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Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data
Open the record for dataset details and reuse information.
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>
Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon
<p>In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils.</p> <p>Kemppinen, Niittynen, Virkkala, Happonen, Riihimäki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems.</p> <p>These are the data from Kemppinen et al. (2021).</p>
Red-optical spectra of three nearby M dwarfs with SALT HRS
<p>We conducted observations of three nearby mid-M dwarfs with the High-Resolution Spectrograph (HRS) at the Southern African Large Telescope (SALT, DDT proposal code: 2019-2-DDT-006). We obtained spectra in its red arm over a wavelength range of 5,500-8,900 Angström with a spectral resolution of about 40,000 in medium-resolution mode. The observations were carried out on February 08 and February 09, 2020. The data were reduced with the PEPSI data reduction software (Strassmeier et al. 2018). The reduction followed the standard steps of bias overscan detection and subtraction, scattered light extraction from the inter-order space and subtraction, definition of échelle orders, optimal extraction of spectral orders, wavelength calibration, and a self-consistent continuum fit to the full two-dimensional (2D) image of extracted orders.</p> <p><strong>Files in this dataset</strong></p> <p>salt1.txt: TIC 44984200 (2MASS J08380224-5855583)<br> salt2.txt: TIC 277539431 (2MASS J10551532-7356091)<br> salt3.txt: TIC 300741820 (2MASS J07404497-6648318)</p> <p><strong>Columns in each file from left to right:</strong></p> <p>1 Wavelength in Angstrom<br> 2 Normalized flux<br> 3 Uncertainty on normalized flux</p> <p><strong>Corresponding author</strong></p> <p>Ekaterina Ilin, eilin@aip.de, Leibniz Institute for Astrophysics Potsdam (AIP)</p>
Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs, in field and laboratory.
<p>The manuscript entitled "Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs" used two set of data: Field data and Laboratory data</p> <p>##### 1. FIELD DATA:<br> We measured photosynthesis and non-structural carbohydrate (NSC) content in adult Scots pine (Pinus sylvestris L.), in boreal conditions at Hyytiaälä SMEAR II station in Sourthen Finland. In the folder "Field Data", you will find automatic CO2 exchange measurements by shoot chambers, dynamic parameters for the light response of photosynthesis, and needles´ non-structural carbohydrate content (NSC) in 2008, 2009 and 2015. See the readme file in the folder for further information.</p> <p> </p> <p>#### 2. LABORATORY DATA</p> <p>We measured the relationship between photosynthesis and non-structural carbohydrate (NSC) content under stable laboratory conditions in three shrubs species:<br> i) evergreen lingonberry (Vaccinium vitis-idaea L.),<br> ii) evergreen heather (Calluna vulgaris (L.) Hull) and<br> iii) deciduous bilberry (Vaccinium myrtillus L.).<br> The plants grew in chambers where we measured the CO2 gas exchange and estimated photosynthesis. After CO2 gas exhcnage measurements we sampled the leaves for NSC analyses. See the readme file in the folder for further information.</p> <p> </p> <p> </p> <p> </p>
Mansar, Maharashtra, India. Dwarf Śiva, fifth century.
<p>Mansar, Maharashtra, India. Śiva, fifth century. Now National Museum of India. Photograph 1980; digitisation 2016.</p>
Gaia Catalogue of Synthetic Photometry - White Dwarfs (GCSP-WD)
<p>GSPC-WD catalogue </p> <p>This catalogue contains objects described in detail in Gaia Collaboration, Montegriffo et al., 2022, A&A, in press. The description of the catalogue from the paper is given below.</p> <p>We have made the GSPC-WD synthetic photometry available<br> as a stand-alone catalogue27, including SDSS, JKC and JPLUS<br> XPSP and the DA classification probability. The photometry<br> of the individual J-PAS bands, used in the random forest<br> analysis, is not included due to their low signal-to-noise. For<br> WDs classified in SDSS, a subset of which were used in the<br> training/validation of the random forest algorithim, we also include<br> the full SDSS classifications as a separate column in the<br> GSPC-WD catalogue table. When the synthetic spectral bands are very narrow a<br> significant number of sources will have low signal-to-noise. Furthermore,<br> at the edges of the Gaia spectral range, away from<br> the peak of the effective area, this is also true for some stars<br> in the wider bands included in the catalogue. In some extreme<br> cases, there is no significant detection of the object. The random<br> forest algorithm is only able to classify a WD when valid<br> flux measurements are available for every photometric band we<br> include in the analysis. Therefore, no classification is recorded<br> in the catalogue when data for one or more bands is "missing".<br> In total 15,003 WDs from the total sample of 101,783 are not<br> classified. For completeness, we have made all the flux measurements<br> and corresponding magnitudes available for all objects in<br> the GSPC-WD. Hence magnitude/fluxes with very large errors,<br> up to several times the flux itself, are included. However, where<br> fluxes are negative, the magnitudes are not defined. When using<br> the catalogue, appropriate signal-to-noise cuts are advisable for<br> the specific work in-hand, to ensure data quality.</p> <p>Total number of objects = 101,786; Format 1 object per row, 73 columns of data as listed below.</p> <p>Column Description of contents<br> 1 Gaia source_id<br> 2 ra<br> 3 ra_error<br> 4 dec<br> 5 dec_error<br> 6 JohnsonStd_mag_U<br> 7 JohnsonStd_mag_B<br> 8 JohnsonStd_mag_V<br> 9 JohnsonStd_mag_R<br> 10 JohnsonStd_mag_I<br> 11 JohnsonStd_flux_U<br> 12 JohnsonStd_flux_B<br> 13 JohnsonStd_flux_V<br> 14 JohnsonStd_flux_R<br> 15 JohnsonStd_flux_I<br> 16 JohnsonStd_flux_error_U<br> 17 JohnsonStd_flux_error_B<br> 18 JohnsonStd_flux_error_V<br> 19 JohnsonStd_flux_error_R<br> 20 JohnsonStd_flux_error_I<br> 21 SdssStd_mag_u<br> 22 SdssStd_mag_g<br> 23 SdssStd_mag_r<br> 24 SdssStd_mag_i<br> 25 SdssStd_mag_z<br> 26 SdssStd_flux_u<br> 27 SdssStd_flux_g<br> 28 SdssStd_flux_r<br> 29 SdssStd_flux_i<br> 30 SdssStd_flux_z<br> 31 SdssStd_flux_error_u<br> 32 SdssStd_flux_error_g<br> 33 SdssStd_flux_error_r<br> 34 SdssStd_flux_error_i<br> 35 SdssStd_flux_error_z<br> 36 Jplus_mag_uJAVA<br> 37 Jplus_mag_J0378<br> 38 Jplus_mag_J0395<br> 39 Jplus_mag_J0410<br> 40 Jplus_mag_J0430<br> 41 Jplus_mag_gJPLUS<br> 42 Jplus_mag_J0515<br> 43 Jplus_mag_rJPLUS<br> 44 Jplus_mag_J0660<br> 45 Jplus_mag_iJPLUS<br> 46 Jplus_mag_J0861<br> 47 Jplus_mag_zJPLUS<br> 48 Jplus_flux_uJAVA<br> 49 Jplus_flux_J0378<br> 50 Jplus_flux_J0395<br> 51 Jplus_flux_J0410<br> 52 Jplus_flux_J0430<br> 53 Jplus_flux_gJPLUS<br> 54 Jplus_flux_J0515<br> 55 Jplus_flux_rJPLUS<br> 56 Jplus_flux_J0660<br> 57 Jplus_flux_iJPLUS<br> 58 Jplus_flux_J0861<br> 59 Jplus_flux_zJPLUS<br> 60 Jplus_flux_error_uJAVA<br> 61 Jplus_flux_error_J0378<br> 62 Jplus_flux_error_J0395<br> 63 Jplus_flux_error_J0410<br> 64 Jplus_flux_error_J0430<br> 65 Jplus_flux_error_gJPLUS<br> 66 Jplus_flux_error_J0515<br> 67 Jplus_flux_error_rJPLUS<br> 68 Jplus_flux_error_J0660<br> 69 Jplus_flux_error_iJPLUS<br> 70 Jplus_flux_error_J0861<br> 71 Jplus_flux_error_zJPLUS<br> 72 probability DA<br> 73 SDSS WD type</p>
Earthquake Catalogues for DWARFS (Dense Westland Arrays Researching Fault Segmentation)
<p>This dataset contains earthquake hypocentral information catalogued as part of the DWARFS (Dense Westland Arrays Researching Fault Segmentation) broadband seismometer networks along New Zealand's Alpine Fault, between April 2019-April 2020.</p> <p>'Preferred Lat/Lon/Depth' refers to origin determined by method under 'Method'. HypoDD is the preferred method, but some origins could not be relocated and so we present the NonLinLoc derived origin instead. All magnitudes are Local magnitudes calculated using displacements on the vertical channel (MLv). All times are in UTC time. </p> <p>This dataset accompanies a publication recently submitted (July 2022) to the AGU journal 'Journal of Geophysical Research: Solid Earth' entitled 'Heterogeneity in microseismicity and stress near rupture-limiting section boundaries along the late interseismic Alpine Fault'. </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>
Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample
<p>Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs. </p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</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>
Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»
<p>A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct <em>Globorotalia multicamerata</em> from the <em>G. menardii</em> stock via the intermediate form <em>G. limbata</em> between about 6 Ma to 5 Ma ago. After splitting both <em>G. limbata</em> and <em>G. multicamerata</em> show gradual divergence from <em>G. menardii</em> in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta<sup>18</sup>O trends between species, that populated the mixed layer (<em>Globigerinoides sacculifer</em>) and the thermocline layer (<em>Neogloboquadrina dutertrei</em>) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification.</p> <p>The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.</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>
Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo
<p>m2fs_HiRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellan/M2FS spectrograph in HiRes configuration</p> <p>m2fs_MedRes_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the Magellen/M2FS spectrograph in MedRes configuration</p> <p>hecto_catalog_public.fits: public catalog of measurements derived from spectroscopic observations of individual targets with the MMT/Hectochelle spectrograph</p> <p>fits_files.tar.gz: Supplementary data products, including all sky-subtracted spectra from individual targets and best-fitting model spectra.</p> <p>template_spectra.tar.gz: synthetic template spectra (columns are wavelength in air (Angstroms), normalized flux)</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.