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22 results for “brown dwarfs”
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
Brown Dwarf Analysis
<p>We perform the analysis with 13 years of Fermi-LAT data. In the notebook, we show the analysis of one Brown Dwarf. Following this methodology, you can reproduce the result of all BDs.</p> <p>In the mathematica file, you can find the calculation of capture rate and the plots related to the scattering cross-section of BDs.</p> <p>Some of the files have been updated. Find the details from Phys. Rev. D 107, 043012 (2023) & Erratum: Phys. Rev. D 109, 129904 (2024).</p>
Reproduction package for the paper "High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stab1893">"High-contrast observations of brown dwarf companion HR 2562 B with the vector Apodizing Phase Plate coronagraph" by Sutlieff et al. (2021)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Data Files and Code Associated with "Brown Dwarfs are Violet"
<p>The accompanying files provide some IDL code and "data behind the figures" for the paper titled "Brown Dwarfs are Violet" (by S. R. Cranmer), which has been submitted to <em>Research Notes of the AAS.</em></p> <p>This paper presents a collection of objective (CIE x,y coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs, as they may be perceived by human eyes without the reddening effects of the Earth's atmosphere. However, the algorithm described in the paper for computing RGB triples ought to be considered as only a preliminary first step; i.e., it needs to be tested by comparing the results to other more established ways of converting astronomical spectra to perceived colors.</p> <p> </p>
Brown Dwarfs are Violet: Python Tools for the Estimation of Human-eye Colors of Stars and Substellar Objects
<p>The accompanying files include a Python Jupyter notebook (and associated data files read in by the Python code) that carry out the calculations described by Cranmer (2023), talk 246.05 presented at the 241st Meeting of the American Astronomical Society (AAS) in Seattle, Washington. The abstract of the talk is provided here:</p> <p>There has always been interest in the perceived colors of the stars. They were key to the development of the H-R diagram, and they are also used widely in educational and public-outreach imagery. Thus, it is useful to develop software tools to compute these colors, as accurately as possible, from spectral energy distributions. This presentation follows up on an RNAAS paper (<a href="https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..201C/abstract">Cranmer 2021</a>) that presented a collection of objective (CIE coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs. A new empirical method of converting from CIE to RGB values is described, and results for various stellar spectra are presented. Although brown dwarfs over a wide range of effective temperatures (400 to 2000 K) emit most of their flux in the infrared, their visible spectra often exhibit a local maximum around a strong dip in the Na I cross section at 0.4-0.5 microns. Thus, they may appear purple to human eyes. Also, the hottest (O-type) main-sequence stars may appear even "bluer than the blue sky" because of Paschen continuum absorption. This presentation will update earlier stellar and brown-dwarf color estimates using more recently published synthetic spectra, and it will also investigate the effects of atmospheric absorption, over a range of air-mass values, on these perceived colors. Python Jupyter notebooks that carry out these calculations will be uploaded to the Zenodo repository for open-access distribution.</p> <p><strong>NOTE 1: </strong>The algorithms described here, for computing RGB triples, ought to be considered as preliminary results in ongoing research; i.e., they need additional testing and validation by comparing to the results of other more established ways of converting astronomical spectra to perceived colors.</p> <p><strong>NOTE 2:</strong> These files follow on from those provided in another Zenodo upload associated with the 2021 RNAAS paper: <a href="https://doi.org/10.5281/zenodo.5293307">https://doi.org/10.5281/zenodo.5293307</a></p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling
<p>Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023). </p> <p>Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days.</p> <p>Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename).</p> <p>`average' is the averaged output of the last 100 days.</p> <p>`daily' is the snapshot at the end of the simulation.</p>
Synthetic Colors for Brown Dwarfs using ATMO Non-Equilibrium Non-Adiabatic Atmospheres
<p>The Tables in the spreadsheets give magnitudes in the Vega system, calculated from synthetic spectra generated by ATMO non-equilibrium non-adiabatic atmospheres (Tremblin et al. 2015, Phillips et al. 2020, Leggett et al. 2021). Each file has three tabs corresponding to three metallicities: [m/H] = 0, -0.5 and -1.0. The file 2023_ATMO_MKO_WISE_Spitzer_phot gives MKO Y, J, H, K, Ks, and L'; WISE W1, W2, W3, and W4; and Spitzer [3.6] and [4.5] (columns 5 - 16 in the Tables). The other three files give colors for JWST NIRCam, NIRISS and MIRI filters, as indicated by the file name.</p> <p>The photometry is given for an observer at the Earth, for a brown dwarf at 10 pc with a radius of 0.1 Rsun. Column 3 in the Tables give the radius determined by evolutionary models for different metallicities by Marley et al. 2021 (and https://zenodo.org/record/5063476), for the specified temperature and gravity (columns 1 and 2). Column 4 gives the correction to the magnitudes for the correct (theoretical) radius. NOTE THAT THE CORRECTION MUST BE ADDED TO THE MAGNITUDES GIVEN IN COLUMNS 5 - 16 TO OBTAIN THE ABSOLUTE MAGNITUDE. For an analysis of the model color trends, using a comparison to observations, see Meisner et al. 2023.</p> <p>The photometry covers the following atmospheric parameters: effective temperatures between 1200 and 250 K (step size 100 K between 1200 and 400 K, with the step size decreasing for lower effective temperatures); log(g) with three values 4.0, 4.5 and 5.0; effective adiabatic index of 1.25; metallicity with three values -1.0, -0.5, and 0. A grid of synthetic spectra was computed at medium resolution (R ~3000) for wavelengths of 0.2 to 30 microns. All the models, for a wider range of parameters, are available at https://opendata.erc-atmo.eu. </p> <p>The models include rainout of condensates which depletes refractory species, but they do not include clouds. Tremblin et al. 2016 shows that diabatic convective processes (Tremblin et al. 2019) can reduce the temperature gradient in the atmosphere and reproduce the spectral reddening previously explained by clouds. Adjustments to the atmospheric temperature gradient have also been shown to be necessary to reproduce the energy distributions of the coldest brown dwarfs (Leggett et al. 2021). The grids used here modify the temperature gradient by adopting an effective adiabatic index. The levels modified are in between 0.15 and 15 bars at log g = 4.5 and are scaled by ×10<sup>(log(g)−4.5)</sup> at other surface gravities. Out-of-equilibrium chemistry is used with Kzz = 10<sup>5 </sup>cm<sup>2</sup>/s at log(g) = 5.0 and is scaled by ×10<sup>(2(5−log(g)))</sup> at other surface gravities. The mixing length is assumed to be 2 scale heights at 1.5 bars and higher pressures at log(g) = 4.5 and is scaled down by the ratio between the local pressure and the pressure at 1.5 bars for lower pressures. The 1.5 bars limit is scaled by ×10<sup>(log(g)−5) </sup>at other surface gravities. The chemistry includes 277 species and out-of-equilibrium chemistry has been performed using the model of Tsai et al. 2017. Opacity sources include H<sub>2</sub>-H<sub>2</sub>, H<sub>2</sub>-He, H<sub>2</sub>O, CO<sub>2</sub>, CO, CH<sub>4</sub>, NH<sub>3</sub>, Na, K, Li, Rb, Cs, TiO, VO, FeH, PH<sub>3</sub>, H<sub>2</sub>S, HCN, C<sub>2</sub>H<sub>2</sub>, SO<sub>2</sub>, Fe, H<sup>-</sup>, and the Rayleigh scattering opacities for H<sub>2</sub>, He, CO, N<sub>2</sub>, CH<sub>4</sub>, NH<sub>3</sub>, H<sub>2</sub>O, CO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>.</p> <p> </p> <p>REFERENCES</p> <p>Leggett et al 2021 ApJ 918, 11</p> <p>Marley et al. 2021 ApJ 920, 85 (and https://zenodo.org/record/5063476)</p> <p>Meisner et al. 2023, ApJ in press </p> <p>Phillips et al. 2020 A & Ap 637, 38</p> <p>Tremblin et al. 2015 ApJ 804, L17</p> <p>Tremblin et al. 2016 ApJ 817, L19</p> <p>Tremblin et al. 2019 ApJ 876, 144</p>
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry
<p><strong>OVERVIEW</strong></p> <p>The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<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>). The grid consists of four parameters – the effective temperature (T<sub>eff</sub>), gravity (log(g)), K<sub>zz</sub> in the radiative zones, and mixing length in the convective zones. Models with T<sub>eff </sub> between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K<sub>zz</sub> in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.</p> <p>There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). </p> <p><strong>ATMOSPHERIC COMPOSITION</strong></p> <p>The atmospheric composition files are located in the folder TP_chemistry. These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. </p> <p>The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.</p> <p><strong>SPECTRA</strong></p> <p>The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but <strong>please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage</strong>. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.</p> <p>The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using </p> <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre> <p> The first column of the file is wavenumbers in cm<sup>-1, </sup>which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm<sup>-1</sup>]. The second column of the file is flux in erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.</p> <p><strong>K<sub>zz</sub> PROFILE</strong></p> <p>The K<sub>zz </sub>as a function of pressure for each model is presented in these files. The K<sub>zz</sub> is reported in cm<sup>2</sup>/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm<sup>2</sup>/s.</p> <p> </p> <p><strong>EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES</strong></p> <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> <p><strong>CREDITS</strong></p> <p>If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a></p> <p> </p>
Asymmetric dark matter may alter the evolution of very low-mass stars and brown dwarfs
<p>MESA inlists and data files for "<a href="https://ui.adsabs.harvard.edu/?#abs/2011PhRvD..84j1302Z">Asymmetric dark matter may alter the evolution of very low-mass stars and brown dwarfs</a>"</p>
Theoretical Models of Ultra-Cool Objects (Brown Dwarfs and Free-Floating Planets) Including Water Latent Heat Effect: Thermal Structure, Spectra, and Photometry
<p><strong>OVERVIEW</strong><br> Data presented here are from the work of Tang et al. (2021), which present a one-dimensional (vertical) atmospheric structure model for ultra-cool objects<br> that includes moist adiabatic convection (water latent heat release effect).<br> Model grids across a range of effective temperatures (350, 300, 250, 200 K), metallicities ([M/H] of 0.0, 0.5, 0.7, 1.5),<br> and gravities (logg of 4.0, 4.5, 4.7, 5.0) that mimic the atmosphere condition of Y-class brown dwarf and Free-Floating Planets.</p> <p><strong>ATMOSPHERIC THERMAL STRUCTURE</strong><br> The pressure-temperature profile are saved under the ./pT_profiles/, which have two folders:</p> <ol> <li>pT_profile_dry_adiabat: for dry adiabatic treatment.</li> <li>pT_profile_moist_adiabat: for moist adiabatic treatment.</li> </ol> <p>File name of each compressed files gives the effective temperature (Teff, in Kelvin), gravity (in MKS), and metallicity (in [M/H]) information as for example:<br> t200g100nc_m0.0.cmp.gz --> Teff of 200K, gravity as 100 m/s2, and [M/H]=0.0.</p> <p>The table formate of the pressure-temperature profile, i.e., the .cmp files, is made to work with the [<a href="https://natashabatalha.github.io/picaso/">PICASO software</a>](https://natashabatalha.github.io/picaso/).<br> Each .cmp file contains</p> <ul> <li>Column 01 (x) : atmosphere pressure level</li> <li>Column 02 (pressure) : atmosphere pressure in bar</li> <li>Column 03 (DEN) : atmosphere density in cm^{-3}</li> <li>Column 04 (temperature) : atmosphere temperature in kelvin</li> <li>Column 05-14 : molecule mixing ratios for H2, He, CH4, H2O, NH3, CO, PH3, H2S, N2, CO2</li> <li>Column 15 (MU) : mean molecular weight in grams/mole</li> </ul> <p><strong>SPECTRA</strong><br> Model spectra are saved under the ./spectra/ with wavelength ranging from 1 to 30 micron.<br> File name of each compressed file gives the effective temperature (Teff, in Kelvin), gravity (in MKS), metallicity (in [M/H]), adiabatic treatment, and<br> the resolving power information as for example:<br> sp_t200_g100_m0.0_dry_R3000.csv.gz --> Teff of 200K, gravity as 100 m/s2, [M/H]=0.0, with dry adiabatic treatment, and R=3000.<br> Each spetrum .cv file contains two columns:</p> <ul> <li>Column 01 wavelength [micron]</li> <li>Column 02 Flux [W/m2/micron], the spectral fluxes are given at the top of the atmosphere, taken to be 1 Jupiter radius.</li> </ul> <p><strong>PHOTOMETRY</strong><br> Photometry data derived from the spectra at the top of the atmosphere (assuming one Jupiter radius) are given in the syn_photometry.csv.<br> Photometry filters are from the Mauna Kea Observatory (MKO) photometry system, the Wide-field Infrared Survey Explorer (WISE), and the<br> Spitzer Space Telescope. Filter profiles and Vega magnitude zero points are from the [<a href="http://svo2.cab.inta-csic.es/theory/fps/">SVO Filter Profile Service</a>](http://svo2.cab.inta-csic.es/theory/fps/)</p> <ul> <li>Column 01 (gravity) , gravity in MKS</li> <li>Column 02 (teff) , effective temperature in Kelvin</li> <li>Column 03 ([M/H]) , metallicity</li> <li>Column 04-12 , photometry magnitude for IRAC36, IRAC45, W1, W2, GiminiM, MKO_J, MKO_H, MKO_K, MKO_Lp all in Vega system.</li> <li>Column 13 (adiabatic), adiabatic treatment; D: dry adiabatic, M: moist adiabatic</li> </ul> <p><strong>CREDITS</strong><br> Please cite Tang et al. (2021, Astrophysical Journal, in press.)<br> [<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract">ADS link</a>](https://ui.adsabs.harvard.edu/abs/2021arXiv210507000T/abstract) if you used data here in your research.</p>
Figure Sets Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry II: Demographis of Nearby Brown Dwarfs"
<p>Images for Figure Sets 2, 3, 4, 6, and 9 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Demographis of Nearby Brown Dwarfs" (Currently accepted and in press.). Figure captions and file names are described in their associated README files (inside the bundles).</p> <p>Figure 2 shows survey sensitivity, Figure 3 shows the posterior distributions of our population models, Figure 4 compares our sensitivity and model distributions to the observed population, Figure 6 shows the marginalized population as a function of mass ratio, and Figure 9 shows the results of injecting an additional artificial detection.</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>
Data and Images for the paper "Evidence for a radiation belt around a brown dwarf" by Climent et al.
<p>Data used for the figures that appear in the main text and also in the supplementary materials in the paper titled "Evidence for a radiation belt around a brown dwarf" by Climent et al. published in Science.</p>
The Demographics of Giant Exoplanets and Brown Dwarfs in Wide Stellar Binaries
<p>While numerous exoplanets have now been uncovered in stellar binaries, the impact of companion stars on planet formation and evolution is still not understood. In this talk, I will present results of population trends seen among the known sample of planets in wide multiple star systems, which allows us to investigate the effects of stellar binarity on the resulting planetary architectures. In particular, observations of stars hosting close-in giant planets and brown dwarfs find an excess of binary companions on few hundred AU separations, and different planet demographics for these systems, suggesting that such binaries may provide favourable conditions for the formation of the observed inner companions. I will show results from simulations of self-gravitating protoplanetary disks adapted to binary-star environments, which show that certain binary configurations may trigger gravitational fragmentation and lead to the formation of giant planets in otherwise-stable disks.</p>
On following pages: 4. Golden Hamster (Mesocricetus auratus); 5. Ciscaucasian Hamster (Mesocricetus raddei); 6 Hamster (Cricetulus migratorius); 9. Long-tailed Dwarf Hamster (Cricetulus longicaudatus); 10. Striped Dwarf Hamster (Cricetulus alticola); 13. Tibetan Dwarf Hamster (Cricetulus kamensis); 14. Gansu Hamster (Cansumys canus); 15. Eversmann's Hamster (Allocricetulus eversmanni); 18. Common Hamster (Cricetus cricetus); 19. Long-clawed Mole (Ondatra zibethicus); 22. Western Heather Vole (Phenacomys intermedius); 23. Eastern Heather Vole (Phenacomys Tree Vole (Arborimus pomo); 27. Northern Bog Lemming (Synaptomys borealis); 28. Southern Bog Lemming (Synaptomys 31. Norway Brown Lemming (Lemmus lemmus); 32. Siberian Brown Lemming (Lemmus sibiricus); 33. Nearctic Brown. Brandt's Hamster (Mesocricetus brandti); 7. Romanian Hamster (Mesocricetus newton); 8. Gray Dwarf (Cricetulus barabensis); 11. Sokolov's Dwarf Hamster (Cricetulus sokolovi); 12. Ladakh Dwarf Hamster Greater Long-tailed Hamster (Tscherskia triton); 16. Mongolian Hamster (Allocricetulus curtatus); 17. Vole (Prometheomys schaposchnikowi); 20. Round-tailed Muskrat (Neofiber allen); 21. Common Muskrat ungava); 24. White-footed Vole (Arborimus albipes); 25. Red Tree Vole (Arborimus longicaudus); 26. Sonoma cooperi); 29. Wood Lemming (Myopus schisticolor); 30. Amur Brown Lemming (Lemmus amurensis); Lemming (Lemmus trimucronatus). in Cricetidae
On following pages: 4. Golden Hamster (Mesocricetus auratus); 5. Ciscaucasian Hamster (Mesocricetus raddei); 6 Hamster (Cricetulus migratorius); 9. Long-tailed Dwarf Hamster (Cricetulus longicaudatus); 10. Striped Dwarf Hamster (Cricetulus alticola); 13. Tibetan Dwarf Hamster (Cricetulus kamensis); 14. Gansu Hamster (Cansumys canus); 15. Eversmann's Hamster (Allocricetulus eversmanni); 18. Common Hamster (Cricetus cricetus); 19. Long-clawed Mole (Ondatra zibethicus); 22. Western Heather Vole (Phenacomys intermedius); 23. Eastern Heather Vole (Phenacomys Tree Vole (Arborimus pomo); 27. Northern Bog Lemming (Synaptomys borealis); 28. Southern Bog Lemming (Synaptomys 31. Norway Brown Lemming (Lemmus lemmus); 32. Siberian Brown Lemming (Lemmus sibiricus); 33. Nearctic Brown. Brandt's Hamster (Mesocricetus brandti); 7. Romanian Hamster (Mesocricetus newton); 8. Gray Dwarf (Cricetulus barabensis); 11. Sokolov's Dwarf Hamster (Cricetulus sokolovi); 12. Ladakh Dwarf Hamster Greater Long-tailed Hamster (Tscherskia triton); 16. Mongolian Hamster (Allocricetulus curtatus); 17. Vole (Prometheomys schaposchnikowi); 20. Round-tailed Muskrat (Neofiber allen); 21. Common Muskrat ungava); 24. White-footed Vole (Arborimus albipes); 25. Red Tree Vole (Arborimus longicaudus); 26. Sonoma cooperi); 29. Wood Lemming (Myopus schisticolor); 30. Amur Brown Lemming (Lemmus amurensis); Lemming (Lemmus trimucronatus).
On following pages: 350. Tanzanian White-toothed Shrew (Crocidura tansaniana); 351. Usambara White-toothed Shrew (Crocidura usambarae); 352. Kivu White-toothed Shrew (Crocidura kivuana); 353. Niobe's White-toothed Shrew (Crocidura niobe): 354. Desert White-toothed Shrew (Crocidura smithii); 355. Sahelian Tiny White-toothed Shrew (Crocidura pasha); 356. Roosevelt's White-toothed Shrew (Crocidura roosevelt); 357. Long-footed White-toothed Shrew (Crocidura crenata): 358. Grasse's White-toothed Shrew (Crocidura grassei); 359. Bicolored African White-toothed Shrew (Crocidura fuscomurina); 360. Flat-headed White-toothed Shrew (Crocidura planiceps); 361. Crosse's White-toothed Shrew (Crocidura crossei): 362. Jouvenet's White-toothed Shrew (Crocidura jouvenetae); 363. Mauritanian White-toothed Shrew (Crocidura lusitania); 364. Naked-tailed White-toothed Shrew (Crocidura littoralis): 365. Gracile White-toothed Shrew (Crocidura maurisca); 366. Reddish-gray White-toothed Shrew (Crocidura cyanea); 367. Swamp White-toothed Shrew (Crocidura mariquensis), 368. Lesser Gray-brown White-toothed Shrew (Crocidura silacea): 369. Dent's White-toothed Shrew (Crocidura denti); 370. Hildegarde's White-toothed Shrew (Crocidura hildegardeae); 371. Mamfe White-toothed Shrew (Crocidura virgata), 372. Bates's White-toothed Shrew (Crocidura batesi): 373. Sao Tome White-toothed Shrew (Crocidura thomensis); 374. Fingui White-toothed Shrew (Crocidura fingui); 375. Fraser's White-toothed Shrew (Crocidura poensis); 376. Nigerian White-toothed Shrew (Crocidura nigeriae); 377. Hun White-toothed Shrew (Crocidura attila); 378. Fox's White-toothed Shrew (Crocidura foxi); 379. Buttikofer's White-toothed Shrew (Crocidura buettikoferi); 380. Therese's White-toothed Shrew (Crocidura theresae); 381. Large-headed White-toothed Shrew (Crocidura grandiceps); 382. Wimmer's White-toothed Shrew (Crocidura wimmeri); 383. African Black White-toothed Shrew (Crocidura nigrofusca); 384. Savanna Dwarf White-toothed Shrew (Crocidura nanilla); 385. Nimba White-toothed Shrew (Crocidura nimbae). in Soricidae
On following pages: 350. Tanzanian White-toothed Shrew (Crocidura tansaniana); 351. Usambara White-toothed Shrew (Crocidura usambarae); 352. Kivu White-toothed Shrew (Crocidura kivuana); 353. Niobe's White-toothed Shrew (Crocidura niobe): 354. Desert White-toothed Shrew (Crocidura smithii); 355. Sahelian Tiny White-toothed Shrew (Crocidura pasha); 356. Roosevelt's White-toothed Shrew (Crocidura roosevelt); 357. Long-footed White-toothed Shrew (Crocidura crenata): 358. Grasse's White-toothed Shrew (Crocidura grassei); 359. Bicolored African White-toothed Shrew (Crocidura fuscomurina); 360. Flat-headed White-toothed Shrew (Crocidura planiceps); 361. Crosse's White-toothed Shrew (Crocidura crossei): 362. Jouvenet's White-toothed Shrew (Crocidura jouvenetae); 363. Mauritanian White-toothed Shrew (Crocidura lusitania); 364. Naked-tailed White-toothed Shrew (Crocidura littoralis): 365. Gracile White-toothed Shrew (Crocidura maurisca); 366. Reddish-gray White-toothed Shrew (Crocidura cyanea); 367. Swamp White-toothed Shrew (Crocidura mariquensis), 368. Lesser Gray-brown White-toothed Shrew (Crocidura silacea): 369. Dent's White-toothed Shrew (Crocidura denti); 370. Hildegarde's White-toothed Shrew (Crocidura hildegardeae); 371. Mamfe White-toothed Shrew (Crocidura virgata), 372. Bates's White-toothed Shrew (Crocidura batesi): 373. Sao Tome White-toothed Shrew (Crocidura thomensis); 374. Fingui White-toothed Shrew (Crocidura fingui); 375. Fraser's White-toothed Shrew (Crocidura poensis); 376. Nigerian White-toothed Shrew (Crocidura nigeriae); 377. Hun White-toothed Shrew (Crocidura attila); 378. Fox's White-toothed Shrew (Crocidura foxi); 379. Buttikofer's White-toothed Shrew (Crocidura buettikoferi); 380. Therese's White-toothed Shrew (Crocidura theresae); 381. Large-headed White-toothed Shrew (Crocidura grandiceps); 382. Wimmer's White-toothed Shrew (Crocidura wimmeri); 383. African Black White-toothed Shrew (Crocidura nigrofusca); 384. Savanna Dwarf White-toothed Shrew (Crocidura nanilla); 385. Nimba White-toothed Shrew (Crocidura nimbae).
On following pages: 164. Mount Anacuao Tree Mouse (Musseromys anacuao); 165. Mount Pulag Tree Mouse (Musseromys beneficus); 166. Mount Banahaw Tree Mouse (Musseromys gulantang); 167. Mount Amuyao Tree Mouse Musseromys inopinatus); 168. Southern Luzon Giant Cloud Rat (Phloeomys cumingi); 169. Northern Luzon Giant Cloud Rat (Phloeomys pallidus); 170. Large-toothed Hairy-tailed Rat (Batomys dentatus); 171. Luzon Cordillera Hairy-tailed Rat (Batomys granti); 172. Hamiguitan Hairy-tailed Rat (Batomys hamiguitan); 173. Russet Hairy-tailed Rat (Batomys russatus); 174. Mindanao Hairy-tailed Rat (Batomys salomonseni); 175. Mount Isarog Hairy-tailed Rat (Batomys uragon), 176. Black-tailed Dwarf Cloud Rat (Carpomys melanurus); 177. Brown-tailed Dwarf Cloud Rat (Carpomys phaeurus); 178. Dinagat Bushy-tailed Cloud Rat (Crateromys australis); 179. Panay Bushy-tailed Cloud Rat (Crateromys heaneyi), 180. llin Bushy-tailed Cloud Rat (Crateromys paulus); 181. Luzon Bushy-tailed Cloud Rat (Crateromys schadenbergi). in Muridae
On following pages: 164. Mount Anacuao Tree Mouse (Musseromys anacuao); 165. Mount Pulag Tree Mouse (Musseromys beneficus); 166. Mount Banahaw Tree Mouse (Musseromys gulantang); 167. Mount Amuyao Tree Mouse Musseromys inopinatus); 168. Southern Luzon Giant Cloud Rat (Phloeomys cumingi); 169. Northern Luzon Giant Cloud Rat (Phloeomys pallidus); 170. Large-toothed Hairy-tailed Rat (Batomys dentatus); 171. Luzon Cordillera Hairy-tailed Rat (Batomys granti); 172. Hamiguitan Hairy-tailed Rat (Batomys hamiguitan); 173. Russet Hairy-tailed Rat (Batomys russatus); 174. Mindanao Hairy-tailed Rat (Batomys salomonseni); 175. Mount Isarog Hairy-tailed Rat (Batomys uragon), 176. Black-tailed Dwarf Cloud Rat (Carpomys melanurus); 177. Brown-tailed Dwarf Cloud Rat (Carpomys phaeurus); 178. Dinagat Bushy-tailed Cloud Rat (Crateromys australis); 179. Panay Bushy-tailed Cloud Rat (Crateromys heaneyi), 180. llin Bushy-tailed Cloud Rat (Crateromys paulus); 181. Luzon Bushy-tailed Cloud Rat (Crateromys schadenbergi).
Spectral ANalog of Dwarfs (SAND) model atmospheres and Evolutionary Extension to SAND (SANDee) evolutionary models for low-mass stars and brown dwarfs
<p>Spectral ANalog of Dwarfs (SAND) is a new grid of model atmospheres for low-mass stars and brown dwarfs at a variety of chemical compositions, characteristic of various components of the Milky Way, including the galactic halo and globular clusters. The models were calculated using <strong>PHOENIX 15</strong></p> <p>A detailed description of SAND is available in RNAAS 2024 by Alvarado, Gerasimov, Burgasser, Brooks, Aganze and Theissen <a href="https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..134A/abstract">[ADS]</a></p> <p>Evolutionary Extension to SAND (SANDee) is a new grid of evolutionary models that uses the SAND models for synthetic photometry and as atmosphere boundary conditions. SANDee is the first set of models that can reproduce the observed star/brown dwarf transition in globular clusters. SANDee models were calculated using <strong>MESA 23.05.1</strong></p> <p>A detailed description of SANDee is available in ApJ 2024 by Gerasimov, Bedin, Burgasser, Apai, Nardiello, Alvarado and Anderson <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240501634G/abstract">[ADS]</a></p> <p> </p> <p><strong>Directory structure:</strong></p> <pre><code>SAND.zip : SAND model atmospheres recommended : Subset of SAND atmospheres whose synthetic photometry maintains continuity as a function of temperature rest : The rest of SAND models (see notes on convergence below) SANDee.zip : SANDee evolutionary models BC : MESA atmosphere boundary condition tables for tau=100 at each chemistry MESA : MESA evolutionary models, organized first by chemistry, then by initial mass isogen.zip : Python script to generate model isochrones from SANDee and SAND models isogen.py : The script itself demo.ipynb : Jupyter notebook that demonstrates how the script can be used vega_bohlin_2004.dat : Standard spectrum of Vega for VEGAMAG photometry<br> Other *.dat : Transmission profiles for JWST filters used by the demo notebook custom.patch : Patch with author's changes to the MESA codebase HBL.mrt : Estimated true hydrogen-burning limits for each SAND/SANDee chemistry</code></pre>
Following the Lithium: Tracing Li-bearing Molecules across Age, Mass, and Gravity in Brown Dwarfs
<p>Lithium is an important element for the understanding of ultracool dwarfs because it is lost to fusion at masses above ∼68MJ. Hence, the presence of atomic Li has served as an indicator of the nearby H-burning boundary at about 75MJ between brown dwarfs and very low mass stars. Historically, the “lithium test,” a search for the presence of the Li line at 670.8 nm, has been a marker if an object has a substellar mass. While the Li test could, in principle, be used to distinguish masses of later-type L–T dwarfs, Li is predominantly no longer found as an atomic gas but rather a molecular species such as LiH, LiF, LiOH, and LiCl in cooler atmospheres. The L- and T-type dwarfs are quite faint at 670 nm and thus challenging targets for high-resolution spectroscopy. But only recently have experimental molecular line lists become available for the molecular Li species, allowing molecular Li mass discrimination. Here we generated the latest opacity of these Li-bearing molecules and performed a thermochemical equilibrium atmospheric composition calculation of their abundances. Finally, we computed thermal emission spectra for a series of radiative–convective equilibrium models of cloudy and cloudless brown dwarf atmospheres (with Teff = 500–2400 K and log g = 4.0–5.0) to understand where the presence of atmospheric lithium-bearing species is most easily detected as a function of brown dwarf mass and age. After atomic Li, the best spectral signatures were found to be LiF at 10.5–12.5 μm and LiCl at 14.5–18.5 μm. Also, LiH shows a narrow feature at ∼9.38 μm.</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.