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33 results for “ligo”
Folded data for first three observing runs of Advanced LIGO and Advanced Virgo
<p>This release contains folded datasets from the first three observing runs of Advanced LIGO and Advanced Virgo detectors, which were recently used to perform the broadband directional search (https://dcc.ligo.org/LIGO-P2000500/public) and the all-sky-all-frequency directional search (https://dcc.ligo.org/LIGO-P2100292/public) for a persistent anisotropic gravitational wave background. The DataContent_Readme.md file provides information on the content of the files and how to read these datasets.</p>
Data for LIGO operates with quantum noise below the Standard Quantum Limit
<p>This repository hosts data sets used to create all the figures in the paper "LIGO operates with quantum noise below the Standard Quantum Limit". </p> <p> </p> <p>Version 1: initial upload</p> <p>Version 2: Add README.txt and MCMC set-up code</p>
Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes
<p>This is the data release associated with <strong>Vitale et al <a href="https://arxiv.org/abs/2209.06978">2209.06978</a></strong></p> <p><strong>Samples.zip: </strong>Contains all of the hyper posterior samples for the runs listed in Tables G.1.</p> <p>The files are in json format. Bilby offers a dedicated routine to read them in</p> <p> </p> <blockquote> <p>import bilby<br> data= bilby.core.result.read_in_result(path_to_json)</p> </blockquote> <p> </p> <p>See the <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby documentation </a>for what is contained in the result object. </p> <p>For each run, we report the posterior hyper samples for the mass model, reshift model, spin magnitude model, spin tilt model and merger rate [Gpc^-3 yr^-1]</p> <p>Here the name used to store and a short description of each parameter (Follow the references in the Method section of the paper for a description of each sub-model):</p> <ol> <li>Primary mass model (Power Law + Peak for all runs) <ol> <li>power_law_slope_m1, slope of the primary mass power law component</li> <li>minmass_m1, minimum BH mass</li> <li>maxmass_m1, maximum BH mass</li> <li>low_end_smoothing_m1, smoothing at the low-mass end</li> <li>peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)</li> <li>peak_mean_m1, mean of the Gaussian peak</li> <li>peak_sigma_m1, sigma of the Gaussian peak </li> </ol> </li> <li>Mass ratio model (power law for all runs) <ol> <li>power_law_slope_mass_ratio, slope of the mass ratio </li> </ol> </li> <li>Redshift (power law for all runs) <ol> <li>power_law_slope_redshift, slope of the redshift</li> </ol> </li> <li>Spin magnitude (IID beta distributions for all runs) <ol> <li>alpha_chi, first argument of beta distribution</li> <li>beta_chi, second argument of beta distribution</li> </ol> </li> <li>Cosine of tilt angle <ol> <li>Gaussian models <ol> <li>mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian</li> <li>sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian</li> <li>mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian</li> <li>sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian</li> <li>mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean</li> <li>mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean</li> <li>sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma</li> <li>sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma</li> </ol> </li> <li>Beta models <ol> <li>alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution</li> <li>alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution</li> <li>beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution</li> <li>beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution</li> </ol> </li> <li>Tukey models: <ol> <li>tukey_x0, the center of the Tukey as defined in appendix E of the paper</li> <li>tukey_k, Tk as defined in appendix E of the paper</li> <li>tukey_r, Tk as defined in appendix E of the paper</li> </ol> </li> <li>Branching ratios: <ol> <li>spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component</li> <li>spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component; for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong></li> </ol> </li> </ol> </li> <li>Merger rate <ol> <li>rates, merger rate per unit Gpc cubed per unit year</li> </ol> </li> </ol> <p>Note that some of the parameters for the tilt models might not be used, but still stored (and fixed to - usually - zero). This can be checked by verifying what priors were used for each parameter. For example the <em>Isotropic</em> run was obtained from the <em>Isotropic + Gaussian </em>model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) </p> <blockquote> <p>> data['prior']<br> [...]<br> <strong> 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> </p> </blockquote> <p> </p> <p><strong>Figures.zip:</strong> Contains PDFs for all figures in the paper, plus individual figures for p(costau) and dR/dcostau for each model.</p> <p>Drop me (Salvatore Vitale) an email if anything doesn't work, is missing, or if you spot issues. Thanks! </p> <p> </p>
Response curves butterfly filtering in LIGO O2
<p>Response curves in butterfly filtering to descending chirps in LIGO O2, calibrating output of chi-image analysis of merged (H1,L1)-spectrograms versus injection energies in Extended Emission to GW170817 (distance of 40Mpc). Response curves are shown for characteristic time-scales of frequency descent over 0.5-4.5 seconds for injection energies 0-8%MSolar c2. Results are averaged following times slides -10ms < Delta t < 10ms (light travel-time between the LIGO H1 and L1 detectors) and over three injection sites (one shown in first movie, closest to GW170817EE; all three shown in second movie). </p>
PixelPop: Nonparametric analysis of correlations in the binary black hole population with LIGO–Virgo–KAGRA data
<p>Data release accompanying the PixelPop papers, analyzing gravitational wave populations.</p> <p>The first dataset (in gwtc3_result_files) is the posterior samples for the runs presented in analysis of LIGO--Virgo--KAGRA data, following the third gravitational wave catalog, see https://arxiv.org/abs/2406.16844. We include a python notebook (example_plot.ipynb) showing how to create the plots presented in this paper.</p> <p>In v2, we also include samples from the predictive distributions. Due to the large uncertainties, marginalizing over the hyperposterior may be a poor representation of the inferred distribution, and so instead we provide samples from the <em>median</em> predictive distribution. That is, samples from the distribution shown in the central panels of the figures. </p> <p>The second dataset (in o4inj_result_files) is the posterior samples accompanying the runs presented in the technical background paper, see https://arxiv.org/abs/2406.16813. </p>
Data release of the Swift-LVK subthreshold search during the third LIGO-Virgo-KAGRA observing run
<div> <div>Here we describe the structure of the data release.</div> <br> <div>1. The folder fits contains all the upper limit maps in the for of fits files. Run the code plot_maps_paper.py to obtain the figures 3 and 4 of the paper</div> <br> <div>2. The folder pdf contains all the upperlimit maps, with the GW sky localizations, in pdf format</div> <br> <div>3. The file lum.csv contains the data to reproduce Fig. 6. If the 'cred' colums is empty, then the event is only detected in low latencyl. If 'no' then the event has p_astro<0.5, otherwise it has p_astro>0.5.</div> <br> <div>4. The file joint_far.cvs contains the data to reproduce the Figure 7. Columns are self explanatory</div> <br> <div>5. The file data_BBH.txt, data_BBH_highpastro.txt, data_BBH_allreal.txt, data_BBH_allreal_earth.txt contain the likelihood to produce Figs 8-9-10-11. The plots are produced runnning read.py and read_allreal.py</div> <br> <div>6. All the tables are provided in cvs format</div> </div>
Data Release: "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function"
<p>This dataset contains results presented in "<strong>A neural network emulator of the Advanced LIGO and Advanced Virgo selection function</strong>" (<a href="https://www.arxiv.org/abs/2408.16828">arXiv: 2408.16828</a>).</p> <p>The code used to generate this data and produce figures in the paper can be found at <a href="https://github.com/tcallister/learning-p-det/">https://github.com/tcallister/learning-p-det/</a>. Specific instructions about the workflow are provided in the <a href="https://tcallister.github.io/learning-p-det/">accompanying documentation</a>.</p> <p>The primary deliverable of this work is a trained neural network emulator for the compact binary selection function during the Advanced LIGO and Advanced Virgo O3 observing run. This emulator is made available in a standalone companion repository, <a href="https://github.com/tcallister/pdet">https://github.com/tcallister/pdet</a>.</p> <p>Additional information:</p> <ul> <li>The files <em>endo3_bbhpop-LIGO-T2100113-v12.hdf5</em>, <em>endo3_bnspop-LIGO-T2100113-v12.hdf5</em>, and <em>endo3_nsbhpop-LIGO-T2100113-v12.hdf5</em>, used for network training, were created and released by the LIGO-Virgo-KAGRA Collaboration at <a href="../records/7890437">https://zenodo.org/records/7890437</a>.</li> <li>The file <em>sampleDict_FAR_1_in_1_yr.pickle</em>, used during hierarchical inference, was created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a>.</li> <li>Inference results (<em>popsummary_standardInjections.h5</em> and <em>popsummary_dynamicInjections.h5</em>) are provided in the <em>popsummary</em> results format; see <a href="https://git.ligo.org/christian.adamcewicz/popsummary">https://git.ligo.org/christian.adamcewicz/popsummary</a>.</li> </ul> <p>Changelog:</p> <ul> <li>v2: Added missing file <em>sampleDict_FAR_1_in_1_yr.pickle</em></li> </ul>
Point Absorber Detection in LIGO Test Masses with YOLO
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Movies for Figures in "In LIGO's Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks"
<p>Full resolution videos for Figures 1, 3, 4 and 6 in publication: "In LIGO’s Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks".</p> <p>The movies for Figure 1 (GW_cooled_disk and GW_noncooled_disk) present 3D rendering movie of the density in cooled and non-cooled disks. </p> <p>The movie for Figure 3 (GW_polarizations) shows 3D rendering of the plus polarization in model C.</p> <p>The movie for Figure 4 (Spectrum) shows the evolution of the characteristic strain in the frequency domain in model C.</p> <p>The movie for Figure 6 (Post_merger_disk_GW) presents 3D rendering of density in the post-merger disk.</p>
Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog - Full Posterior Sample Data Release
<p>Data release containing full posterior samples of the following analyses reported in the paper "Tests of General Relativity with Binary Black Holes from the second LIGO–Virgo Gravitational-Wave Transient Catalog" from the LIGO Scientific Collaboration and Virgo Collaboration (<a href="https://doi.org/10.1103/PhysRevD.103.122002">Phys. Rev. D 103, 122002</a>, also available at <a href="https://arxiv.org/abs/2010.14529">arxiv.org:2010.14529</a> and <a href="https://dcc.ligo.org/LIGO-P2000091/public">https://dcc.ligo.org/LIGO-P2000091/public</a>):</p> <ul> <li>Echoes (Sec VII B): ech.zip</li> <li>Inspiral-merger-ringdown consistency test (Sec IV B): imr.zip</li> <li>Lorentz invariance violation test (Sec V I): liv.zip</li> <li>Parametrized tests of general relativity (Sec V A): par.zip</li> <li>Ringdown test (Sec VII A): rin.zip</li> <li>Spin-induced quadrupole moment test (Sec V B): sim.zip</li> </ul> <p>Each zip file contains HDF5 files that can either be read directly with standard HDF5 tools, or using PESummary (<a href="https://docs.ligo.org/lscsoft/pesummary/">https://docs.ligo.org/lscsoft/pesummary/</a>)</p> <p> </p>
Data release: Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors
<p>This contains the data release associated to "Parameterised population models of transient non-Gaussian noise in the LIGO gravitational-wave detectors".</p> <p>We provide the figures, machine-readable json summary files associated to Tables I-IV, scripts and data products used to produce the hyperparameter inference results in this publicatioln. A "lightweight" version is provided which excludes the pickled data products. To reproduce the results, download the full tar file, unzip, enter the scripts directory, and use the Makefile commands. These results where created using bilby v1.1.3 at commit hash <a href="https://git.ligo.org/lscsoft/bilby/-/commit/63c7aacaf30d721e77599bd11f3a9fa2447915cb">63c7aaca</a>.</p>
Movies for Figure 1 in "In LIGO's Sight? Vigorous Coherent Gravitational Waves from Cooled Collapsar Disks, (#AAS56281R1)"
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Analysis of Subthreshold Binary Black Hole Merger Candidates in LIGO O1 and O2 Observing Runs
<p>Posterior datasets and figure creation code for "Analysis of Subthreshold Binary Black Hole Merger Candidates in LIGO O1 and O2 Observing Runs". </p> <p>Abstract: </p> <p>This study uses data from the LIGO Hanford and Livingston detectors (Abbott et al. 2019) to investigate binary black hole (BBH) merger candidates. 19 candidates were reviewed in total, each having been proposed by either the Institute of Advanced Study (Venumadhav et al. 2019; Zackay et al. 2019a) or the second Open Gravitational-wave catalog (Nitz et al. 2019). Triggers were categorised as significant or insignificant based on studies of posterior distribution consistency with the original detections, coherence tests, and q-transforms. Of the 19 candidates, 14 were identified as significant. Further analysis attempts to identify the formation models that produced each BBH event, such as primordial or hierarchical models. Spin population analysis was one such method used. The method implemented for spin population analysis favoured an isotropic distribution of the effective spin, which in turn could cause the results to favour hierarchical merging. This could offer some explanation to the four candidates identified in the mass gap, the range of masses for which no black holes have been detected, which was proposed by Fishbach & Holz (2017) and has so far been confirmed by LIGO.GW170304 and GW170425, as well as GW170123 and GW170727, warrant further investigation as they could be strongly lensed by a galaxy or galaxy cluster, due to similarities in the calculated parameters.GW170104A requires further investigation as q-transforms showed signal in both detectors, indicating the trigger is of astrophysical origin; however, the IMRPhenomv2 waveform has not correctly modelled the signal.</p>
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