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351 results for “jetting”
Magnetosheath Jets MMS (5/2015 - 6/2019)
<p>This dataset contains the jets and their classes that are used and analyzed in the paper: <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754</a> </p> <p>The list has been generated using MMS1 satellite with data from 5/2015 to 6/2019.</p> <p>More information can be found in the manuscript and its supplementary material. </p> <p> </p> <p> </p>
Reproduction package for the paper "Correlating spectral and timing properties in the evolving jet of themicro blazar MAXI J1836-194"
<p>Basic reproduction package for the paper in the title; it contains all the necessary information and scripts required to replicate the results and plots, minus the proprietary code used (which can be found on github on request).</p>
Coupling the COST reference plasma jet to a microfluidic device: a computational study - Figure Data
Open the record for dataset details and reuse information.
Investigating the Unsteady Dynamics of a Multi-Jet Impingement Cooling Flow Using Large Eddy Simulation - Promotional Video and Image
<p>Video: Volume rendering of temperature field obtained from a large eddy simulation of 9 inline impinging jets in a narrow channel.</p> <p>Image: Turbulent structures as isosurface of Q-criterion with an illustration of laser sheet used for PIV measurement.</p> <p>The results were presented at the ASME Turbo Expo 2024 (paper number GT2024-122465) and published in the ASME Journal of Turbomachinery (<a href="https://doi.org/10.1115/1.4066508">https://doi.org/10.1115/1.4066508</a>). The accepted manuscript of the paper is available under <a href="https://elib.dlr.de/207257/">https://elib.dlr.de/207257/</a>.</p> <p>The simulations were performed on DLR's HPC system <a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/cara">CARA</a> within the DLR project InnoCool.</p>
Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains
<p>Dec 15, 2021</p> <p> </p> <p><strong>Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains</strong></p> <p> </p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola</p> <p> </p> <p><strong>Motivation</strong></p> <p>The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).</p> <p> </p> <p><strong>License </strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p> eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p><strong> </strong>eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p><strong>Credit</strong></p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, <em>Journal of Applied Meteorology and Climatology</em>. In review.</p> <p><strong>Grant funding</strong></p> <p>This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.</p>
Novel polarimetric technique to constrain the magnetic field structure and strength of Gamma-ray burst jets
<p>Gamma-ray bursts (GRBs) are extremely energetic events of cosmological origin. Observed GRBs have high luminosity and rapid variability that requires ultra-relativistic motion in the production mechanism which drive the synchrotron radiation associated with the relativistic jets and their shocked interactions with the local ambient medium. They are broadly divided into two types based on the gamma-ray duration; long GRBs (>2 seconds), and short GRBs (<2 seconds). Long GRBs are thought to be originated from explosions of very massive stars and short GRBs are thought to be produced by the merger of compact binaries. Several key open questions about our understanding of GRB physics remain: What is the driving mechanism of GRB jets? What is the origin and role of magnetic fields in driving the explosion? Since these events happen at cosmological distances, they can not be resolved using traditional astronomical techniques. However, polarimetric observations of GRBs have allowed us to start the exploration of the structure and magnetic field configurations of their relativistic jets. Generally, polarization is measured via the ratio of fluxes by taking consecutive exposures, however for rapidly varying objects such as GRBs, it is not an effective way to observe polarization. Liverpool Telescope (LT) has utilized rapidly rotating polaroids to overcome this problem and created a series of polarimeters that have successfully detected early-time optical polarimetry of various GRBs. I will present photometric and polarimetric results of various GRBs observed by RINGO3. 10 GRBs were bright enough to perform analysis and we were able to perform polarimetric analysis for 7 GRBs. I will discuss how polarimetric detection for a long GRB 191016A along with photometric data constraint the energy injection mechanism for the central engine. In addition, I will present how polarization depends on various properties of GRBs such as photometric decay index, isotropic energy of GRBs, redshift etc.</p>
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders: generator-level and reconstruction-level jets dataset
<p>Jets at generator and reconstruction level saved in .npy format.</p> <p>Each jet is represented as an array of jet constituents characterized by their particle momentum in Cartesian coordinates, i.e., (px, py, pz). For both generator-level and reconstruction-level jets, jet constituents are ordered by decreasing pT.</p> <p>The shape of the datasets is [N, 50, 3], where N is the total number of jets, 50 is the number of particles per jet, and 3 is the number of particle features (in order): [px, py, pz].<br> About 1.7M jets split into training, validation and testing sets at 60%, 20% and 20% respectively.</p>
FastNLO interpolation tables for NLO pQCD predictions of Inclusive Jet Production in $pp$ collisions at $\sqrt{s}=200$ and $510$~GeV
<p>This is generated using the NLOJet++ interface using jet pT for renormalization and factorization scale.</p> <p><strong>run12pp200_R050.tab:</strong></p> <p>\sqrt{s} = 200 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 6.9 8.2 9.7 11.5 13.6 16.1 19.0 22.5 26.6 31.4 37.2 44.0 52.0</p> <p><strong>run12pp510.tab:</strong></p> <p>\sqrt{s} = 510 GeV<br> anti-kT R=0.5<br> |eta| bins: 0.0 0.5 0.9<br> pT bins (GeV): 8.0 10.0 13.0 17.0 21.0 26.0 32.0 39.0 47.0 57.0 68.0 80.0</p> <p><em>This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute.</em></p> <p>If you use this, please cite: D. Britzger, T. Kluge, K. Rabbertz, F. Stober, M. Wobisch, arXiv:1109.1310</p>
Dark Matter Jets of Rotating Black Holes
<p>This dataset is associated with</p> <p>"Dark Matter Jets of Rotating Black Holes",<br> Ottavia Balducci, Stefan Hofmann, and Maximilian Koegler<br> (2022)</p> <p>It includes the python 3 files "boost-main.py", "kerrsystem.py" and "KerrSystemPlot.py"<br> which produce the raw data files "r_out_d_table.csv" and "mass_r_out_table.csv"<br> of figures 2 and 3.</p> <p>Additionally, it includes the raw data files "velocity_table.csv" which allows for determining the<br> boundaries of the velocity integrals and "boost_d_a_small.csv" with "boost_d_a_large.csv"<br> which demonstrate that the dark matter jet becomes more collimated for larger black hole spin.</p>
JetClass: A Large-Scale Dataset for Deep Learning in Jet Physics
<p>JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper <a href="https://arxiv.org/abs/2202.03772">Particle Transformer for Jet Tagging</a>. An interface to use the dataset is provided in <a href="https://github.com/jet-universe/particle_transformer">https://github.com/jet-universe/particle_transformer</a>.</p>
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: truth-jet and reco-jet datasets
<p>All truth-jet and reco-jet datasets used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>.</p> <p><strong>Main</strong>: fragments are named `truth-jet_${MODEL}.tar.gz`, where MODEL is either `pv_msme_${lambdaPV}` (PV-mSME and lambdaPV is a floating point number with "." replaced with "p"), or `sm` for the Standard Model (lambdaPV = 0).</p> <p>Within each are three directories for the independent splits:</p> <ul> <li>train,</li> <li>test, and</li> <li>private_test.</li> </ul> <p> In the paper, we describe "test" as the validation set and "private_test" as the test set. Data in "private_test" were not used until models were finalized for the paper.<br> Within each of those are the processed results from simulations with different random seeds. They are independent shards which can be trivially combined.</p> <p>Each data file is in <a href="https://www.h5py.org/">h5</a> format. Its data are under the key "events" as an array with shape (n, 20). That last axis contains the reconstructed four-momenta of the hardest five jets in the order [Px, Py, Pz, E] * 5, with missing jets filled with zeros.</p> <p>Truth-jet files have additional keys "flavors" and "helicities". These contain truth-level flavour and helicity information, respectively. Their shape is (n, 5), where the last axis contains the zero-padded results. Flavours are encoded in the PDG ID scheme.</p> <p><strong>Rotated</strong>: Rotated PV-mSME data are in archives named `truth-jet-rot_${HOUR}.tar.gz`.<br> These have similar (train, test, private_test) structures as the others, but comprise parts of the same mixed model and should be combined by subsampling to a weighted average.</p> <p>HOUR is an integer rotation in [0, 23] for which the detector is rotated by an angle given in radians as HOUR * 2 pi / 24. This rotated dataset has lambdaPV=1. We demonstrate merging of these datasets in the code sharing (<a href="https://github.com/Rupt/paper-hunting-vampires">git</a>, <a href="https://doi.org/10.5281/zenodo.6827723">Zenodo</a>).</p>
Simulated and Reconstructed Jets with the COCOA Detector
<p>We present a single-jet dataset for fast simulation studies. The light-quark jets were generated using Pythia8.3 with energies between 20 and 200 GeV. The detector simulation was performed with the GEANT4-based <a href="https://arxiv.org/pdf/2303.02101" target="_blank" rel="noopener">COCOA</a> tool kit. Particle flow objects were obtained through <a href="https://arxiv.org/pdf/2212.01328" target="_blank" rel="noopener">HGPflow</a>. The truth and pflow particles are saved above a 1 GeV threshold. </p> <p>We provide two files with 5.5 M and 377 k unique single-jet events. Additionally, we have a file for resolution studies where each jet is sent through the detector simulation and reconstruction 100 times. This contains 3.6 k * 100 jets. The replicas are arranged sequentially; every 100 events have the same truth particles.</p>
Movies (II) for "3D Space-Time Adaptive Hybrid Simulations of Magnetosheath High-Speed Jets"
<p>Movies for the aforementioned paper showing magnetic field lines, dynamic pressure and plasma density in global magnetospheric simulations. NW and SW refer to quasi-radial northward and southward IMF. </p> <p>https://doi.org/10.1029/2020JA029035</p>
Wminus + up to 9 jets parton level events at 14 TeV in HDF5
<p>Wminus + up to 9 jets parton level events at 14 TeV</p> <p>Merging scale is at 20 GeV.</p> <p>This data was used in FERMILAB-PUB-19-192-T</p>
Pythia/Herwig + Delphes Jet Datasets for OmniFold Unfolding
<p>Datasets of QCD jets used for studying unfolding in <a href="https://arxiv.org/abs/1911.09107">OmniFold: A Method to Simultaneously Unfold All Observables</a>. Four different datasets are present:</p> <ul> <li>Herwig 7.1.5 with the default tune</li> <li>Pythia 8.243 with tune 21 (ATLAS A14 central tune with NNPDF2.3LO)</li> <li>Pythia 8.243 with tune 25 (ATLAS A14 variation 2+ of tune 21)</li> <li>Pythia 8.243 with tune 26 (ATLAS A14 variation 2- of tune 21)</li> </ul> <p><span class="math-tex">\(Z \)</span> + jet events (with the <span class="math-tex">\(Z \)</span> set to be stable) were generated for each of the above generator/tune pairs with the <span class="math-tex">\(Z\)</span> boson <span class="math-tex">\(\hat p_{T}^\text{min}>150\,\text{GeV} \)</span> and <span class="math-tex">\(\sqrt{s}=14\,\text{TeV}\)</span>. Events were then passed through the Delphes 3.4.2 fast detector simulation of the CMS detector. Jets with radius parameter <span class="math-tex">\(R=0.4\)</span> were found with the anti-<span class="math-tex">\(k_T\)</span> algorithm at both particle level ("gen"), where all non-neutrino, non-<span class="math-tex">\(Z\)</span> particle are used, and detector level ("sim"), where reconstructed energy flow objects (tracks, electromagnetic calorimeter cells, and hadronic calorimeter cells) are used. Only jets with transverse momentum greater than <span class="math-tex">\(10\,\text{GeV}\)</span> are kept (note that sim jets have a simple jet energy correction applied by Delphes). The hardest jet from events with a <span class="math-tex">\(Z\)</span> boson with a final transverse momentum of <span class="math-tex">\(200\,\text{GeV}\)</span> or greater are kept, yielding approximately 1.6 million jets at both gen-level and sim-level for each generator/tune pair.</p> <p>Each zipped NumPy file consists of several arrays, the names of which begin with either 'gen_' or 'sim_' depending on which set of jets they correspond to. The name of each array ends in a key word indicating what it contains. With the exception of 'gen_Zs' (which contains the <span class="math-tex">\((p_T,\,y,\,\phi)\)</span> of the final <span class="math-tex">\(Z\)</span> boson), there is both a gen and sim version of each array. The included arrays are (listed by their key words):</p> <ul> <li>'jets' - The jet axis four vector, as <span class="math-tex">\((p_T^\text{jet},\,y^\text{jet},\,\phi^\text{jet},\,m^\text{jet})\)</span> where <span class="math-tex">\(y^\text{jet}\)</span> is the jet rapidity, <span class="math-tex">\(\phi^\text{jet}\)</span> is the jet azimuthal angle, and <span class="math-tex">\(m^\text{jet}\)</span> is the jet mass.</li> <li>'particles' - The (rescaled, translated) constituents of the jets as <span class="math-tex">\((p_T/100,\,y-y^\text{jet},\,\phi-\phi^\text{jet},\,f_\text{PID})\)</span> where <span class="math-tex">\(f_\text{PID}\)</span> is a small float corresponding to the PDG ID of the particle. The PIDs are remapped according to <span class="math-tex">\(22\to0.0,\,211\to0.1,\,-211\to0.2,\)</span> <span class="math-tex">\(130\to0.3,\,11\to0.4,\,-11\to0.5,\,13\to0.6,\,-13\to0.7,\,321\to0.8,\,-321\to0.9,\)</span> <span class="math-tex">\(2212\to1.0,\,-2212\to1.1,\,2112\to1.2,\,-2112\to1.3\)</span>. Note that ECAL cells are treated as photons (id 22) and HCAL cells are treated as <span class="math-tex">\(K_L^0\)</span> (id 130). </li> <li>'mults' - The constituent multiplicity of the jet.</li> <li>'lhas' - The Les Houches (<span class="math-tex">\(\beta=1/2\)</span>) angularity.</li> <li>'widths' - The jet width (<span class="math-tex">\(\beta=1\)</span> angularity).</li> <li>'ang2s' - The <span class="math-tex">\(\beta=2\)</span> angularity (note that this is very similar to the jet mass, but does not depend on particle masses).</li> <li>'tau2s' - The 2-subjettiness with <span class="math-tex">\(\beta=1\)</span>.</li> <li>'sdms' - The groomed mass with Soft Drop parameters <span class="math-tex">\(z_\text{cut}=0.1\)</span> and <span class="math-tex">\(\beta=0\)</span>.</li> <li>'zgs' - The groomed momentum fraction (same Soft Drop parameters as above).</li> </ul> <p>If you use this dataset, please cite this Zenodo record as well as the corresponding paper:</p> <ul> <li>A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables, <a href="https://arxiv.org/abs/arXiv:1911.09107">arXiv:1911.09107</a>.</li> </ul> <p>The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#z-jets-with-delphes-simulation">EnergyFlow Python package</a>.</p> <p> </p>
Supporting Information for Publication "Multi-band study of a bi-directional jet occurred in the upper chromosphere"
<p>This supporting information provides three observational movies from SDO/AIA, SDO/HMI, and IRIS. The evolution of the jet and the evolution of the line-of-sight magnetograms in the corresponding region can be tracked in these movies. The caption of each movie is included in 'AGUSupporting-Information_Movie.pdf'.</p>
ttbar data with up to 4 jets and 2 leptons
<p>The ttbar data as described and required for the training and evaluation of the generative models in <a href="https://arxiv.org/abs/1901.00875">https://arxiv.org/abs/1901.00875</a></p> <p>The data contains the MET, METphi and the 4-vectors of the final state objects in the format (E, p_T, eta, phi) in the following order:</p> <p>MET, METphi, jet_1, jet_2, jet_3, jet_4, lepton_1, lepton_2 in each row of the .csv file. </p>
Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet
<h3>This record contains supplementary tables and figures for the article <em>'Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet' </em>by A. Murphy, E. T. Whelan, F. Bacciotti, D. Coffey, F. Comeron, J. Eisloffel, B. Nisini, S. Antoniucci, J. M. Alcala and T. P. Ray, accepted for publication by Astronomy & Astrophysics.</h3> <p> </p> <p><strong>Abstract: </strong></p> <p>Characterising stellar jet asymmetries is key to providing robust constraints for jet launching models, and hence to understanding the underlying mechanisms of jet launching. This study aims to characterise the asymmetric properties of the bipolar jet from the Classical T Tauri Star Th 28. We combine data from integral field spectroscopy with VLT/MUSE and high-resolution spectra from VLT/X-Shooter to map optical emission line ratios in both jet lobes. We carry out a diagnostic analysis of these ratios to compare the density, electron temperature, and ionisation fraction within both lobes. The mass accretion rate is derived from the emission lines at the source, and compared with the mass outflow rate derived in both lobes using the estimated densities and measured [O I]λ6300 and [S II]λ6731 luminosities. The blue-shifted jet shows a significantly higher electron temperature and moderately higher ionisation fraction than the red-shifted jet. In contrast to previous studies we also estimate higher densities n H in the blue-shifted jet by a factor ∼2. These asymmetries are traced to within 1′′ (160 au) of the source in the line ratio maps. We find Ṁacc = 2.4 × 10^−7 M⊙ yr ^−1 , with an estimated obscuration factor of ∼54 due to grey scattering around the star. Estimated values of Ṁout range between 0.66 – 13.7 × 10^−9 M⊙ yr^−1 in the blue-shifted jet and 5-9 × 10^−9 M⊙ yr^−1 in the red-shifted jet.<em> </em>The emission line maps and diagnostic results suggest that the jet asymmetries originate close to the source and are likely intrinsic to the jet. Furthermore, the combined dataset allows access to a broad array of accretion tracers. This in turn enables a more accurate estimation of the mass accretion rate, revealing Ṁacc higher by a factor > 350 than would otherwise be determined.</p> <p> </p> <p><strong>Summary of supplemental material:</strong></p> <p>Table 1: Emission lines detected in X-shooter observations of the jet. Fluxes are measured from the red-shifted jet lobe.</p> <p>Tables 2 and 3: Mass accretion rates measured from MUSE and X-Shooter observations of Th 28, respectively, assuming an on-source extinction of 2.5 mags.</p> <p>Tables 4 and 5: As in Tables 2 and 3, for an on-source extinction of 1.26 mags.</p> <p>Figures 1-4: Position-velocity maps of detected emission lines from the UV and VIS arms of the X-shooter observations.</p> <p>Figure 5: Accretion luminosities measured from MUSE and X-Shooter data, before and after correction for on-source obscuration. Left and right panels show the corresponding values if the fluxes are corrected for a wavelength-dependent extinction of 2.5 and 1.26 mags, respectively.</p> <div> </div> <div> </div>
Simulated semivisible jets of the "Aachen" model
<p>Dataset published with "<a href="https://arxiv.org/abs/2312.03067">Semi-visible jets, energy-based models, and self-supervision</a>", Favaro L. et al.</p> <p>It consist of a QCD dijets background file "qcd_constit.h5" and a signal file "aachen_constit.h5". The signal model has been proposed in "<a href="https://arxiv.org/abs/1907.04346">Strongly interacting dark sectors in the early Universe and at the LHC through a simplified portal</a>", Bernreuther E. et al. </p> <p>The signal model has a dark mediator with mass m_Z'=2TeV and dark quarks with mass m_qd=500MeV charged under dark SU(3)_d. The hadronization of the dark sector consist of dark pions with mass m_pi=4GeV and dark rhos with mass m_rho=5GeV.</p> <p>Simulation:</p> <ul> <li>Madgraph + Pythia + Delphes simulation;</li> <li>(E, px, py, pz) of the first 200 constituents, pT ordered;</li> <li>most jets have less than 70 constituents, the remaining ones are zero-padded;</li> <li>Fastjet reconstructed fat jets using anti-kt algorithm with R=0.8;</li> <li>kinematic cuts: 100<pT<300, |\eta<2| ;</li> <li>semivisible jets are matched to the parton level dark quark;</li> <li>pheno parameters: invisible fraction r_inv=0.75, m_pi=4GeV, m_rho=5GeV.</li> </ul> <p>Other publications which use this dataset:</p> <p>[<a href="https://arxiv.org/abs/2206.14225">1</a>] "A Normalized Autoencoder for LHC triggers", Dillon B. et al., arXiv:2206.14225</p> <p>[<a href="https://arxiv.org/abs/2202.00686">2</a>] "What's anomalous in LHC jets", Buss T. et al., arXiv:2202.00686</p>
Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 of 6
<p>Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 1 / 6<br>Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior</p> <p>This database presents unsteady calculated data derived from large-eddy simulations (LES) of a supersonic jet flow utilizing the FLEXI solver (https://numericsresearchgroup.org/codes.html#codes_flexi). <br>The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.</p> <p>The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file. </p> <p>For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file. <br>The database is divided into six parts. The present set of data is number one.</p> <p>This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development". </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.