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81 results for “Interferometry”
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>
Laser interferometry measurements of a Moroccan rabāb
<p>Instrument: <i>rabāb</i><strong> </strong><br>Country of origin: Morocco <br>Place of origin: Fès <br>Instrument maker: Abdessalam Chiki <br>Year of manufacture: 2015 <br>Location: Basel, private property of Thilo Hirsch</p><p>Dimensions: <br>Total length: 513.2 mm <br>Max. Body width: 114.8 mm <br>Width at the upper end of the skin: 96.2 mm <br>Width at top nut: 31.6 mm <br>Body depth at the upper end of the skin: approx. 80 mm</p><p>Vibrating string lengths: <br>d-string: 410 mm <br>G-string: 403 mm</p><p>Materials: <br>Body: walnut <br>Pegbox: walnut <br>Fingerboard: Acajou (mahogany) <br>Decoration: mother-of-pearl <br>Bars: spruce <br>Top nut, tailpiece button: Bone <br>Bridge: bamboo <br>Top: goatskin</p><p>Laser interferometry measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 18.2.2020</p><p>Each figure (Filenames: A_####Hz.jpg) corresponds to the frequency of excitation on the bass side of the bridge (shaker: Frederiksen Vibration Generator Nr 2185.00). Images are made by time-averaging of laser speckle interference at the camera (time-average ESPI approach). Operating deflection shapes (roughly translating to theoretical vibrating modes) are observed at each frequency.</p><p>Photo of the setup: Thilo Hirsch</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>
Supporting data for "Fundamental limitations of cavity-assisted atom interferometry"
<p>Supporting data with code to generate Fig. 2 Cavity-induced deformation of a Gaussian input. Publication: DOI:https://doi.org/10.1103/PhysRevA.96.053820</p> <p>arXiv:1710.02448</p> <p>This dataset contains a zip file with raw data sets of all relevant measurements to plot figure 2.</p> <p>Figure 2. Envelope functions of the intracavity field for a 1 m cavity injected with<br> a 1μs pulse for different cavity finesses. All areas are normalized<br> to the input pulse area for comparison. When the pulse duration is<br> comparable to the photon lifetime of the cavity, its envelope function<br> is elongated. Inset: Envelopes without normalization.</p> <p> </p> <p>Further data and information are available from Miguel Dovale <mdovale@star.sr.bham.ac.uk> at reasonable request.</p> <p>School of Physics and Astronomy and Institute of Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom</p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</p>
Horizontal and vertical velocities in Ho Chi Minh city by Sentinel-1 radar interferometry
<p>Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr.</p> <p>File "Dinh_HCMUD_v1.tif" is the 50-m vertical velocity in mm/year. Negative velocities represent movement subsidence.</p> <p>File "Dinh_HCMEW_v1.tif" is the 50-m east-west horizontal velocity in mm/year. Positive velocities represent movement Eastward.</p> <p>For more details on the technique, the reader can be found in [1].</p> <p>[1] Ho Tong Minh, D.; NGO, Y.; Lê, T.T.; Le, T.C.; Bui, H.S.; Vuong, Q.V.; Le Toan, T. Quantifying Horizontal and Vertical Movements in Ho Chi Minh City by Sentinel-1 Radar Interferometry. <em>Preprints</em> <strong>2020</strong>, 2020120382. Available: https://www.preprints.org/manuscript/202012.0382/v2</p> <p> </p> <p> </p>
Dataset for: Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio
<p>Repository of data plotted in figures for the journal publication "Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio" (doi: 10.1364/OE.500077 ).</p> <p>Please see metadata file for details on individual data files.</p>
Dataset for "On Closure Phase and Systematic Bias in Multilooked SAR Interferometry"
<ul> <li>Datasets of the Barstow-Bristol Trough area -- unwrapped interferograms of con-1 through con-10, and con-20 and their associated correlation, connected components.</li> <li>Jupyter notebook tutorial can be found in https://github.com/insarlab/MintPy-tutorial/tree/main/applications</li> </ul>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Asteroseismology and Interferometry: a powerful combination to understand Ap stars
<p>Ultra-precise, 2-min cadence, photometric data is now available on over one thousand Ap stars, thanks to the NASA Transiting Exoplanet Survey Satellite (TESS) mission. These data is enabling the first unbiased search for pulsations among Ap stars, which, in turn, will impose stringent tests on current theoretical models for the driving of pulsations in these stars. To be successful, these tests require also that the global parameters of the stars are determined with great accuracy. In this talk we will present recent results from an ongoing interferometric programme on Ap stars aimed at establishing their radii and effective temperatures in a quasi model-independent way. We will then show how these accurate parameter determinations are enabling us to test the driving of pulsations in Ap stars, in combination with the asteroseismic data. Finally, we will show how the small cohort of stars in reach of interferometry is being used to test other methods for the computation of stellar parameters that are applicable to a vaster number of Ap stars and how this may, in turn, allow us to check the evolution of their magnetic properties.</p>
Dataset for 'Intercontinental comparison of optical atomic clocks through very long baseline interferometry'
<p>Dataset for the international comparison of optical atomic clocks using Very Long Baseline Interferometry (VLBI) and Global Positioning System (GPS) precise point positioning solution with integer ambiguity (IPPP) techniques appeared in <em>Nature Physics, <strong> 17</strong></em>, 223-227 (2021). The dataset is also available at <a href="https://doi.org/10.1038/s41567-020-01038-6">https://doi.org/10.1038/s41567-020-01038-6</a></p>
Figure Sets Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry II: Demographis of Nearby Brown Dwarfs"
<p>Images for Figure Sets 2, 3, 4, 6, and 9 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Demographis of Nearby Brown Dwarfs" (Currently accepted and in press.). Figure captions and file names are described in their associated README files (inside the bundles).</p> <p>Figure 2 shows survey sensitivity, Figure 3 shows the posterior distributions of our population models, Figure 4 compares our sensitivity and model distributions to the observed population, Figure 6 shows the marginalized population as a function of mass ratio, and Figure 9 shows the results of injecting an additional artificial detection.</p>
Multichannel Displacement measurement via self mixing interferometry and neural network : training and test datasets
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844], with dataset [10.5281/zenodo.7303745]. </p> <p>Here, the dataset is composed by a training set and a test set, in a specific configuration in which 3 self-mixing sensors measure simultaneously the same target displacement. Both datasets contain the displacement itself and the 3 interferometric signals (1 per sensing channel)</p> <p><strong>The training set </strong>relies on two python/numpy data files corresponding to <strong>harmonic displacements</strong> for different frequencies ranging from 53 and 93 Hz and amplitudes from 3.5 to 7.5 µm : </p> <ul> <li>Training_set_2_lostchannel_displacement.npy : 93744-elements long numpy array containing the target's displacement in units of µm/ms with a 1.024 ms time step. </li> <li>Training_set_2_lostchannel_signal.npy : numpy array of shape (3, 93744, 256, 1) containing the interferometric signals. The first dimension refers to the channel (1, 2 or 3), the second dimension is the number of segments of 256 points. Each segment of 256 points correspond to a 1.024 ms window of signal, matching one element of the displacement. For instance, the displacement value in `displacement[416]` corresponds to the interferometric signal segment `signal[0,416,:,0]` for channel 1, `signal[1,416,:,0]` for channel 2 and `signal[2,416,:,0]` for channel 3. </li> </ul> <p><strong>The test </strong>set follows the same architecture and format as the training set, but contains only <strong>random displacements </strong>generated by a delta-correlated signal, which we Fourier filter with a fifth order Butterworth filter between 10 and 100 Hz :</p> <ul> <li>"Displacement_test.npy" : with shape (246078, 1)</li> <li>"Signal_test.npy" : with shape (3, 246078, 256, 1)</li> <li>Only the displacement type has changed from harmonic to random, from the training to the test datasets.</li> </ul> <p>These datasets have been used to train and test a 3 channel neural network (after data augmentation) in order to emphasize the high availability potential of multichannel schemes, against backscattered power fluctuations. </p>
Mach-Zehnder-like interferometry with graphene nanoribbon networks
<p>S. Sanz, N. Papior, G. Giedke, D. Sanchez-Portal, M. Brandbyge, and T. Frederiksen<br><em>Mach–Zehnder-like interferometry with graphene nanoribbon networks</em><br><a href="https://iopscience.iop.org/article/10.1088/1361-648X/acd832">J. Phys.: Condens. Matter 35, 374001 (2023)</a></p> <p>We study theoretically electron interference in a Mach–Zehnder-like geometry formed by four zigzag graphene nanoribbons (ZGNRs) arranged in parallel pairs, one on top of the other, such that they form intersection angles of 60˚ Depending on the interribbon separation, each intersection can be tuned to act either as an electron beam splitter or as a mirror, enabling tuneable circuitry with interfering pathways. Based on the mean-field Hubbard model and Green's function techniques, we evaluate the electron transport properties of such 8-terminal devices and identify pairs of terminals that are subject to self-interference. We further show that the scattering matrix formalism in the approximation of independent scattering at the four individual junctions provides accurate results as compared with the Green's function description, allowing for a simple interpretation of the interference process between two dominant pathways. This enables us to characterize the device sensitivity to phase shifts from an external magnetic flux according to the Aharonov–Bohm effect as well as from small geometric variations in the two path lengths. The proposed devices could find applications as magnetic field sensors and as detectors of phase shifts induced by local scatterers on the different segments, such as adsorbates, impurities or defects. The setup could also be used to create and study quantum entanglement.</p>
Quantifying Fatigue-Damage and Failure-Precursors using Ultrasonic Coda Wave Interferometry
<p>Data sets for the Publication "Quantifying Fatigue-Damage and Failure-Precursors using Ultrasonic Coda Wave Interferometry".</p>
Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry
<p><strong>RESULTS DESCRIPTION</strong></p> <p>Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased.</p> <p>A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications.</p> <p><strong>ANALYSIS SUMMARY</strong></p> <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> <p><em>More information and context available at insarap.org</em></p> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "<em>TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION</em>".</p> <p><em>Acknowledgments: </em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p>
Mapping and analysis of the Central Italy Earthquake (2016) with Sentinel-1 A/B interferometry
<p><strong>Results Description</strong></p> <p><em>Background</em></p> <p>A 6.2M earthquake hit Central Italy, the area of city of Amatrice, on 24 August 2016. The quake epicentre was southeast of Norcia, Italy, in an area near the borders of the Umbria, Lazio, Abruzzo and Marche regions. The quake hypocentre was at a depth of approximately 5 km.</p> <p><em>InSAR</em></p> <p>We computed a set of coseismic Sentinel-1A/B interferograms, over the affected area.</p> <ul> <li>Descending track 95: S1A (2016-08-26) / S1A (2016-08-14)</li> <li>Descending track 22: S1A (2016-08-21) / S1B (2016-08-27)</li> <li>Ascending track 117: S1B (2016-08-21) / S1A (2016-08-27)</li> </ul> <p><em>Earthquake Motion Decomposition</em></p> <p>We also performed the two-dimensional earthquake motion decomposition. As an input for the decomposition, data of tracks 22 descending and 117 ascending were used, since they provide the full coverage of the earthquake.</p> <p><strong>Results and Data Package</strong></p> <p><em>Descending Interferogram, Track 95:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li><em>Descending Interferogram, Track 22:</em></li> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Ascending Interferogram, Track 117:</em></p> <ul> <li>Goldstein filtered wrapped interferometric phase, 16x4 ML (KMZ format)</li> <li>Interferometric coherence (KMZ format)</li> <li>Full geo-coded Goldstein filtered wrapped interferogram, 16x4 ML (GeoTiff format)</li> <li>Intereferorgram subset (GeoTiff format, 7Mb zip file) <ul> <li>Specifically for subset: 8x2 ML + goldstein + unwrapping + geocoding, incl oversampling to 10 m + smoothing (5x5 pixel boxcar) + subsampling to ~50m + calibrated at the point at used for 2D decomposition</li> </ul> </li> </ul> <p><em>Decomposed Solution</em></p> <ul> <li>Vertical Component (KMZ format)</li> <li>East-West Component (KMZ format)</li> <li>Vertical Component (GeoTiff format)</li> <li>East-West Component (GeoTiff format)</li> </ul> <p><em>Additional Information</em></p> <ul> <li>Filenames are self descriptive and not require additional information</li> <li>KMZ and GeoTiff files that are available for download, are generated for high-resolution investigations in GoogleEarth and post-processing & interpretation. They are not prepared for overviews, and thus are not heavily smoothed nor filtered.</li> </ul> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION".</p> <p><em>Acknowledgments:</em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p> <p><em>More information and context available at insarap.org .</em></p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Dataset and data processing tools of EPL 139 (2022) 55002 — Harmonic calibration of quadrature phase interferometry
<p>Dataset for <em>EPL</em> <strong>139</strong> (2022) 55002 — Harmonic calibration of quadrature phase interferometry</p> <p><strong>Scripts: </strong>the main Matlab script to analyse all data is <strong>analyseall.m</strong>, and uses all other scripts and functions (<strong>*.m</strong>). See comments in this main script for details. The script <strong>DefineDatasets.m,</strong> and its output <strong>Dataset.mat</strong>, were run before to define the main parameters of the 20 datasets, and define the frequency range where to estimate the background noise to be subtracted from the spectra of the driven cantilever when computing the total harmonic distortion.</p> <p><strong>Data:</strong> the 100 data files (.mat) have the following name scheme: [driving-frequency]-[Amplitude]-(low frequency external phase)-001 to 005.mat. For example 1kHz-10nm-w1Hz150nm-001.mat correspond to a driving of the cantilever at 1kHz, with an oscillation amplitude close to 10nm, with a low frequency driving of the external optical phase at 1Hz and equivalent 150nm amplitude. 001 stand for the first of the five files corresponding to this measurement. Note that the low frequency driving is optional. The main driving frequency is either directly given in kHz, or corresponds to one of the 2 first resonance modes of the cantilever (mode1 at 13.4kHz, and mode2approx at 84.6 kHz). Each of these data files contains 5 variables: Cx and Cy are the 2 outputs of the quadrature phase interferometer (1s of acquisition, 2e6 samples), fs is the sampling frequency (2 MHz), Ix and Iy are the mean total intensities on the photodiodes corresponding to Cx and Cy (units: A, this information can be useful to estimate the expected shot noise floor on the measurement).</p>
ADS-B Interferometry - (22/09/2023)
<p>Dataset of ADS-B data from Clee Hill on the 22 September 2023 used in the refractivity retrievals using ADS-B interferometry paper.</p>
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