "Early Release Science of the Exoplanet WASP-39b with JWST NIRCam"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05590-4">https://www.nature.com/articles/s41586-022-05590-4</a><br> <br> OVERVIEW: Measuring the metallicity and carbon-to-oxygen (C/O) ratio in exoplanet atmospheres is a fundamental step towards constraining the dominant chemical processes at work and, if in equilibrium, revealing planet formation histories. Transmission spectroscopy<sup> </sup>provides the necessary means by constraining the abundances of oxygen- and carbon-bearing species; however, this requires broad wavelength coverage, moderate spectral resolution, and high precision that, together, are not achievable with previous observatories. Now that JWST has commenced science operations, we are able to observe exoplanets at previously uncharted wavelengths and spectral resolutions. Here we report time-series observations of the transiting exoplanet WASP-39b using JWST’s Near InfraRed Camera (NIRCam). The long-wavelength spectroscopic and short-wavelength photometric light curves span 2.0 – 4.0 µm, exhibit minimal systematics, and reveal well-defined molecular absorption features in the planet’s spectrum. Specifically, we detect gaseous H<sub>2</sub>O in the atmosphere and place an upper limit on the abundance of CH<sub>4</sub>. The otherwise prominent CO<sub>2</sub> feature at 2.8 µm is largely masked by H<sub>2</sub>O. The best-fit chemical equilibrium models favour an atmospheric metallicity of 1–100× solar (i.e., an enrichment of elements heavier than helium relative to the Sun) and a sub-stellar carbon-to-oxygen (C/O) ratio. The inferred high metallicity and low C/O ratio may indicate significant accretion of solid materials during planet formation<sup> </sup>or disequilibrium processes in the upper atmosphere.</p>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
Galactic chemical evolution of the solar neighborhood, solar twins and exoplanet indicators
<p>Galactic chemical evolution (GCE), solar analogues or twins, and peculiarities of<br> the solar composition with respect to the twins are inextricably related.<br> We examine GCE parameters from the literature and present newly derived values<br> using a quadratic fit that gives zero for a Solar age (i.e. 4.6 Gyr). <br> We show how the GCE parameters may be used not only to ``correct'' abundances<br> to the solar age, but to predict {\bf average} relative elemental <br> abundances as a function of age. <br> We address the question of whether the solar abundances<br> are depleted in refractories and enhanced in volatiles and<br> find that the answer is sensitive to the selection of a<br> representative standard. Our best quality data sets do<br> not support the notion that the Sun is depleted in refractories. <br> A simple model allows us to estimate<br> the amount of refractory-rich material missing from the sun<br> or alternately added to the average solar twin. The model<br> gives between zero and 1.4 earth masses. <br> </p>
Exoplanet imaging data challenge, phase 2
<p><strong>Datasets for the second phase of the Exoplanet Imaging Data Challenge</strong> (<a href="https://exoplanet-imaging-challenge.github.io/">https://exoplanet-imaging-challenge.github.io</a>). </p> <p>The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must perform two tasks: provide (1) astrometry of the detected signals, and (2) spectrophotometry of the detected signals.</p> <p>For this phase, we therefore provide with <strong>8</strong> high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in <em>.fits</em> format):</p> <p>(a) <em>image_cube_instxx.fits</em>: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode;<br> (b) <em>parallactic_angles_instxx.fits</em>: the corresponding parallactic angle and airmass variation during the observation sequence;<br> (c) <em>wavelength_vect_instxx.fits</em>: the corresponding wavelength vector for each spectral channel;<br> (d) <em>psf_cube_instxx.fits</em>: the non-coronagraphic (point spread function) multispectral image of the target star;<br> (e) <em>first_guess_astrometry_instxx.fits</em>: a first guess position w.r.t the star of the (2 or 3) injected planetary signals.</p> <p>Each data set has 2 to 3 injected planetary signals in various locations.<br> Each data set has very different observing conditions, from very good to very bad.</p> <p><em>Additional information and ressources can be found on the <a href="https://exoplanet-imaging-challenge.github.io/">website</a> and dedicated <a href="https://github.com/exoplanet-imaging-challenge/phase2">Github repository.</a></em></p> <p><em>The results of the data challenge must be submitted directly by participants on the <a href="https://eval.ai/web/challenges/challenge-page/1717/overview">EvalAI platform</a>.</em></p>
Benchmark protocol for exoplanet forward model and retrieval
<p>Benchmark protocol for giant exoplanet atmosphere tools, presented in Baudino et al. 2017 <a href="https://doi.org/10.3847/1538-4357/aa95be">https://doi.org/10.3847/1538-4357/aa95be</a></p> <p>The original data to reproduice the protocol are used in a jupyter notebook "Tutorial.ipynb" including all the plot routines to help to compare with you own models</p>
Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Exoplanet Occurrence Rates Plot
<p>Everyone knows the obligatory semi-major axis vs mass plot that every exoplanet scientist is required by law to show in every talk. We wondered how literature occurrence rates would look overlaid on this plot. So we made it. </p> <p>The script for generating this plot is located at <a href="https://github.com/logan-pearce/occurrence-rate-plot">https://github.com/logan-pearce/occurrence-rate-plot</a>, so you can adjust the colors and the plot to your heart's content.</p>
"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>
The UltracoolSheet: Photometry, Astrometry, Spectroscopy, and Multiplicity for 4000+ Ultracool Dwarfs and Imaged Exoplanets
<p>The UltracoolSheet is a catalog of 4000+ ultracool dwarfs (spectral types M6 and later) and imaged exoplanets, including photometry, absolute astrometry, proper motions, parallaxes, multiplicity, spectroscopic classifications, memberships, and ages.</p> <p>The catalog lives in a Google spreadsheet at</p> <p> <a href="http://bit.ly/UltracoolSheet"><strong>http://bit.ly/UltracoolSheet</strong></a></p> <p>where it will receive future updates. The README tab in the spreadsheet contains a detailed description of the contents of the catalog.</p> <p>UltracoolSheet started as a catalog of all spectroscopically confirmed objects of spectral type L0 or later in the extended solar neighborhood (out to ~100 pc) that were known as of April 15, 2015. It has subsequently been extended to include many late-M dwarfs and also augmented with more recent discoveries spanning all ultracool spectral types.</p> <p>The component spreadsheets of UltracoolSheet are posted here as CSV files, along with summary figures and (.dat) files containing ASCII tables to simplify figure plotting for users. The Summary Plots.pdf file shows the sky map, age distribution, publication dates, and proper motion since publication for all objects in UltracoolSheet, as well as the distribution of projected separations of companions.</p> <p>When using data from the UltracoolSheet, please cite the individual papers from which the data comes. Citations codes are included for all the data in the tables, and the References table translates the citation codes into ADS bibcodes, Papers citekeys, and publication titles.</p> <p>For research that benefits from this compilation, please cite this Zenodo post and include the following acknowledgment:</p> <p>"This work has benefited from The UltracoolSheet at <a href="http://bit.ly/UltracoolSheet">http://bit.ly/UltracoolSheet</a>, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="http://adsabs.harvard.edu/abs/2012ApJS..201...19D">Dupuy & Liu (2012)</a>, <a href="http://adsabs.harvard.edu/abs/2013Sci...341.1492D">Dupuy & Kraus (2013)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2014ApJ...792..119D/abstract">Deacon et al. (2014)</a>, <a href="http://adsabs.harvard.edu/abs/2016ApJ...833...96L">Liu et al. (2016)</a>, <a href="http://adsabs.harvard.edu/abs/2018ApJS..234....1B">Best et al. (2018)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B/abstract">Best et al. (2021)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...959...63S">Sanghi et al. (2023)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S">Schneider et al. (2023)</a>."</p> <p>The UltracoolSheet itself can be cited by this Zenodo DOI: 10.5281/zenodo.4169084 </p> <p>Questions, comments, suggestions, or contributions?<br>Please send them to <a href="mailto:ultracool.sheet@gmail.com">ultracool.sheet@gmail.com</a> (especially contributions!)</p> <p> </p>
LBT/PEPSI Spectropolarimetry of the Exoplanet Host Star 51 Peg
<p>Reduced observations of the exoplanet host star 51 Peg from the Large Binocular Telescope (LBT) using the Potsdam Echelle Polarimetric and Spectroscopic Instrument (PEPSI) on 12 nights between 2022 November 18 and 2022 December 3.</p>
Source Data for Manuscript: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs"
<p>This dataset accompanies the manuscript entitled: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs", which was accepted for publication in the Planetary Science Journal. Included are the source files for all figures included in the paper.</p>
Data and code for "Carbon cycle instability for high-CO2 exoplanets: implications for habitability"
<p>Data supporting "Carbon cycle instability for high-CO2 exoplanets: implications for habitability," Graham and Pierrehumbert, 2024, <em>Astrophysical Journal </em>(under review at time of upload)</p> <ul> <li>Global climate model (GCM) simulation output necessary to reproduce figures in paper. Netcdf (.nc) format. <ul> <li>For simulations receiving instellation of 1250 W m-2, the format is atmos_monthly_[co2 mixing ratio]ppmv_S1250.nc</li> <li>For other simulations, the format is: atmos_monthly_[co2 partial pressure]bar_S[675, 750, 800, or 1000].nc</li> </ul> </li> <li>Continental configuration land mask used in GCM simulations. Netcdf (.nc) format. <ul> <li>land.nc</li> </ul> </li> <li>Python scripts to<br> <ul> <li>post-process climate simulation data to calculate global weathering rates according to WHAK or MAC weathering <ul> <li>isca_weathering_calc.py</li> </ul> </li> <li>reproduce all model-based figures <ul> <li>figures 1-8: isca_plot_maker.py</li> <li>figure 9: isca_weathering_calc.py</li> </ul> </li> </ul> </li> </ul>
Supplementary material for the paper EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS - Chemical network details
<p>Supplementary material for the paper</p> <p>EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS</p> <p>by Locci et al. 2021, submitted to PSJ (R1 version)</p> <p>This document contains the complete list of the chemical reactions included in the model: bimolecular reactions (neutral-neutral and ion-neutral) in Table 1, termolecular reactions in Table 2, thermodissociative reactions in Table 3, reverse reactions in Table 4, and finally photochemical reactions in Table 5.</p>
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>
Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
<p>The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O<sub>2</sub>) concentrations increased from ~10<sup>−5</sup> times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O<sub>2</sub> mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O<sub>2</sub> and H<sub>2</sub>O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O<sub>2</sub> concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O<sub>2</sub> concentrations of <1% PAL.</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.