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22 results for “Radial velocity”
Gaia EDR3 Catalogs of Machine-Learned Radial Velocities
<p><strong>Gaia EDR3 Catalogs of Machine-Learned Radial Velocities</strong></p> <p>Spatially complete Test-Set and Machine-Learned Radial Velocity (ML-RV) Catalogs described in Dropulic et al., arXiv:<a href="https://arxiv.org/abs/2205.12278">2205.12278</a>. The spatially complete Test-Set Catalog contains a total of 4,332,657 stars, while the spatially complete ML-RV Catalog contains 91,840,346 stars. We provide Gaia EDR3 Source IDs, the network-predicted line-of-sight velocity in km/s, and the network-predicted uncertainty in km/s. </p> <p>We have included a simple Jupyter notebook demonstrating how to import the data, and make a simple histogram with it.</p> <p>If you find this catalog useful in your work, please cite Dropulic et al. arXiv:<a href="https://arxiv.org/abs/2205.12278">2205.12278</a>, as well as Dropulic et al. <a href="https://doi.org/10.3847/2041-8213/ac09ef">ApJL 915, L14 (2021)</a> arXiv:<a href="https://arxiv.org/abs/2103.14039">2103.14039</a>. </p>
CI23: a 3D radially anisotropic velocity model of Central Apennines lithosphere
<p>We retrieve the 3D radially anisotropic model of Central Apennines lithosphere implementing Full-Waveform Inversion (FWI). </p> <p>The model has the following parameterization: VPH, VPV, VSH, VSV. It resolves P- and S-waves velocities in the period range 8 - 50s (0.02 - 0.125 Hz). For each point in the mesh (LAT1: 40.0°, LAT2: 45.0°, LON1: 11.0°, LON2: 16.0°), the model returns velocity values in units of m/s.</p> <p>The CI_23 model is available in multiple formats:</p> <ul> <li> <p>A <code>.vtk</code> version is hosted on Zenodo</p> </li> <li> <p>An <code>.h5</code> version can be accessed via Google Drive <a href="https://drive.google.com/drive/folders/18mHH6WRGOTJIaBGB8wn3FcrqYBMwVJyZ?usp=drive_link" target="_blank" rel="noopener">here</a></p> </li> <li> <p>A version interpolated onto a structured grid (in <code>.netCDF</code> format) is available through <a href="https://doi.org/10.17611/dp/emc.2025.ci23stallone.1" target="_blank" rel="noopener">IRIS-EMC </a></p> </li> </ul> <p> </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data description</strong></p> <p>This data set is comprised of a single TAR file containing 1536 hourly NetCDF files. Within these, radial velocities from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159 Doppler wind lidars, as well as coplanar-retrieved horizontal wind speed components in their common scanning plane are stored. </p> <p>The time period is 29 June 2022, 00:00 UTC - 31 August 2022, 23:58 UTC.</p> <p>More details about the variables, lidar locations, scan details, as well as post-processing can be found in the NetCDF metadata. The wind fields stored in the NetCDF files are also available in daily animation form under an accompanying Zenodo Video/Audio data set (DOI: 10.5281/zenodo.7212837).</p>
VELOCE I. High-precision Radial Velocities of Cepheids
<p>The first data release of the VELOcities of CEpheids project (VELOCE DR1, Anderson et al. 2024, A&A in press, arXiv: 2404.12280, doi: <a href="https://doi.org/10.1051/0004-6361/202348400">10.1051/0004-6361/202348400</a>) comprises 18,225 radial velocity measurements (RVs) of 258 bona-fide classical Cepheids as well as 1161 RVs of 164 additional targets, most of which were previously misclassified as Cepheids. The observations were collected mainly between 2010 and 2022 using two 1.2m telescopes: Euler (Coralie spectrograph) at ESO La Silla Observatory in Chile, and Mercator (Hermes spectrograph) at Roque de los Muchachos Observatory on La Palma, Canary Islands, Spain. </p> <p>Here, we publish the FITS files as described in appendix C of VELOCE paper I (Anderson et al. 2024). A total of 422 FITS files -- one per star -- are compressed together using tar and gzip as they would otherwise exceed the limit of 100 files in Zenodo. The FITS files contain all 19,386 individual RV measurements, as well as the per-epoch template fit residuals used to measure zero-point offsets and investigate long-term orbital motion. </p>
Synthetic automotive LiDAR dataset with radial velocity additional feature - (x,y,z,v)
<p>The synthetic dataset was generated using KITTI-like specifications and annotations format. It is comprised by the KITTI standard folders: label_2, image_2 and calib. Furthermore, there is a velodyne file for each of the following use cases:</p> <ul> <li>Point cloud 1: (x,y,z, (Bool)Is_Object): In this point cloud, the best performance of the Deep Learning model is expected as ground truth information is provided as the additional feature of each point. <ul> <li>Point cloud 1A: (x,y,z, (Bool)Is_Car): the additional feature of each point that belongs to an object of the ’Car’ type has a Boolean 1.0 value; contrariwise, the 0.0 value was used. File: velodyne_1A_isCar;</li> <li>Point cloud 1B: (x,y,z, (Bool)Is_Ped): the additional feature of each point that belongs to an object of the ’Pedestrian’ type has a Boolean 1.0 value; contrariwise, the value 0.0 was used. File: velodyne_1A_isPed.</li> </ul> </li> <li>Point cloud 2: (x,y,z, (Float)Radial_Velocity): this point cloud has the relative radial velocity as an additional feature for each point. File: velodyne_2_radial_velocity;</li> <li>Point cloud 3: (x,y,z,(Float)Absolute_Speed): in this point cloud, every point has the absolute speed of the object as the additional feature. File: velodyne_3_abs_speed;</li> <li>Point cloud 4: (x,y,z,(Bool)Is_Moving): the additional feature of this point cloud is a Boolean value that is set to 1.0 if the object is moving; contrariwise, it is set to 0.0 for static objects. File: velodyne_4_is_moving;</li> <li>Point cloud 5: (x,y,z,0): no additional feature information. If desired, requires post-processing to convert to (x,y,z) or changing the toolbox point cloud configuration to not consider the additional feature. File: velodyne_5_xyz;</li> </ul> <p>Additionally, the label split for testing and training sets used can be found at file: Labels_split.</p> <p>This work was made as part of a master thesis. For further details, please check the dataset generation source code [1]. Any further questions please contact Leandro Alexandrino (l.alexandrino@ua.pt).</p> <p> </p> <p>[1] - Fork deepgtav-presil - leandro alexandrino, https://github.com/leandroalexandrino1995/DeepGTAVPreSIL.</p>
Radial velocities and broadening functions for AI Hya
<p>Radial velocity and broadening function measurements from CORALIE and HIDES spectra.</p>
State of the Art in Exoplanets Observing Methods: Radial Velocity and Transit
<p>Recording of the presentation given at the Summer School</p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - Animations of radial velocity and coplanar-retrieved horizontal wind fields from KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159
<p><strong>Abstract</strong></p> <p>This data set was collected during the TEAMx pre-campaign in summer 2022 (TEAMx-PC22) in the Inn Valley Target Area, Austria.</p> <p><strong>Data Description</strong></p> <p>This data set is comprised of 64 daily .mp4 files showing:</p> <ul> <li>post-processed radial velocity fields sampled by KITcube Leosphere/Vaisala Windcube WLS200s-124 and WLS200s-159</li> <li>their coplanar-retrieved horizontal wind field output</li> <li>additionally, horizontal wind speed and direction sampled by the KITcube Vaisala Windcube v2.1 (WLS7-1489) at 60-m above ground level is added as an independent measurement for subjective validation of the coplanar-retrieved wind</li> </ul> <p>The wind fields shown in the animations originate from an accompanying Zenodo data set (DOI: 10.5281/zenodo.7212801), where complete information concerning lidar locations, scan parameters, and post-processing may be found.</p>
Posteriors for the spatial pm model and radial velocity model
<p>Posterior samples for the spatial pm model and line-of-sight velocity model. Labels of parameters are included in the npz file.</p>
Simple distance estimates for Gaia DR2 stars with radial velocities
<p>Bayesian distance estimates for stars with radial velocities and parallaxes published in <em>Gaia</em> DR2. Our method and prior is designed to apply to this specific subset of stars in <em>Gaia</em> DR2.</p> <p>The method is published in "Simple distance estimates for Gaia DR2 stars with radial velocities", McMillan 2018, arXiv:1806.00426</p> <p>The code used to produce the estimates is here: https://doi.org/10.5281/zenodo.1270548</p>
Craton Radial Anisotropy and Low Velocity Zones imaged with Bayesian and LSQR methods
<p>******************** README for Rad_anisotropy_BOYCE_GRL *****************************</p> <p>This repository contains data files, inversion software, outputs and plotting codes to accompany the following submitted manuscript:</p> <p>Boyce, A., Bodin, T., Durand, S., Soergel, D., Debayle, E. Seismic Evidence for Craton Formation by Underplating and Development of the MLD (submitted) Geophysical Research Letters.</p> <p>The Rad_anisotropy_BOYCE_GRL_V2.tar repo contains:<br> • LSQR_inversion - Least Squares inversion of synthetic and real data sets, all input and results and plotting files are included.<br> • SRF_Forward_modelling - Codes to make Axisem synthetic models and synthetic data to be inverted with Bayesian Code. Also included are Greens functions from Axisem simulations at 5s minimum period and python codes for forward modelling synthetic S-to-p reciever functions from the Axisem outputs.<br> • Bayesian_inversion - Bayesian inversion of synthetic and real data sets, all input files, processed files and plotting codes necessary for reconstructing the posterior distributions are included. Please see https://github.com/alistairboyce11/RJ_MCMC for an up-to-date distribution.<br> • MLD_compilation - Our MLD compilation "MLD_compilation_BOYCE_2023.xlsx" and codes to combine this with Fu et al., (GRL 2022) and plot Figure 2 of main manuscript<br> • Plotting_for_manuscript<br> - Radial_anisotropy_models - codes used to extract data and make plots for Figures S1--S5. Tomographic models available to download at https://ds.iris.edu/ds/products/emc/<br> - Figures for Figures 1, 3, 4 in main manuscript.</p>
Constructing a 3-D radially anisotropic crustal velocity model for Oklahoma by using full waveform inversion
<p>The OK3D_Vp_Ani.csv and OK3D_Vs_Ani.csv is inverted 3-D compressive and shear velocity model proposed in the publication.</p> <p>Each file contains horizontally and vertically polarized velocity components and their relative perturbation with respect to the averaged 1-D velocity profile, as well as the RA defined in the paper, at each location (longitude, latitude, depth).</p> <p>These two files are stored in CSV format, and can be easily readed by pandas module in python environment.</p>
Gaia XP radial velocity catalogues
<p>Here we provide the radial velocities we measured from the Gaia XP spectra.</p>
Data for "Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m/s RV Precision for APOGEE in the Plate Era"
<p>Data products associated with the paper "Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m/s RV Precision for APOGEE in the Plate-Era" are made publicly available including data and code to reproduce all figures.</p>
Precise dynamical masses of new directly imaged companions from combining relative astrometry, radial velocities, and HIPPARCOS-Gaia eDR3 accelerations
<p>The VLT/SPHERE reduced images using the Geneva reduction pipeline, GRAPHIC, used to obtain the astrometry and photometry as published in Rickman et al. 2022. Each .fits file has been cosmetically corrected (i.e. bad pixels), background-subtracted, and flat-fielded. These files correspond to the 'flux frames' of the imaging observing sequence that was used to calculate the astrometry and photometry for each companion. The raw data are also available on the ESO archive with the relevant program numbers as listed in Rickman et al. 2022.</p>
Relative radial velocities for Sanders 364 from the Tillinghast Reflector Echelle Spectrograph (TRES)
<p>We observed Sanders 364 with the Tillinghast Reflector Echelle Spectrograph 407 times between UT 2011-Feb-22 and 2022-May-16. TRES is a fiber-fed instrument mounted on the 1.5 m Tillinghast Reflector at the Fred Lawrence Whipple Observatory on Mount Hopkins, Arizona. The first two observations were taken as part of a long-running survey for binaries in M67, and the remaining 405 spectra were acquired with denser sampling starting in 2016, with the goal of better characterizing the planetary system orbiting S364. Typical exposure times were 10 to 15 minutes, yielding signal-to-noise ratios (SNR) most often between 40 and 80 per resolution element. We obtained Thorium-Argon emission-line spectra before and after the science exposures for wavelength calibration. We optimally extracted the spectra and derived RVs according to the procedures outlined in Buchhave et al. (2010), with the exception that we use the high-SNR median observed spectrum as the template for cross-correlation, and we account for drifts in the instrument zero point through nightly monitoring of RV standard stars. The mean uncertainty of our derived velocities is 15 ms<sup>-1</sup>. </p> <p>The first column in the file is the time of observation recorded in Barycentric Julian Date (BJD). The second column is the relative radial velocity in ms<sup>-1</sup>. The third column is the uncertainty associated with the derived radial velocity in ms<sup>-1</sup>.</p>
ZZ and TT cross-correlation functions, shear wave velocity and radial anisotropy models in the Bohai Bay basin
<p>This dataset contains three parts: ambient noise cross-correlation functions (ZZ and TT), S-wave velocity models and radial anisotropy models in the Bohai Bay basin.</p>
Inverted S-wave velocity model for "Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography"
<p># Instructions for the South China velocity model dataset from multi-mode surface-wave inversion.</p> <p> </p> <p>1. The detailas can be found at the paper: Tang Q, Sun W, Yoshizawa K, et al. Anomalous radial anisotropy and its implications for upper mantle dynamics beneath South China from multimode surface wave tomography. Journal of Geophysical Research: Solid Earth, 2022, 127(8): e2021JB023485.</p> <p> </p> <p>2. The relative SV-wave and SH-wave velocity models at different depths (from 0 to 300 km) are stored in the "data" foder. Each file follows the name convention: shear_{SV/SH}.{depth}.dat representing SV or SH velocity at a given depth.</p> <p> </p> <p>3. In each velocity file, each line have three columns: longitude (deg) latitude (deg) relative_velocity (%)</p> <p> </p> <p>4. The reference velocity can be found in the two files "SV_velocity" and "SH_velocity", depth and velocity.</p> <p> </p> <p>5. The relative velocity is calculated from (absolute-reference)/reference*100%. Thus one can have the absolute velocity via absolute = relative * (1+relative/100).</p>
Radial velocities for compact hierarchical triples: BD+44 2258 and KIC 06525196
<p>Radial velocity from the CREME project used in the work "Detached eclipsing binaries in compact hierarchical triples: triple-lined systems BD+442258 and KIC 06525196".</p>
Validation of the Bond et. al. (2010) SDSS-derived kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 proper motion and radial velocity data
<p>We validate the Bond et. al. (2010) kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 data. Bond et al. constructed models for stellar velocity distributions using stellar radial velocities measured by the Sloan Digital Sky Survey (SDSS) and stellar proper motions derived from SDSS and the Palomar Observatory Sky Survey astrometric measurements. These models describe velocity distributions as functions of position in the Galaxy, with separate models for disk and halo stars that were labeled using SDSS photometric and spectroscopic metallicity measurements. We find that the Bond et al. model predictions are in good agreement with recent measurements of stellar radial velocities and proper motions by the Gaia survey. In particular, the model accurately predicts the skewed non-Gaussian distribution of rotational velocity for disk stars and its vertical gradient, as well as the dispersions for all three velocity components. Additionally, the spatial invariance of velocity ellipsoid for halo stars when expressed in spherical coordinates is also confirmed by Gaia data at galacto-centric radial distances of up to 15 kpc.</p>
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