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98 results for “Exoplanets”
Raw data for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection of data contains all raw data needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>It can also be used as a demonstration dataset for our Python package fours.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
Intermediate results for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection contains all intermediate results needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>You can use these intermediate results to create all plots in our paper without the need to run all experiments on a large cluster.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
Products and Models for "A benchmark JWST near-infrared spectrum for the exoplanet WASP-39 b"
<p>Publication Here: https://www.nature.com/articles/s41550-024-02292-x<br><br>Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to <em>JWST,</em> these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we show a combined analysis of <em>JWST</em> transmission spectroscopy across four different instrumental modes spanning 0.5–5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date. Leveraging the advantages of a uniform light curve analysis, we improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. Together, these collected data constitute the most comprehensive transmission spectrum of an exoplanet to date, providing unparalleled access to atmospheric absorbers including Na, K, H2O, CO, CO2, and SO2.</p>
The Locus Algorithm Exoplanet-Search Pointings Catalogue
<p>Presented here is a Catalogue of Pointings for Optimal Differential Photometry for 61,662,376 stars presented in the form of a CSV file. A total of 67,043,579 stars were analysed using the Locus Algorithm (Creaner et al, 2019), and the results of that analysis are presented here. A paper detailing this work is currently in writing. This catalogue is invisaged for use in the search for extrasolar planets by using the pointings presented here to allow for a maximum differential photometry precision and thus aid ground based searches for extrasolar planets.</p> <p>Instructions on how to use the data are contained in readme.md. An SQL script to identify the reference stars is also presented.</p> <p>Also given here are source files containing the fits and csv files generated by the grid jobs in a .zip archive.</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>
Products and Models for "Early Release Science of the Exoplanet WASP-39b with JWST NIRSpec G395H"
<p>Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05591-3">https://www.nature.com/articles/s41586-022-05591-3</a></p> <p>Overview:<br> Measuring the abundances of carbon and oxygen in exoplanet atmospheres is considered a crucial avenue for unlocking the formation and evolution of exoplanetary systems. Access to an exoplanet’s chemical inventory requires high-precision observations, often inferred from individual molecular detections with low-resolution space-based and high-resolution ground-based facilities. Here we report the medium-resolution (R≈600) transmission spectrum of an exoplanet atmosphere between 3–5 𝛍m covering multiple absorption features for the Saturn-mass exoplanet WASP-39b, obtained with JWST NIRSpec G395H. Our observations achieve 1.46x photon precision, providing an average transit depth uncertainty of 221 ppm per spectroscopic bin, and present minimal impacts from systematic effects. We detect significant absorption from CO<sub>2</sub> (28.5<span class="math-tex">\(\sigma\)</span>) and H<sub>2</sub>O (21.5<span class="math-tex">\(\sigma\)</span>), and identify SO<sub>2</sub> as the source of absorption at 4.1 𝛍m (4.8<span class="math-tex">\(\sigma\)</span>). Best-fit atmospheric models range between 3 and 10x solar metallicity, with sub-solar to solar C/O ratios. These results, including the detection of SO<sub>2</sub>, underscore the importance of characterising the chemistry in exoplanet atmospheres, and showcase NIRSpec G395H as an excellent mode for time series observations over this critical wavelength range.<strong> </strong></p> <p> </p> <p> </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 and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</p>
Accompanying Data for the Manuscript "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"
<p>This file contains results for all cases considered in the manuscript titled "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"</p>
Bioverse: The Habitable Zone Inner Edge Discontinuity as an Imprint of Runaway Greenhouse Climates on Exoplanet Demographics
<p>This repository contains data required to run the <a href="https://github.com/matiscke/hz-inner-edge-discontinuity">pipeline producing the results and figures in Schlecker+2023</a>, in particular results objects created with expensive model grid runs of <a href="https://github.com/danielapai/bioverse">Bioverse</a>.</p>
Data for: Higher water loss on Earth-like exoplanets in eccentric orbits
<p>The climate of a terrestrial exoplanet is controlled by the type of host star, the orbital configuration and the characteristics of the atmosphere and the surface. Many rocky exoplanets have higher eccentricities than those in the Solar System, and about 18% of planets with masses < 10 M⊕ have 𝑒 > 0.1. Underexplored are the implications of such high eccentricities on the atmosphere, climate, and potential habitability on such planets. We use WACCM6, a state-of-the-art fully-coupled Earth-system model, to simulate the climates of two Earth-like planets; one in a circular orbit (𝑒 = 0), and one in an eccentric orbit (𝑒 = 0.4). We quantify the effects of eccentricity on the atmospheric water abundance and loss given the importance of liquid water for habitability. The asymmetric temperature response in the eccentric orbit results in a water vapour mixing ratio in the stratosphere (> 20 ppmv) that is approximately five times greater than that for circular orbit (∼ 4 ppmv). This leads to a ∼ 3 time increase in the atmospheric hydrogen loss rate and a corresponding ∼ 3 times decrease in the ocean loss timescale. Thus, highly-eccentric Earth-like exoplanets can still retain their oceans over the lifetime of the system. Using the Planetary Spectrum Generator, we simulate the idealised transmission spectra for both cases. We find that the water absorption features are stronger at all wavelengths for the 𝑒 = 0.4 spectrum than for the circular case. Hence, highly-eccentric Earth-like exoplanets may be prime targets for future transmission spectroscopy observations to confirm, or otherwise, the presence of atmospheric water vapour.</p>
Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
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Data for: Higher water loss on Earth-like exoplanets in eccentric orbits
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Microlensing events indicate that super-Earth exoplanets are common in Jupiter-like orbits
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Data associated with: Haze evolution in temperate exoplanet atmospheres through surface energy measurements
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Exoplanet Aerosol Transmission Spectra
<p>Aerosol transmission spectra for a variety of proposed cloud and haze species computed assuming monodisperse 0.1 micron particles distributed with a constant mass mixing ratio profile in the atmosphere. The first column is wavelength in microns, the second is planet radius in km, and third column is atmospheric molecular weight. Optical constants for tholins are taken from Khare et al. (1984); those for soots are from Lavvas and Koskinen (2017); those for KCl, ZnS, Na2S, MnS, and Cr are from Morley et al. (2012); those for NaCl are from Eldridge and Palik (1985) and Querry (1987); those for Mg2SiO4, Fe, and Al2O3 are from Wakeford and Sing (2015); and those for TiO2 are from Posch et al. (2003) and Zeidler et al. (2011).</p>
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>
Refractive Indices For Virga Exoplanet Cloud Model
<p><strong>Difference between v1 and v2? </strong></p> <p>The optical constants were updated based on Batalha et al (in prep). The default values and references are located in the <a href="https://github.com/natashabatalha/virga/blob/master/virga/ior_factory.py">IOR Factory</a>. Additionally the specific radii for which the mieff parameters were computed have also changed. Therefore if using Virga V1 we recommend using the Mieff's computed in V2. </p> <p><strong>Additional Details</strong></p> <p>These files are used to compute parameterized cloud models with the <a href="http://github.com/natashabatalha/virga">Virga Exoplanet Cloud Model</a>. The <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#">documentation is available</a> here for specifically how these files are included into virga.</p> <p><strong>1. REFRACTIVE INDICES: (.refrind): </strong>contain the refractive indices of each condensate species. If you are running cloud models utilizing this data, please cite the corresponding source for each species listed below. All files are 4 columns structured as : </p> <blockquote> <p>index, wavelength (micron), real part, imaginary part. </p> </blockquote> <p>Virga reads in these files <a href="https://github.com/natashabatalha/virga/blob/32665150fc83769cae46fd3290f5a237f959712c/virga/calc_mie.py#L452">in this routine</a> but you can simply use this python code:</p> <pre><code>filename = "H2O.refrind" idummy, wave, nn, kk = np.loadtxt(open(filename,'rt').readlines(), unpack=True, usecols=[0,1,2,3])#[:-1] </code></pre> <p> </p> <p><strong>2. MIE PARAMETERS: (.mieff): </strong>There are specific tutorials and functions in virga that will guide you through computing these on your own. However, we provide them here for completeness. The program that calculates these is <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Creating-the-Mie-scattering-Database">demonstrated here</a>. Note that each Mie parameters are averaged 6 points within the wavelength bin. You can use this function here to <a href="https://natashabatalha.github.io/virga/notebooks/1_GettingStarted.html#Analyzing-Mie-Parameters">parse the data</a>. Or, you can simply read the mieff files with this code: </p> <pre><code>import pandas as pd gas = "H2O" df = pd.read_csv(gas+".mieff",names=['wave','qscat','qext','cos_qscat'], delim_whitespace=True) </code></pre> <p> </p> <p><strong>CITATIONS TO REFERENCE FOR EACH SPECIES (See Table 1 Batalha et al. submitted):</strong></p> <p> </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>
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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.