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8 results for “gluon”
Pythia8 Quark and Gluon Jets for Energy Flow
<p>Two datasets of quark and gluon jets generated with Pythia 8, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process). The one without c and b jets was originally used in <a href="https://arxiv.org/abs/1810.05165">Energy Flow Networks: Deep Sets for Particle Jets</a>. Generation parameters are listed below:</p> <ul> <li>Pythia 8.226 (without bc jets), Pythia 8.235 (with bc jets), <span class="math-tex">\(\sqrt{s}=14\,\text{TeV} \)</span></li> <li>Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos</li> <li>FastJet 3.3.0, anti-ki jets with R=0.4</li> <li><span class="math-tex">\(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)</span></li> </ul> <p>There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file</p> <ul> <li>X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.</li> <li>y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.</li> </ul> <p>If you use this dataset, please cite this Zenodo record as well as the corresponding paper:</p> <ul> <li>P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.</li> </ul> <p>For the corresponding dataset of Herwig jets, see <a href="https://zenodo.org/record/2664330">this Zenodo record</a>. The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#quark-and-gluon-jets">EnergyFlow Python package</a>.</p> <p>Changes:</p> <ul> <li>v1 - Added files with b and c quark jets.</li> </ul>
LatticeQuarkProp/Vertex: Quark propagator and quark-gluon vertex with O(a) improved Wilson fermions
<p>Data for the quark propagator and quark-gluon vertex, from lattice simulations with Nf=2 Wilson-clover fermions.</p> <p>Files M_hyb_*.dat and Z_*.dat are data used in figs 3 and 4 of arXiv:1809.05421. These are plain text (3 columns, legend at top of each file).</p> <p>VertexData.zip contains the form factors lambda_1, lambda_2, lambda_3 in the soft gluon limit, presented in arXiv:2103.02945. The content and format of the files is explained in the document VertexData_Info.pdf</p> <p> </p>
Symbol Representation of the Three Gluon Form Factor in N=4 Planar Super Yang-Mills Theory
<p>Datasets describing the symbol of the three-gluon form factor in N=4 planar super Yang-Mills theory, generated using the amplitude bootstrap approach. The file "EZ_symb_new_norm" contains the symbol form of this quantity at 1 through 5 loops of precision, while the file "EZ6_symb_new_norm" contains the symbol at 6 loops. The file "EZ_symb_quad_new_norm" contains the symbol at 1 through 6 loops in compressed "quad" form, where the final-entry conditions described in (https://arxiv.org/pdf/2204.11901) are used to dramatically reduce the total number of terms in the symbol. The file "EZ7_symb_quad_new_norm" contains the symbol at 7 loops in the "quad" form. </p> <p>The tag “new_norm” refers to the fact that in the symbols given here, the letters a,b,c are defined by a = sqrt(u/(v*w)), b = sqrt(v/(w*u)), c = sqrt(w/(u*v)), as in arXiv:2405.06107, in order to make all coefficients integers. In contrast, in arXiv:2204.11901, the letters a,b,c were defined by a = u/(v*w), b = v/(w*u), c = w/(u*v).</p> <p>In addition to the funding sources listed, MW was supported by research grant 00025445 from Villum Fonden.</p>
Herwig7.1 Quark and Gluon Jets
<p>Two datasets of quark and gluon jets generated with Herwig 7.1.4, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process), analogous to <a href="https://zenodo.org/record/2658763">this dataset of Pythia jets</a>. Note that the two datasets in this record should not be combined. Generation parameters are listed below:</p> <ul> <li>Herwig 7.1.4, <span class="math-tex">\(\sqrt{s}=14\,\text{TeV} \)</span></li> <li>Quarks from <span class="math-tex">\(gq\to Z(\to\nu\bar\nu)q\)</span>, gluons from <span class="math-tex">\(q\bar q\to Z(\to\nu\bar\nu)g\)</span></li> <li>FastJet 3.3.0, anti-kT jets with R=0.4</li> <li><span class="math-tex">\(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)</span></li> </ul> <p>There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file</p> <ul> <li>X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.</li> <li>y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.</li> </ul> <p>If you use this dataset, please cite this Zenodo record and, optionally, the <a href="https://zenodo.org/record/2658763">Pythia dataset</a> which inspired it. The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#quark-and-gluon-jets">EnergyFlow Python package</a>.</p> <p>Changes:</p> <ul> <li>v1 - Renamed files from v0 to include 'withbc' (events were also shuffled around), added files without b and c quark jets.</li> </ul>
Quark and Gluon Nsubs
<p>A dataset consisting of 45 N-subjettiness observables for 100k quark and gluon jets generated with Pythia 8.230. Following <a href="https://arxiv.org/abs/1704.08249">1704.08249</a>, the observables are in the following order:</p> <div>$$\{\tau_1^{(\beta=0.5)},\tau_1^{(\beta=1.0)},\tau_1^{(\beta=2.0)},<br>\tau_2^{(\beta=0.5)},\tau_2^{(\beta=1.0)},\tau_2^{(\beta=2.0)},<br>\ldots,<br>\tau_{15}^{(\beta=0.5)},\tau_{15}^{(\beta=1.0)},\tau_{15}^{(\beta=2.0)}\}.$$</div> <div> </div> <p>The dataset contains two members: <code>'X'</code> which is a numpy array of the nsubs that has shape <code>(100000,45)</code> and <code>'y'</code> which is a numpy array of quark/gluon labels (quark=<code>1</code> and gluon=<code>0</code>).</p>
On the Landau gauge ghost-gluon-vertex close to and in the conformal window - data release
<h4>On the Landau gauge ghost-gluon-vertex close to and in the conformal window</h4><p>This repository contains the data presented in "On the Landau gauge ghost-gluon-vertex close to and in the conformal window". See the file README.md for more information.</p>
Nuclear shadowing in DIS at electron-ion colliders: gluon shadowing grids
<p>Data grids for <em>C<sub>eff</sub></em> a <em>R<sub>G</sub></em> accordign to <a href="https://arxiv.org/abs/2003.04156">arXiv:2003.04156</a> [hep-ph].</p> <p>Data for an arbitrary value between the minimal and maximal value of input parameters can be obtained using multidimensional linear interpolation function, for example, <em>Multidimensional Linear Interpolation F104 from CERNlib</em>, <a href="https://cernlib.web.cern.ch/cernlib/">https://cernlib.web.cern.ch/cernlib/</a>.</p> <p><strong>Abstract:</strong></p> <p>We present a revision of predictions for nuclear shadowing in deep-inelastic scattering at small Bjorken $\x_{Bj}$ corresponding to kinematic regions accessible by the future experiments at electron-ion colliders. The nuclear shadowing is treated within the color dipole formalism based on the rigorous Green function technique. This allows incorporating naturally color transparency and coherence length effects, which are not consistently and properly included in present calculations. For the lowest $|q\bar q\rangle$ Fock component of the photon, our calculations<br> are based on an exact numerical solution of the evolution equation for the Green function. Here the magnitude of shadowing is tested using a realistic form for the nuclear density function, as well as various phenomenological models for the dipole cross section. The corresponding variation of the transverse size of the $q\bar q$ photon fluctuations is important for $\x_{Bj}\gtrsim 10^{-4}$, on the contrary with the most of other models, which use frequently only the eikonal approximation with the ``frozen" transverse size.<br> At $\x_{Bj}\lesssim 0.01$ we calculate within the same formalism also a shadowing correction for the higher Fock component of the photon containing gluons. The corresponding magnitudes of gluon shadowing correction are compared adopting different phenomenological dipole models. Our results are tested by available data from the E665 and NMC collaborations. Finally, the magnitude of nuclear shadowing is predicted for various kinematic regions that should be scanned by the future experiments at electron-ion colliders.</p>
Bayesian estimation of the specific shear and bulk viscosity of the quark-gluon plasma
<p>This repository contains all data necessary to reproduce Bayesian parameter estimates of a computational model of relativistic heavy-ion collisions.</p> <p>The computational collision model which generated the raw data is available at <a href="https://github.com/Duke-QCD/hic-eventgen">https://github.com/Duke-QCD/hic-eventgen</a>. The analysis code which postprocessed the raw data and performed the parameter estimation is available at <a href="https://github.com/jbernhard/hic-param-est">https://github.com/jbernhard/hic-param-est</a>.</p> <p>For more information, see the author's dissertation, <a href="https://arxiv.org/abs/1804.06469">Bayesian parameter estimation for relativistic heavy-ion collisions (arXiv:1804.06469 [nucl-th])</a>. In particular, chapter 3 describes the computational collision model, chapter 4 describes the parameter estimation method, and section 5.3 presents the results based on this dataset.</p> <p><strong>Usage</strong></p> <p>hic-param-est-cache.tar.gz is designed to be used with the analysis code (<a href="https://github.com/jbernhard/hic-param-est">https://github.com/jbernhard/hic-param-est</a>).<br> See the documentation at <a href="http://qcd.phy.duke.edu/hic-param-est">http://qcd.phy.duke.edu/hic-param-est</a>. After downloading the code, extract the data archive in the hic-param-est folder. This will create the cache directory containing the parameter design files, model calculations, and experimental data. Note that this data archive includes Python pickle files created by <a href="https://joblib.readthedocs.io/en/latest/persistence.html">joblib</a>, which is included with <a href="https://scikit-learn.org">scikit-learn</a>, a dependency of the analysis code.</p> <p>alternate-format.hdf contains the same data in HDF5 format. This is not intended for use directly with the analysis code, but may be more convenient for other purposes.</p> <p>chain.hdf contains the posterior distribution sample (the "chain") generated by MCMC sampling in HDF5 format. It may be used with the analysis code by placing it in a subfolder "mcmc" in the hic-param-est folder. It can also be regenerated by the analysis code, although this can take a long time.</p>
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