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Dataset results
20 results for “Pythia”
CMS 2011A Simulation | Pythia 6 QCD1800-inf | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.CX2X.J3KW">Simulated QCD 1800-<span class="math-tex">\(\infty\)</span> Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 1400-1800 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.RC9V.B5KX">Simulated QCD 1400-1800 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 1000-1400 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.96U2.3YAH">Simulated QCD 1000-1400 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 800-1000 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.S3D5.KF2C">Simulated QCD 800-1000 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 470-600 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.BKTD.SGJX">Simulated QCD 470-600 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 300-470 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.X3XQ.USQR">Simulated QCD 300-470 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 170-300 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.WKRR.DCJP">Simulated QCD 170-300 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul>
CMS 2011A Simulation | Pythia 6 QCD 600-800 | pT > 375 GeV | MOD HDF5 Format
<p>Simulated QCD jets from the <a href="http://doi.org/10.7483/OPENDATA.CMS.EJT7.KSAY">Simulated QCD 600-800 Dataset of the CMS 2011 Open Data</a> reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have <span class="math-tex">\(p_T^\text{jet}>375\)</span> GeV, where <span class="math-tex">\(p_T^\text{jet}\)</span> includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.</p> <p>For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have <span class="math-tex">\(|\eta^\text{jet}|<1.9\)</span> to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.</p> <p>The supported method for downloading, reading, and using this dataset is through the <a href="https://energyflow.network">EnergyFlow Python package</a>, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please <a href="https://github.com/pkomiske/EnergyFlow/issues">submit an issue on GitHub</a>.</p> <p>For reference, the other corresponding datasets of simulated jets available on Zenodo are:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3341500">SIM/GEN QCD Jets 170-300 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341498">SIM/GEN QCD Jets 300-470 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341419">SIM/GEN QCD Jets 470-600 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3364139">SIM/GEN QCD Jets 600-800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341413">SIM/GEN QCD Jets 800-1000 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341502">SIM/GEN QCD Jets 1000-1400 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341770">SIM/GEN QCD Jets 1400-1800 GeV</a></li> <li><a href="https://doi.org/10.5281/zenodo.3341772">SIM/GEN QCD Jets 1800-<span class="math-tex">\(\infty\)</span> GeV</a></li> </ul> <p>There is an associated dataset of jets recorded by the CMS detector available on Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.3340205">CMS 2011A Jets, pT > 375 GeV</a></li> </ul> <p>Changes:</p> <ul> <li>v1 - Uploaded missing file.</li> </ul>
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
SRGN: Pythia + Delphes pp --> tt-bar
<p>A collection of datasets used in <a href="https://arxiv.org/abs/2010.03569">Parameter Estimation using Neural Networks in the Presence of Detector Effects</a> for the top quark mass extraction example. Sample code for reproducing the results is available on <a href="https://github.com/hep-lbdl/SRGN">GitHub</a>.</p> <p>Top quark pair production is generated using Pythia 8.230 and detector effects are modeled using Delphes 3.4.1 using the default CMS run card. One of the <span class="math-tex">\(W\)</span> bosons is forced to decay to <span class="math-tex">\(\mu + \nu_\mu\)</span> while the other <span class="math-tex">\(W\)</span> boson decays hadronically. Each event is recorded as a variable-length set of objects, consisting of jets, muons, and neutrinos. At simulation-level, the neutrino is replaced with the missing transverse momentum. Generator-level and simulation-level jets are clustered with the anti-<span class="math-tex">\(k_t\)</span> algorithm using <span class="math-tex">\(R=0.4\)</span> and are labeled as <span class="math-tex">\(b\)</span>-tagged if the highest energy parton inside the jet cone (<span class="math-tex">\(\Delta R<0.5\)</span>) is a <span class="math-tex">\(b\)</span> quark. Jets are required to have <span class="math-tex">\(p_T>20\)</span> GeV and they can only be <span class="math-tex">\(b\)</span>-tagged if <span class="math-tex">\(|\eta|<2.5\)</span>. Furthermore, jets overlapping with the muon are removed.</p> <p>Events are only saved if they have at least two <span class="math-tex">\(b\)</span>-tagged jets and at least two additional non <span class="math-tex">\(b\)</span>-tagged jets. Four observables are formed for performing the top quark mass extraction. First, the <span class="math-tex">\(b\)</span>-jet closest to the muon is labeled <span class="math-tex">\(b_1\)</span>. Of the remaining <span class="math-tex">\(b\)</span>-tagged jets, the highest <span class="math-tex">\(p_T\)</span> one is labeled <span class="math-tex">\(b_2\)</span>. The highest two <span class="math-tex">\(p_T\)</span>non <span class="math-tex">\(b\)</span>-tagged jets are labeled <span class="math-tex">\(j_1\)</span> and <span class="math-tex">\(j_2\)</span> . The four observables are given by: <span class="math-tex">\(m_{b_1\mu\nu}\)</span>, <span class="math-tex">\(m_{b_2\mu\nu}\)</span>, <span class="math-tex">\(m_{b_1j_1j_2}\)</span>, and <span class="math-tex">\(m_{b_2j_1j_2}\)</span>, where the four- momentum of the detector-level neutrino is determined by solving the quadratic equation for the <span class="math-tex">\(W\)</span> boson mass.</p> <p>Per event, the data is listed: <span class="math-tex">\((m_{b_1\mu\nu}, m_{b_2\mu\nu}, m_{b_2j_1j_2}, m_{b_1j_1j_2}, M_t)\)</span>, where <span class="math-tex">\(M_t\)</span>denotes the top quark mass.</p> <p><strong>Training Datasets:</strong></p> <p><em>DCTR_Mt_train.npz</em> contains two arrays, X and Y.<br> X is an array of events and Y is 0 (1) if the jet was generated with default (non-default) top quark mass. For a non-default event (Y=1), the <span class="math-tex">\(M_t\)</span> in each list represents the value of the top quark mass that was used. Note that for a default event (Y=0) the <span class="math-tex">\(M_t\)</span> in each list is <strong>not</strong> the default top quark mass, but <span class="math-tex">\(M_t\)</span> uniformly sampled in the same range as the Y=1 jets. </p> <p>The top quark mass was uniformly sampled in <span class="math-tex">\(M_t\in[170.0, 180.0]\)</span>.</p> <p><strong>Test Datasets:</strong><br> The test datasets are arrays made up of the same observables discussed above but with the top quark mass removed (i.e. <span class="math-tex">\((m_{b_1\mu\nu}, m_{b_2\mu\nu}, m_{b_2j_1j_2}, m_{b_1j_1j_2})\)</span>. <em>SRGN_Mt_default.npz</em><strong> </strong>was generated with the default top quark mass, <span class="math-tex">\(M_t=172.5\)</span> GeV, and <em>SRGN_Mt_unknown.npz</em><strong> </strong>was generated with <span class="math-tex">\(M_t=175.0\)</span> GeV.</p>
DCTR: Pythia e+e- -> Z -> dijets datasets
<p>A collection of datasets used in <a href="http://arxiv.org/abs/arXiv:1907.08209">Neural Networks for Full Phase-space Reweighting and Parameter Tuning</a>. Sample code for reproducing the results is available on <a href="https://github.com/bnachman/DCTR">GitHub</a>.</p> <p>Each dataset was generated with the Pythia 8.230 event generator. Particle-level <span class="math-tex">\(e^+ e^- \to Z \to \text{dijet}\)</span> events with about 100 particles in each event are clustered into jets using the anti-kt clustering algorithm (R = 0.8) with Fastjet 3.0.3. Every jet is presented as a list of constituents <span class="math-tex">\((p_T, \eta, \phi, \text{particle ID}, \theta)\)</span> where <span class="math-tex">\(\theta = (\texttt{TimeShower:alphaSvalue}, \texttt{StringZ:aLund }, \texttt{StringFlav:probStoUD})\)</span>.</p> <p><strong>Training Datasets:</strong><br> Each training file contains two arrays, X and Y.<br> X is an array of jets and Y is 0 (1) if the jet was generated with default (non-default) Pythia parameters. For a non-default jet (Y=1), the <span class="math-tex">\(\theta\)</span> in each constituent represents the value of the Pythia parameter that was used. Note that for a default jet (Y=0) the <span class="math-tex">\(\theta\)</span> in each constituent is <strong>not</strong> the default Pythia parameters, but <span class="math-tex">\(\theta\)</span> uniformly sampled in the same range as the Y=1 jets. </p> <p>The parameters were uniformly sampled in </p> <ul> <li><span class="math-tex">\(\texttt{TimeShower:alphaSvalue} \in [0.10, 0.18]\)</span></li> <li><span class="math-tex">\(\texttt{StringZ:aLund } \in [0.50, 0.90]\)</span></li> <li><span class="math-tex">\(\texttt{StringFlav:probStoUD } \in [0.10, 0.30]\)</span></li> </ul> <p>The 1D datasets are labeled by which parameter was changed, and the 3D dataset simultaneously vary all three parameters. </p> <p><strong>Test Datasets:</strong><br> Each test dataset consists of an dictionary containing: </p> <ul> <li>'jet': the jet constituents</li> <li>'multiplicity': Number of particles in jet</li> <li>'tau21': Nsubjettiness observable</li> <li>'tau32': Nsubjettiness observable</li> <li>'ECF_N3_B4': Energy Correlation Function(N=3, <span class="math-tex">\(\beta\)</span>=4)</li> <li>'ECF_N4_B4': Energy Correlation Function(N=4, <span class="math-tex">\(\beta\)</span>=4)</li> </ul> <p>The corresponding <span class="math-tex">\(\theta\)</span> values for each test set are described in the <a href="http://arxiv.org/abs/arXiv:1907.08209">paper</a>. </p>
JEWEL+PYTHIA simulated pp and PbPb collisions at 5020 GeV - Jet substructure variables
<p>Samples of simulated jets generated using JEWEL+PYTHIA for both Unquenched (pp) and Quenched (PbPb) cases, with the former being generated using the vacuum executable, jewel-vac, and the later using the medium executable, jewel-simple. For each case 320 000 events were produced with <span class="math-tex">\(\sqrt{s}=5020\)</span> GeV, <span class="math-tex">\(p_T \in [40, 250]\)</span> GeV, <span class="math-tex">\(|y|<2.5\)</span> . For the Quenched case, the medium settings were set to <span class="math-tex">\(\tau_i = 0.4\)</span> fm/c, <span class="math-tex">\(T_i = 440\)</span> MeV, <span class="math-tex">\(T_c = 170\)</span> MeV, and centrality <span class="math-tex">\(0-10\%\)</span>.</p> <p>Three Quenched samples are produced from the same JEWEL output eventfiles.</p> <ul> <li>One without thermal recoils</li> <li>One with thermal recoils but no background subtraction</li> <li>One with thermal recoils and background subtraction via the event-level subtraction prescription found in <a href="https://link.springer.com/article/10.1140/epjc/s10052-022-10954-1">Improved background subtraction and a fresh look at jet sub-structure in JEWEL</a></li> </ul> <p>More details about the simulation of the events and the jet variables please refer to the <a href="https://gitlab.com/lip_ml/jet-substructure-observables-ml-analysis">code repository</a> and <a href="https://arxiv.org/abs/2304.07196">Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis</a>, where they were first used.</p>
Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training
<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p> <p><strong>Format</strong>:<br> HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li> <li>scikit-image==0.12.0 implementation of cubic spline rotation</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Project Pythia: Output from WRF V3.6 model
Project Pythia is a community resource for learning how to analyze geosciences data using the Scientific Python Ecosystem. This data set is used primarily on the WRF-Python webpage to illustrate how WRF-Python can be used in conjunction with the Python packages Cartopy, Matplotlib, and Basemap. The data set is also used in a vertical interpolation example that is also found on the WRF-Python webpage. The central latitude for the data is 38.5N and the central longitude is 97.5W.
PERFORMING DELPHI. A Glance over the Pythia through its Editions and Artists
<p>This video was part of "The Delphic Preview: the Festival of the Muses", held by the Center for Hellenic Studies - Harvard University, the Isadora Duncan International Institute, the Ecumenical Delphic Union, and the Committee for the Reinstatement of the Delphic Games. Spanning over Delphic inspirations and reinstatements of the Pythian Games in modern times, the contribution goes back to ancient times to take a look at the musical contests of the Pythia and we will focus on the Hellenistic period in particular, when an amazing turnout of itinerant professionals of literature and music (the so-called “poeti vaganti”) took place in Delphi.</p>
PyThia Scatterometry Tutorial Data
<p>Data set for PyThia scatterometry tutorial.</p>
Pythia - 1st Evaluation
<p>Results of a survey in the higher education area. Participants are German students and the survey is about evaluating the learning management system Pythia after Röhrl et al. (S. Röhrl, S. Staufer, V.K. Nadimpalli, F. Bugert, F. Hauser, L. Grabinger, D. Bittner, T. Ezer, J. Mottok (2024) PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT, INTED2024 Proceedings, pp. 2871-2880.)</p> <p> </p> <p>Learning path algorithms:</p> <ul> <li>Tyche (https://doi.org/10.21125/inted.2024.1080 & https://zenodo.org/doi/10.5281/zenodo.10461677) is a Markov model</li> <li>Nestor (https://doi.org/10.21125/iceri.2023.1144) is a Bayesian network</li> </ul> <p> </p> <p>Learning element categories:</p> <ul> <li>Data: https://doi.org/10.5281/zenodo.10022143 & https://doi.org/10.5281/zenodo.10476168</li> <li>Publications: https://doi.org/10.21125/iceri.2023.0815 & https://doi.org/10.21125/inted.2024.1087</li> </ul> <p> </p> <p>The following aspects are evaluated:</p> <ul> <li>General aspects</li> <li>Graphical user interface</li> <li>Usability</li> <li>Learning elements</li> <li>Learning paths</li> <li>Other aspects</li> </ul> <p> </p> <p>The survey itself is structured as follows:</p> <ol> <li>Demographic data</li> <li>Usage behavior</li> <li>Usability</li> <li>GUI</li> <li>Learning elements</li> <li>Learning paths</li> </ol> <p> </p> <p>Our software engineering course has the following structure:</p> <ol> <li>Introduction</li> <li>Development processes</li> <li>Requirements engineering</li> <li>UML</li> <li>Design Pattern</li> <li>Safety and Security</li> <li>Software testing</li> </ol> <p> </p> <p>Further notes:</p> <p>The ranking of the learning elements range from 1 to 9. 1 is the best leaning element, while 9 is the worst.</p> <p> </p> <p> </p> <p>The corresponding scientific paper can be found via ORCID (https://orcid.org/0009-0009-4346-8678) as of September 2024.</p> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
PYTHIA Jet Datasets for cDDPM Unfolder
<p>Datasets of QCD jets used for studying unfolding in "Towards Universal Unfolding using Denoising Diffusion Probabilistic Models" consist of two different detector simulation frameworks:</p> <h3>Data-driven detector smearing:</h3> <ul> <li>Events were generated using PYTHIA 8.3 for proton-proton collisions at √s = 14 TeV</li> <li>Several physics processes were simulated: <ul> <li>ttbar production with various PDFs (CT14lo, NNPDF23, CTEQ6L1)</li> <li>Z+jets (Z → μμ) with CT14lo, NNPDF23, CTEQ6L1</li> <li>W+jets (W → μν) with CT14lo, NNPDF23, CTEQ6L1</li> <li>Dijet production</li> <li>Leptoquark production</li> </ul> </li> <li>Jets with radius parameter R = 0.4 were reconstructed using the anti-kT algorithm at particle-level ("gen_jets"), and then detector effects were applied ("reco_jets")</li> <li>Detector effects were simulated using ATLAS 8 TeV calibration data-derived jet resolution functions for pT, η, and φ</li> <li>Phase space bias was applied to some samples to enhance high-pT statistics: (pT_hat/pT_ref)^a with pT_ref = 100 GeV and a = 5</li> </ul> <h3> </h3> <h3>DELPHES CMS detector simulation:</h3> <ul> <li>Events were generated using PYTHIA 8.3 for proton-proton collisions at √s = 14 TeV</li> <li>Physics processes included: <ul> <li>ttbar production with CTEQ6L1</li> <li>Z+jets (Z → μμ) with CTEQ6L1</li> <li>W+jets (W → μν) with CTEQ6L1</li> <li>Dijet production with CTEQ6L1</li> <li>Leptoquark production with CTEQ6L1</li> </ul> </li> <li>Events were passed through DELPHES 3.4.2 fast detector simulation using the CMS detector configuration</li> <li>Jets with radius parameter R = 0.4 were reconstructed using the anti-kT algorithm at both particle level ("gen_jets") and detector level ("reco_jets")</li> <li>Phase space bias was applied to some samples using (pT_hat/pT_ref)^a with pT_ref = 100 GeV and a = 5</li> </ul> <p> </p> <p>For both frameworks, each dataset consists of several arrays containing jet kinematic information (pT, η, φ, E, px, py, pz) at both truth ("gen_jets") and detector ("reco_jets") level. Additional features such as event identifiers ("event_num") are included to enable reconstruction of event-level observables.</p>
EIC Pythia Events
<p>Dataset used in the publication Point cloud-based diffusion models for the Electron-Ion Collider, arXiv: <a href="https://arxiv.org/abs/2410.22421" target="_blank" rel="noopener">2410.2242</a><br>The dataset consists of DIS events for the EIC generated by Pythia8 at a representative CM energy for electron-nucleus collisions at the EIC sqrt(s) = 105 GeV. We avoid the low-Q^2 photoproduction region by imposing a cut of Q^2>25 GeV^2. <br>We include all particles in the rapidity range |y|<5, and we do not impose a lower cut on the transverse momentum. </p> <p>In the h5 files we include the following entries:<br><br>['data'] = [N,50, 13] the set of input particles with features : [eta_p + eta_h, phi_p + phi_h, log(h_pT/e_pT), charge,is_proton,is_neutron, is_kaon, is_charged_pion, is_neutrino,is_muon, is_electron,is_photon,]<br><br>The train_eic.h5 and val_eic.h5 are used for training and validation while results are compared to the test_eic.h5 file</p>
FIGURE 23. Scape length versus head width measurements for A. pythia and A in Australian ants of the genus Aphaenogaster (Hymenoptera: Formicidae)
FIGURE 23. Scape length versus head width measurements for A. pythia and A. reichelae.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.