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3 results for “Jet tagging”
Resources for The Fundamental Limit of Jet Tagging
<p>Resources related to The Fundamental Limit of Jet Tagging (arxiv:2411.02628)</p> <p>Includes:</p> <p>-In total 11,200,0000 qcd and 11,200,0000 top jets generated from corresponding transformer-based models (Tmodels) trained using the JetClass Dataset.</p> <p>-The trained Tmodels.</p> <p>-The LLR predictions from the Tmodels, for different number of constituents.</p> <p>-The predictions corresponding to the classifier-based jet taggers.</p>
RS3L: A jet tagging dataset for self-supervised learning based on re-simulation
<p>Jet tagging dataset used for "Re-Simulation-based Self-Supervised Learning" (RS3L, <a href="https://arxiv.org/abs/2403.07066">arXiv:2403.07066</a>). Simulated partons are re-showered with various parton shower configurations and reconstructed with the Delphes3 detector software. <br><br>The <code>singletons</code> array contains information about the jet and has dimensions <code>N_examples x N_augmentations x N_singletons</code>. The augmenations are ordered by: <br><br>1. nominal scenario: jet showered with Pythia8<br>2. changing the numerical seed in Pythia8<br>3. changing the scale controlling the probability for final state radiation by 1/sqrt(2)<br>4. changing the scale controlling the probability for final state radiation by sqrt(2)<br>5. using Herwig7 as parton shower</p> <p>The variables stored in the <code>singletons</code> array are:</p> <p><code>singletons = ['jettype','parton1_pt','parton1_eta','parton1_phi','parton1_e','parton2_pt','parton2_eta','parton2_phi','parton2_e', 'jet_pt','jet_eta','jet_phi','jet_e','jet_msd','jet_n2']</code><br><br></p> <p>For gluon-initiated jets, <code>parton1</code> and <code>parton2</code> are the two gluon daughters (see below). For any other jet type, parton1 is the main particle (W, H, Z, or single quark), and parton2 is filled with 0s.</p> <p>The <code>jet_pflow_cands</code> contains the particles clustered into the jet. The array has a shape <code>N_examples x N_augmentations x N_features x N_particles</code>.<br><br>The features per particle are:</p> <p><code>features = ['pt','relpt','eta','phi','dr','e','rele','charge','pdgid','d0','dz']</code><br><br>The <code>jettype</code> gives the parton at the origin of the jet: <br><br><code>jettype: <br>1: q<br>2: c<br>3: b<br>4: H->bb<br>5: g->qq<br>6: g->cc<br>7: g->bb<br>8: g->gg<br>9: W->two quarks<br>10: Z->qq<br>11: Z->bb</code></p> <p> </p>
PAIReD Jet Tagging Dataset
<p>Training and testing dataset used in "PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts" (arXiv:2311.11011).</p> <p>This dataset corresponds to approximately 2% of the dataset used in the article. Five types of jets are simulated: AK4, AK8, AK15, PAIReD, and PAIReDEllipse. Each tar file contains one type of jet. Jets are split into train and test datasets. Three processes, namely ZHbb, ZHcc, and Z+jj are included. The Higgs mass in the training dataset is varied between 10-500 GeV, while the test dataset contains jets arising from a 125 GeV Higgs.</p> <p> </p> <p> </p>
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