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Dataset results
11 results for “21 cm”
On the Constraints on Superconducting Cosmic Strings from 21-cm Cosmology (supplementary inference products)
<p>These are the nested sampling inference products that were used to compute the results for <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a>.</p> <p>The python script, and utility functions, required to produce most of the figures in the paper are included to demonstrate usage. Plotting script for functional posteriors are not included as these require emulators that are not part of this data release. </p> <p>All the included chains were computed using <a href="https://github.com/PolyChord/PolyChordLite">PolyChordLite</a> .</p> <p>Chain foldername conventions:</p> <ul> <li>HERA: Constraints from HERA Phase I 21-cm power spectrum upper limits</li> <li>SARAS_3: Constraints from the SARAS 3 21-cm global signal null detecetion</li> <li>Xray_Background: Constraints from collated measurements of the unresolved X-ray background</li> <li>HERA_SARAS_3_Xray_Background: Joint analysis of the above</li> </ul> <p>Chains have rootnames that are of the form 'foldername_constraints'. See <a href="https://arxiv.org/abs/2312.08828">arXiv:2312.08828</a> for additional details on each of the model parameters. </p> <p>Software used: <a href="https://numpy.org/">numpy</a>, <a href="https://pandas.pydata.org/">pandas</a>, <a href="https://pyyaml.org/">pyYAML</a>, <a href="https://scipy.org/">scipy</a>, <a href="https://github.com/htjb/globalemu">globalemu</a>, <a href="https://matplotlib.org/stable/">matplotlib</a>, <a href="https://www.tensorflow.org/">tensorflow</a>, <a href="https://scikit-learn.org/stable/">scikit-learn</a>, <a href="https://joblib.readthedocs.io/en/stable/">joblib</a>, <a href="https://github.com/PolyChord/PolyChordLite">pypolychord</a>, <a href="https://github.com/handley-lab/anesthetic">anesthetic</a>,<a href="https://github.com/HERA-Team/hera_pspec"> hera-pspec</a>, <a href="https://seaborn.pydata.org/">seaborn</a>, <a href="https://github.com/htjb/margarine">margarine</a>, <a href="https://github.com/handley-lab/fgivenx">fgivenx</a>, <a href="https://github.com/tqdm/tqdm">tqdm</a></p> <p>Exact software versions are specified in an included requirements.txt file for reproducibility. </p>
Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay
<p>This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm. </p>
21 cm intensity mapping: a 900 - 1300 MHz full-sky simulation
<p>This HDF5 file stores the simulation used and described in <a href="http://dx.doi.org/10.1093/mnras/staa2854">Recovery of 21 cm intensity maps with sparse component separation</a> (<a href="https://arxiv.org/abs/2006.05996">arXiv:2006.05996</a>). It is a collection of intensity maps in units of mK, full-sky, in HEALPIX format with nside=256. There are 5 components: 1. 21cm cosmological signal, 2. galactic free-free diffuse emission, 3. galactic synchrotron diffuse emission, 4. extragalactic point sources and background and 5. instrumental polarization leakage. For each component, there are 400 maps, corresponding to 400 channels of 1 MHz from 900 to 1300 MHz. In <a href="https://doi.org/10.1093/mnras/staa2854">the main paper</a> the simulation is used to test the algorithm GMCA as a foreground removal method for 21cm intensity mapping. A practical example of how to use these data and apply GMCA on them is shown at this GitHub <a href="https://github.com/isab3lla/gmca4im">repository</a>.</p> <p>If you are using this dataset or the tools developed to work with it, please do not forget to cite the dataset itself as well as the relevant publication.</p>
What can we learn about transients from the 21 cm line emission in their environments?
<p>I present the result of an observational campaign to map the 21 cm atomic hydrogen line in host galaxies of supernovae (SNe), long gamma-ray bursts (GRBs), and fast radio bursts (FRBs). For all analysed hosts of type Ic-BL SNe and GRBs we found off-centre gas concentrations close to the GRB/SN positions and irregular velocity fields. This suggests a recent gas inflow. This is consistent with GRB/SN progenitors being born when a galaxy accretes metal-poor gas from the intergalactic medium, and opens a possibility to use GRB/SN hosts to study gas accretion. This supports a very massive model for their progenitors. Similarly, the host galaxies of FRBs exhibit very asymmetric 21 cm lines, suggesting a connection between the birth of FRB progenitors and galaxy interactions. On the other hand, the host galaxy of the unusual transient AT 2018cow (the first fast blue optical transient, FBOT) does not exhibit such features, which suggests that its progenitor may not have been a massive star. </p>
Galactic maps of HI, reconstructed from the HI4PI 21-cm line survey
<p>Maps of galactic HI, reconstructed from the HI4PI 21-cm line survey data (HI4PI Collaboration 2016, A&A 594, A116), <a href="http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/585/A41">Strasbourg astronomical Data Center</a>, using a Bayesian variational inference approach.</p> <p>Here, we provide the mean and standard deviation of the gas density (in cm<sup>-3</sup>) on a three-dimensional 768<span class="math-tex">\(\times\)</span>768<span class="math-tex">\(\times\)</span>64 grid, spanning -24 kpc < x < 24 kpc, -24 kpc < y < 24 kpc and -2 kpc < z < 2 kpc, both for the BEG03 and the SBM15 gas flow models:</p> <ul> <li> <p>HI_dens_mean_BEG03.fits</p> </li> <li> <p>HI_dens_stddev_BEG03.fits</p> </li> <li> <p>HI_dens_mean_SBM15.fits</p> </li> <li> <p>HI_dens_stddev_SBM15.fits</p> </li> </ul> <p>In addition, we also publish the mean and standard deviation of the mass surface density (in M<sub>sun</sub> pc<sup>-2</sup>) on a two-dimensional 768<span class="math-tex">\(\times\)</span>768 grid, spanning -24 kpc < x < 24 kpc and -24 kpc < y < 24 kpc, again for both gas flow models:</p> <ul> <li> <p>HI_surf_mass_dens_mean_BEG03.fits</p> </li> <li> <p>HI_surf_mass_dens_stddev_BEG03.fits</p> </li> <li> <p>HI_surf_mass_dens_mean_SBM15.fits</p> </li> <li> <p>HI_surf_mass_dens_stddev_SBM15.fits</p> </li> </ul> <p>Throughout, we have assumed the optically thin limit for the gas and adopted the conversion factor <span class="math-tex">\(X_{{\rm HI}}\simeq 1.823\times 10^{18}\)</span> molecules cm<sup>-2</sup> (K km s<sup>-1</sup>)<sup>-1</sup>. Refer to the FITS headers for more detailed information and see <a href="https://arxiv.org/abs/2202.02341">arXiv:2202.02341</a> for a detailed description of the reconstruction as well as a comparison with earlier studies.</p> <p>When using the deprojected gas maps in your research work, please refer to this Zenodo dataset and the following publication<br> P. Mertsch and V.H.M. Phan, "Bayesian inference of three-dimensional gas maps: II. Galactic HI", <a href="https://arxiv.org/abs/2202.02341">arXiv:2202.02341</a>.</p>
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm.
Informing antenna design for Global 21-cm experiments using a simulated Bayesian data analysis pipeline (supplementary data)
<p>These are the posterior files, foreground simulation data sets and chromaticity factor values used to produce the results for <a href="https://arxiv.org/abs/2106.10193">arXiv:2106.10193</a>.</p> <p>Plots of the fitted signal and residuals for each case are included, as is a plotting function to reproduce key figures.</p> <p>Naming conventions:</p> <ul> <li>f0: Centre frequency of the 21cm signal present in the simulated data</li> <li>A: Amplitude of the simulated 21cm signal present in the simulated data</li> <li>M_sig: Model being fit to the data includes a 21cm signal</li> <li>M_nosig: Model being fit to the data is a foreground only</li> </ul> <p>Software used:</p> <ul> <li><a href="https://github.com/PolyChord/PolyChordLite/tree/839292290a7747dbee82933bb9f7f955ac45c3ca">PolyChord</a></li> </ul> <p> </p>
Simulation Data of a Model with Ionospheric Effects for Global 21-cm Experiments
<p>The files contain integrated antenna temperature of two different models in binary.</p>
Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments data
<p>Nested sampling posterior samples when a systematic structure is present inside the data.</p> <p>The data is organised in the following way:</p> <p>Folder structure: [likelihood used]/[systematic amplitude]/[type of systematic]/</p> <p>Result structure: [Noise Model used]_[likelihood used]_sys_[systematic amplitude]_[systematic period]_[systematic phase]_[G21 signal strength]</p> <p>If damped systematic, add _damp at result string.</p> <p>Within the results folder, the "enhance" folder contains the more accurate posterior samples of the enhanced PolyChord run.</p> <p>The third folder [systematicModelled] contains the results when the systematic structure is modelled with the same results string as above.</p> <p> </p>
Bayesian evidence-driven likelihood selection for sky-averaged 21-cm signal extraction data
<p>Nested sampling posterior samples of different noise distributions with the following pattern:</p> <p>[Noise Model]_[Likelihood used]_[Noise parameter]_[Noise parameter value]</p> <p>The enhanced/ folders contain the more accurate posterior samples of the enhanced run of PolyChord.</p>
Figure 21 from: Denchev TT, Knudsen H, Denchev CM (2020) The smut fungi of Greenland. MycoKeys 64: 1-164. https://doi.org/10.3897/mycokeys.64.47380
Figure 21 Microbotryum arcticum on Silene uralensis subsp. arctica. A Habit (holotype) B, C spores in LM, in median and surface view, respectively (holotype) D–F spores in SEM (D leg. P. Frykman & B. Fredskild, s.n., C-Greenland herb., s.n.; E, F SOMF 29 998) G distribution map. – A, D, E reproduced, with permission, from Denchev et al. 2019. Scale bars: 0.5 cm (A); 10 µm (B, C); 5 µm (D–F); 500 km (G).
ScienceDex guides
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OpenNeuro
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