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28 results for “posterior samples”
Single-pulsar search for eccentric SMBHBs using NANOGrav 12.5-year data of PSR J1909--3744: Posterior samples
<p>This repository contains posterior samples for a Bayesian single-pulsar search for nanohertz gravitational waves originating from eccentric supermassive binaries, done using the NANOGrav 12.5-year dataset for PSR J1909-3744. The analysis is presented in Susobhanan 2023 [https://arxiv.org/abs/2210.11454].</p>
Posterior samples of a dynamical fit for planetary system HD128311
<p>This dataset contains the posterior samples from a dynamical fit of the multi-planetary system HD128311. See <a href="https://doi.org/10.1093/mnrasl/slu202">https://doi.org/10.1093/mnrasl/slu202</a> for a description of the fit. The dataset is one numpy array, consisting of</p> <ul> <li>500 walkers </li> <li>1250 generations</li> <li>3 bodies (1 star + 2 planets)</li> <li>7 parameters per body (mass, x, y, z, vx, vy, vz)</li> </ul>
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter: Neutron star equation of state posterior samples
<p>Equation of state posterior samples associated with Legred et al., "Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter," Phys. Rev. D 104, 063003 (2021); doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p>Three sets of 1e4 samples from the posterior distribution over equations of state are provided. These sets are drawn from the posterior conditioned on different combinations of radio pulsar observations, gravitational wave data, and NICER x-ray measurements. The data release contains the equation of state table and the corresponding table of neutron star observables for each sample. The posterior distributions one can generate from these samples approximate those plotted in Figs. 1-6 of the accompanying paper.</p> <p> </p> <p>Refer to the readme for usage information.</p>
Posterior PDF samples for flicker model parameters
<p>h5py files containing posterior PDF samples of the flicker-logg-rho_star relations described in the associated paper (submitted).</p>
Updated posterior PDF samples for flicker model parameters
<p>Updated posterior PDF samples for for the model parameters of the flicker-logg-rho_star relations. </p>
Constraining the dense matter equation of state with new NICER mass-radius measurements and new chiral effective field theory constraints: prior and posterior samples and scripts for generating plots
<p>Full reproduction package accompanying the paper: <em>Constraining the dense matter equation of state with new NICER mass-radius measurements and </em><em>new chiral effective field theory inputs</em></p> <p> </p> <p><em>*Note, the changes made from version to version are made visible in the CHANGELOG.rst file</em></p>
gwastro/o2-bbh-pe: v2.2 data release of O2 Binary Black Hole posterior samples
<p>This is the v2.2 data release associated with the parameter estimation analysis of the binary black-hole signals from Advanced LIGO-Virgo's second observing run, using the PyCBC Inference toolkit : <a href="https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b">https://iopscience.iop.org/article/10.1088/1538-3873/aaef0b</a> . A companion paper presenting our parameter estimation analysis and the data release is available here : <a href="https://arxiv.org/pdf/1811.09232.pdf">https://arxiv.org/pdf/1811.09232.pdf</a>.</p> <p>The analysis was performed using the PyCBC v1.12.3 code on the gravitational-wave data available at <a href="https://www.gw-openscience.org/catalog/GWTC-1-confident/html/">https://www.gw-openscience.org/catalog/GWTC-1-confident/html/</a> . Descriptions of the gravitational-wave data can be found in the paper <a href="https://arxiv.org/abs/1811.12907">https://arxiv.org/abs/1811.12907</a> .</p> <p>The changes in this release are</p> <ul> <li>An update to the plotting code in <code>data_release_o2_bbh_pe.ipynb</code> for generating Figs. 1, 2, and 3 in the companion paper to take into account cases where a boundary bias may be introduced for plotting probability contours.</li> <li>Addition of a plotting code in <code>data_release_o2_bbh_pe.ipynb</code> that generates a corner plot showing estimates (median and 90% credible interval) and posterior distributions for all the parameters presented in Table 1 of the companion paper.</li> <li>Addition of a notebook <code>o2_bbh_pe_skymaps.ipynb</code> that demonstrates the method for visualizing sky location posteriors as presented in Fig. 4 of the manuscript.</li> </ul> <p>The data and configuration files included remain the same as in the v2.1 release.</p> <p>This release includes :</p> <ul> <li>posterior and prior samples from parameter estimation analyses of the seven binary black-hole events---GW170104, GW170608, GW170729, GW170809, GW170814, GW170818, and GW170823.</li> <li>PSDs used in each of the analyses</li> <li>configuration files and run scripts for running the analyses and generating the data.</li> <li>tutorials for manipulating the data and reconstructing the figures in the companion paper.</li> </ul>
Frasnian, lateral (B1), ventral (B2), anterior (B3), posterior (B4), and dorsal (B5) views of a rounded exfoliated shell, 27.7 mm wide, 25.6 mm long, and about 14.5 mm thick. C. PUM05008, sample PY4, Panxi section, probably Middle Frasnian, lateral (C1), dorsal (C2), and ventral (C3) views of the sectioned specimen (Fig. 6). D. PUM05009, sample PY5, Panxi section, probably Middle Frasnian, ventral beak broken, showing small conjunct deltidial plates (note that true foramen (approximately dashed line) takes up only a small part at the bottom of the seen later enlarged hole). E. PUM05010, sample PY5, Panxi section, probably Middle Frasnian, posterior (E1), lateral (E2), anterior (E3), ventral (E4), and dorsal (E5) views, 26.7 mm wide, 27.7 mm long, 18.5 mm thick, adpressed ventral beak. F. PUM05011, sample GC22, Caiziyan section, Early Frasnian, dorsal view. in Early and Middle Frasnian brachiopod faunas and turnover on the South China shelf
Frasnian, lateral (B1), ventral (B2), anterior (B3), posterior (B4), and dorsal (B5) views of a rounded exfoliated shell, 27.7 mm wide, 25.6 mm long, and about 14.5 mm thick. C. PUM05008, sample PY4, Panxi section, probably Middle Frasnian, lateral (C1), dorsal (C2), and ventral (C3) views of the sectioned specimen (Fig. 6). D. PUM05009, sample PY5, Panxi section, probably Middle Frasnian, ventral beak broken, showing small conjunct deltidial plates (note that true foramen (approximately dashed line) takes up only a small part at the bottom of the seen later enlarged hole). E. PUM05010, sample PY5, Panxi section, probably Middle Frasnian, posterior (E1), lateral (E2), anterior (E3), ventral (E4), and dorsal (E5) views, 26.7 mm wide, 27.7 mm long, 18.5 mm thick, adpressed ventral beak. F. PUM05011, sample GC22, Caiziyan section, Early Frasnian, dorsal view.
Quasi-periodic Gaussian process rotation period posterior samples of Kepler light curves.
<p>.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017). </p> <p>koi_results_02_15.tgz contains posterior samples for 1132 Kepler objects of interest.</p> <p>results_acfprior_02_16.tgz contains posterior samples for 998 simulated light curves (previously published in Aigrain et al., 2015: https://arxiv.org/abs/1504.04029) with an ACF-based prior.</p> <p>results_noprior_02_16.tgz contains posterior samples for 997 simulated light curves (previously published in Aigrain et al., 2015) with an uninformative prior.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("<filename>", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an uninformative prior) for 998 simulated Kepler light curves.
<p>997.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>997 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an ACF prior) for 998 simulated Kepler light curves.
<p>998.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>998 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p>
Deep Lensing Posterior Samples
<p>This archive contains the main results of <em>Probabilistic Mass Mapping with Neural Score Estimation</em> (Remy et al. 2022).</p> <p>It consists of convergence posterior samples computed with our method from different fields (HSC/ACS COSMOS, mocked shear field computed from \kappaTNG map, mocked shear field with added NFW cluster).</p> <p>It also contains the trained neural network weights we used.</p> <p>The details of the creation of this dataset and how to use it to reproduce the plots of the paper can be found at https://github.com/cosmoStat/jax-lensing .</p>
Posterior samples for "Searching for a Ringdown Overtone in GW150914"
<p>Posterior files associated with <em>Searching for a Ringdown Overtone in GW150914 </em>(<a href="https://arxiv.org/abs/2205.07809">arXiv:2205.07809</a>, <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.043005">Phys. Rev. D 106, 043005</a>).</p> <p>We provide samples for both the GW150914 analysis (<code>posterior_samples/GW150914</code>) and the injection campaign (<code>posterior_samples/injection_campaign</code>). For the GW150914 analysis, where multiple models were used, the folder naming follows the convention:</p> <p><code>{number of wavelets included in the model}W{QNM content described by lmn indices}</code></p> <p>with the kerr-deviation analysis having the additional label <code>_with_deviation</code>.</p> <p>Only the <code>3W220221</code> model was used in the injection campaign. There, subfolders are named according to the injection time in seconds (where <code>m</code> indicates a minus value, relative to t_ref as detailed in the paper). The exception is the noiseless injection, which is in the <code>noiseless</code> folder.</p> <p>Each directory contains a <code>posterior_samples.dat</code> file and a <code>sampler_output.json</code> file which contains the log-evidence (natural log) and the estimated error on the log-evidence.</p> <p>Notebooks to reproduce the figures in the paper are available in the <code>plots</code> folder.<br> </p>
Posterior samples for "Frequency-Domain Analysis of Black-Hole Ringdowns"
<p>Posterior files associated with <em>Frequency-Domain Analysis of Black-Hole Ringdowns</em> (<a href="http://arxiv.org/abs/2108.09344">arxiv:2108.09344</a>, <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.104.123034">Phys. Rev. D <strong>104</strong>, 123034</a>).</p> <p>Folder naming:</p> <ul> <li>{event name} <ul> <li>SNR{injection signal-to-noise ratio} <ul> <li>frequency_domain <ul> <li>{number of wavelets included in the model}W{QNM content described by lmn indices} <ul> <li>{ringdown start time in ms}_{ringdown start time prior width in ms}</li> </ul> </li> </ul> </li> <li>time_domain <ul> <li>{QNM content described by lmn indices} <ul> <li>{ringdown start time in ms}_{ringdown start time prior width in ms}</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p>For example, the file </p> <p>GW190521/SNR15/frequency_domain/1W220/12-7_01/posterior_samples.dat</p> <p>contains the results of a frequency-domain analysis of the GW190521-like injection (at signal-to-noise ratio 15) using <span class="math-tex">\(W=1\)</span> wavelet with just the fundamental <span class="math-tex">\(\ell = m = 2\)</span>, <span class="math-tex">\(n=0\)</span> QNM. It has a Gaussian prior on the ringdown start time centered 12.7 ms after the time of peak strain, with a width of 1 ms.</p> <p>The special-case two-interferometer analysis with Virgo excluded has "_HL" appended to its folder name.</p> <p>Each directory contains a 'posterior_samples.dat' file (see the 'plot_example.ipynb' for more information on reading the posterior files) and a 'sampler_output.json' which contains the log-evidence and the estimated error on the log-evidence.</p>
Data from: Bayesian analyses in phylogenetic palaeontology: interpreting the posterior sample
<p>Establishing hypotheses of relationships is a critical prerequisite for any macroevolutionary analysis, but different approaches exist for achieving this goal. Amongst palaeontologists using morphological data the Bayesian approach is increasingly preferred over parsimony, but this shift also alters the way we think about samples of trees. Here we revisit stratigraphic congruence as a comparator between Bayesian and parsimony samples, but in a new visual context: treespace. Such spaces represent an ordination of unique topologies that can also be extended to create a "landscape" where altitude represents some comparative measure (here congruence with stratigraphy). By co-opting existing visualization tools and applying them to a meta-analysis of 128 cladistic data sets we show that there is no consistent favouring of either Bayesian or parsimony according to stratigraphic congruence metrics, and further that empirical treespace visualizations suggest a complex variety of topological landscapes. We conclude by arguing that treespaces should become a standard exploratory tool in phylogenetic analysis.</p>
GW190521: posterior samples conditional on AGN J1249+3449
<p>Posterior samples for the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> conditional on the location of AGN J1249+3449. This dataset contains a file with results fixing the sky location to J1249+3449 (<em>posterior_samples_GW190521_J1249+3449.h5</em>), and another additionally fixing the luminosity distance to 2511 Mpc (<em>posterior_samples_GW190521_J1249+3449_2511Mpc.h5</em>). These analyses are motivated by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posteriors were sampled using LALInference (<a href="https://arxiv.org/abs/1409.7215">Veitch et al 2015</a>) with the same configuration as in the LIGO-Virgo analyses (<a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.101102">Abbott et al 2020 PRL</a> and <a href="https://iopscience.iop.org/article/10.3847/2041-8213/aba493">Abbott et al 2020 ApJL</a>). The results were packaged using <a href="https://arxiv.org/abs/2006.06639">PESummary</a>.</p> <p>For instructions on how to load the result files, please see the PESummary <a href="https://docs.ligo.org/lscsoft/pesummary/igwn_pinned/core/read_pesummary.html">documentation</a>.</p>
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants. in Phylogenetic approach for identification and life cycles of Puccinia (Pucciniaceae) species on Poaceae from northeastern China
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants.
Text-fig. 7. Scatter diagram of molar length against the ratio anterior width / posterior width of upper molars of S. giganteus from Keseköy. in An Exceptional Large Sample Of The Early Miocene Ctenodactyline Rodent Sayimys Giganteus, Specific Variation And Taxonomic Implications
Text-fig. 7. Scatter diagram of molar length against the ratio anterior width / posterior width of upper molars of S. giganteus from Keseköy.
Contribution of Posterior Corneal Astigmatism to Total Corneal Astigmatism in a Sample of Egyptian Population
ClinicalTrials.gov study NCT04393740. IPD Sharing: YES. Countries: 1. Publications: 2.
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