Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “top quark”
Top quark pair production at the LHC, with all hadronic resolved decays for solving event combinatorics
<p><strong>R&D Datasets for solving event combinatorics in all hadronic top quark pair events at the LHC.</strong></p> <p>Used in the development of Topographs: Topological Reconstruction of Particle Physics Processes using Graph Neural Networks</p> <p> </p> <p>The datasets contain 5.8M ttbar events in the all hadronic decay channel, with jets matched to the truth partons in the top quark decays.</p> <p> </p> <p><strong>Event generation</strong></p> <ul> <li>Centre of Mass energy: 13 TeV</li> <li>MC Generator: MadGraph5_aMC@NLO v3.1.0, with MadSpin modelling the decays of the top quarks and W bosons.</li> <li>Parton Shower: Pythia v .243</li> <li>Detector response: Delphes v3.4.2 using ATLAS-like geometry</li> <li>Jets reconstructed with anti-kt algorithm, R=0.4, using FastJet</li> <li>b-Tagging corresponds to inclusive 70% b-jet efficiency</li> </ul> <p><strong>Event selection and truth matching</strong></p> <ul> <li>All events are required to have at least six reconstructed jets and exactly zero leptons (electrons or muons)</li> <li>Partons are matched to jets using <span class="math-tex">\(\Delta R\)</span> matching, with <span class="math-tex">\(\Delta R < 0.4\)</span></li> <li>Events with partons matched to multiple jets or jets to multiple partons are discarded</li> <li>Up to 16 jets are stored per event</li> </ul> <p>In the training dataset 1,340,000 events have all partons from the ttbar decays matched to jets.</p> <p>In the validation dataset, 71,000 events have all partons from the ttbar decays matched to jets.</p> <p>In the testing dataset 76,000 events have all partons from the ttbar decays matched to jets.</p> <p><strong>Dataset format</strong></p> <p>The dataset is in h5 format and the key 'delphes' has the following numpy arrays:</p> <pre><code>jets (16), jets_indices (16), matchability, nbjets, njets, partons (10) </code></pre> <p>Jets structured numpy array per event:</p> <ul> <li> <pre><code>(pt, eta, phi, energy, is_tagged)</code></pre> </li> </ul> <p>Jets_indices:</p> <ul> <li>Integer corresponding to the parton the jet is matched to</li> <li>From 0 to 5: b1 W1j1 W1j2 b2 W2j1 W2j2 (1= from top, 2=from antitop)</li> <li>-1 indicates not matched to a parton</li> <li>Properties of matched partons can be obtained from the partons array</li> </ul> <p>matchability:</p> <ul> <li>Which partons are matched to jets in event</li> <li>Binary representation with bits corresponding to each parton (length 6) 0b111111</li> <li>From left to right: b1 W1j1 W1j2 b2 W2j1 W2j2</li> <li>0b111000 (56) is one top fully matched, 0b000111 (7) is the other top fully matched, 0b111111 (63) is both tops fully matched</li> </ul> <p>njets, nbjets:</p> <ul> <li>How many jets/bjets in event</li> </ul> <p>partons:</p> <ul> <li>List of truth particles from ttbar decay: tops, Ws, quarks, ordered by top quark and its decays followed by anti-top and its decays</li> <li> <pre><code>PDGID, pt, eta, phi, mass</code></pre> </li> </ul> <p> </p> <p> </p> <p> </p>
Proton collision producing top pair, decaying hadronically via bottom quarks and W bosons
<p>This dataset contains the matrix element calculations for 10,000 events of `p p > t t~ , (t > b W+) , (t~ > b~ W-)`, as produced by MadGraph, without showering or hadronisation, and applying no cuts.</p>
Top quark pair production at the LHC
<p><strong>R&D Datasets containing all-hadronic, semi-leptonic and di-leptonic top quark pair events at the LHC.</strong></p> <p>Used in the development of PIPPIN: Particles Into Particles with Permutation Invariant Network</p> <p> </p> <p>The datasets contain a total of 40M ttbar events in the all-hadronic, semi-leptonic and di-leptonic decay channels, with jets matched to the truth partons in the top quark decays.</p> <p> </p> <p><strong>Event generation</strong></p> <ul> <li>Centre of Mass energy: 13 TeV</li> <li>MC Generator: PYTHIA v8.307</li> <li>Parton Shower and Hadronisation: PYTHIA v8.307</li> <li>Detector response: Delphes v3.4.2 using ATLAS-like geometry</li> <li>Jets reconstructed with anti-kt algorithm, R = 0.4, using FastJet</li> </ul> <p><strong>Event selection and truth matching</strong></p> <ul> <li>All events are required to have between 2 and 16 reconstructed jets</li> <li>Jets are required to fall within |η| < 2.5 and to have a minimum pT > 25 GeV</li> <li>Leptons are required to fall within |η|<2.5 and to have a minimum pT > 15 GeV</li> <li>Partons are matched to jets using <span><span><span><span><span>Δ</span><span>R</span></span></span></span></span> matching, with <span><span><span><span><span>Δ</span><span>R </span><span>< </span><span>0.4</span></span></span></span></span></li> <li>Events with partons matched to multiple jets or jets to multiple partons are discarded</li> </ul> <p>The training dataset contains 37M events.<br>The validation dataset contains 0.8M events.<br>The testing dataset contains 2.4M events.</p> <p> </p> <p><strong>Dataset format</strong></p> <p>The dataset is in HDF5 format and the key 'delphes' contains the following numpy arrays:</p> <p>Truth information (parton-level):</p> <ul> <li><code>truth_leptons</code>, <code>truth_neutrinos</code>, <code>truth_quarks</code>: Truth level information of the final state partons <ul> <li><em>keys:</em> <code>PDGID</code>, <code>pt</code>, <code>eta</code>, <code>phi</code>, <code>mass</code></li> </ul> </li> <li><code>truth_particles</code>: Truth level information of the final state partons and the intermediate particles <ul> <li><em>keys:</em> <code>PDGID</code>, <code>pt</code>, <code>eta</code>, <code>phi</code>, <code>mass</code></li> </ul> </li> </ul> <p>Reconstructed information (detector-level):</p> <ul> <li><code>leptons</code>: The zero padded reconstructed leptons (0 to 2), ordered by decay channel <ul> <li><em>keys:</em> <code>pt</code>, <code>eta</code>, <code>phi</code>, <code>energy</code>, <code>charge</code>, <code>type</code></li> </ul> </li> <li><code>MET</code>: The missing transverse energy <ul> <li><em>keys:</em> <code>MET</code>, <code>phi</code></li> </ul> </li> <li><code>jets</code>: The zero padded reconstructed jets (2 to 16), ordered by pT <ul> <li><em>keys:</em> <code>pt</code>, <code>eta</code>, <code>phi</code>, <code>energy</code>, <code>is_tagged</code>, <code>is_tau</code></li> </ul> </li> </ul> <p>Miscellaneous information:</p> <ul> <li><code>decay_channel</code>: The decay channel of the event <ul> <li>0b00 for all-hadronic, 0b01 for semi-leptonic (from Top), 0b10 for semi-leptonic (from Anti-Top), 0b11 for di-leptonic</li> </ul> </li> <li><code>matchability</code>: Which partons are matched to a reconstructed object <ul> <li>Binary representation with bits corresponding to each parton (length 6) 0b111111</li> <li>From left to right: b1, q1<sub>W1</sub>, q2<sub>W1</sub>, b2, q1<sub>W2</sub>, q2<sub>W2 </sub>(b1/W1 = from Top, b2/W2 = from Anti-Top)</li> <li>0b111000 means Top fully matched, 0b000111 means Anti-Top fully matched, 0b111111 means both Tops fully matched, etc.</li> </ul> </li> <li><code>jet_indices</code>: Integer corresponding to the parton a jet is matched to <ul> <li>From 0 to 5: b1, q1<sub>W1</sub>, q2<sub>W1</sub>, b2, q1<sub>W2</sub>, q2<sub>W2 </sub>(b1/W1 = from Top, b2/W2 = from Anti-Top)</li> <li>-1 indicates not matched to a parton</li> </ul> </li> <li><code>nleptons</code>, <code>njets</code>, <code>nbjets</code>: How many leptons, jets, b-jets in the event</li> </ul>
Top Quark Tagging Reference Dataset
<p>A set of MC simulated training/testing events for the evaluation of top quark tagging architectures.</p> <p>In total 1.2M training events, 400k validation events and 400k test events. Use “train” for training, “val” for validation during the training and “test” for final testing and reporting results.</p> <p><strong>Description</strong></p> <ul> <li> <p>14 TeV, hadronic tops for signal, qcd diets background, Delphes ATLAS detector card with Pythia8</p> </li> <li> <p>No MPI/pile-up included</p> </li> <li> <p>Clustering of particle-flow entries (produced by Delphes E-flow) into anti-kT 0.8 jets in the pT range [550,650] GeV</p> </li> <li> <p>All top jets are matched to a parton-level top within ∆R = 0.8, and to all top decay partons within 0.8</p> </li> <li> <p>Jets are required to have |eta| < 2</p> </li> <li> <p>The leading 200 jet constituent four-momenta are stored, with zero-padding for jets with fewer than 200</p> </li> <li> <p>Constituents are sorted by pT, with the highest pT one first</p> </li> <li> <p>The truth top four-momentum is stored as truth_px etc.</p> </li> <li> <p>A flag (1 for top, 0 for QCD) is kept for each jet. It is called is_signal_new</p> </li> <li> <p>The variable "ttv" (= test/train/validation) is kept for each jet. It indicates to which dataset the jet belongs. It is redundant as the different sets are already distributed as different files.</p> </li> </ul>
Top Quark Momentum Reconstruction Dataset
<p>A set of Monte Carlo simulated events, for the evaluation of top quarks' (and their child particles') momentum reconstruction. Produced using the HEPData4ML package [1]. The data is saved in HDF5 format, as sets of arrays with keys (as detailed below). There are ~1.15M events, with approximately 700k in "train.h5", 200k in "valid.h5", and 250k in "test.h5".</p> <p>There are two versions of the data, the difference between them being whether or not (fast) detector simulation was performed. Those with the detector simulation have the "_delphes" suffix in their filenames. Both versions are produced from the same set of generator-level events.</p> <ul> <li>13 TeV center-of-mass energy, fully hadronic top quark decays, simulated with Pythia8. <ul> <li>Events are generated with leading top quark pT in [550,650] GeV.</li> <li>Where applicable, detector simulation is done using Delphes, with the ATLAS detector card.</li> </ul> </li> <li>Clustering of particles/objects is done using the anti-kT algorithm, with <span class="math-tex">\(R=0.8\)</span>. <ul> <li>For the data without detector simulation, the inputs to clustering are the stable, visible final-state particles from Pythia8.</li> <li>For the data with detector simulation, the inputs are calorimeter towers (`Towers`) from Delphes.</li> </ul> </li> <li>Each entry corresponds with a single jet. <ul> <li>All jets are matched to a parton-level top quark within <span class="math-tex">\(\Delta R =0.8\)</span></li> <li>Jets are required to have <span class="math-tex">\(|\eta| < 2, \; p_T > 15 \text{ GeV}\)</span></li> <li> <p>The 200 leading (highest <span class="math-tex">\(p_T\)</span>) jet constituent four-momenta are stored in Cartesian coordinates <span class="math-tex">\((E,p_x,p_y,p_z)\)</span>, sorted by decreasing <span class="math-tex">\(p_T\)</span> and with with zero-padding for jets with fewer than 200 constituents. These are stored under the key `Pmu`. The number of non-zero jet constituents is stored under the key `Nobj`.</p> </li> <li> <p>The jet four-momentum is stored in Cartesian coordinates and in cylindrical coordinates <span class="math-tex">\((p_T,\eta,\phi,m)\)</span> under keys `jet_Pmu` and `jet_Pmu_cyl`, respectively.</p> </li> <li> <p>The truth (parton-level) four-momenta of the top quark, and the bottom quark and W-boson to which it decays, are stored in Cartesian coordinates in keys `truth_Pmu_0`, `truth_Pmu_1` and `truth_Pmu_2` respectively.</p> <ul> <li> <p>In addition, these are stored together under the key `truth_Pmu`, with the corresponding PDG codes stored under the key `truth_Pdg`.</p> </li> </ul> </li> </ul> </li> </ul> <p>The testing files `test.h5` and `test_delphes.h5` contain additional information for gauging performance, primarily on measurements of the W boson:</p> <ul> <li>In addition to the parton-level top, bottom and W-boson, the truth-level particles in `truth_Pmu` also include the two quarks from W decay, followed by up to 200 stable daughter particles from the W boson decay.</li> <li>`is_W_daughter`: An array of {0,1} indicating whether or not a particular jet constituent in `Pmu` is matched to a truth-level W daughter. <em>This particular array is only present in the testing file without detector simulation</em>.</li> <li>`jet_is_contained`: Whether or not one of the quarks from W decay has a distance from the jet center of <span class="math-tex">\(\Delta R > 0.8\)</span> .</li> <li>`jet_q_dr_max`: The maximum distance between the jet center and one of the quarks from W decay in <span class="math-tex">\((\eta,\phi)\)</span>.</li> <li>`jh_tag`: Whether or not this jet was tagged by the Johns Hopkins top tagger [2], as implemented in Fastjet.</li> <li>`jh_W_pred`: The four-momentum of the W boson candidate identified by the JH tagger (only present for tagged jets), in Cartesian coordinates.</li> <li>`jh_W_pred_constituents`: Up to 200 constituent four-momenta of the JH W boson candidate, in Cartesian coordinates.</li> <li>`jh_W_nobj`: The number of constituent four-momenta of the JH W boson candidate.</li> <li>`is_jh_constituent`: An array of {0,1} indicating whether or not a particular jet constituent in `Pmu` is matched to a JH W boson candidate constituent.</li> <li>`jh_m_pred`: The mass of the JH W boson candidate.</li> <li>`jh_pt_pred`: The <span class="math-tex">\(p_T\)</span> of the JH W boson candidate.</li> <li>`jh_m_res`: The ratio of the JH W boson candidate mass to the true W boson mass.</li> <li>`jh_pt_res`: The ratio of the JH W boson candidate <span class="math-tex">\(p_T\)</span> to the true W boson <span class="math-tex">\(p_T\)</span>.</li> <li>`jh_psi`: The lab-frame angle between the JH W boson candidate 3-momentum and the true W boson 3-momentum.</li> <li>`event_idx`: An integer indexing the event number, which may be useful for bookkeeping if splitting the testing file.</li> <li>`process_code`: The process code as given by Pythia.</li> <li>`cross_section`: The cross section estimate for the process that produced this jet, as given by Pythia. Potentially useful if combining this dataset with other datasets that involve different processes.</li> <li>`cross_section_uncertainty`: The cross section estimate uncertainty as given by Pythia.</li> <li>`mc_weight`: The generator weight for each event as given by Pythia.</li> </ul> <p>[1]: J. T. Offermann and X. Liu, <a href="https://github.com/janTOffermann/HEPData4ML">HEPData4ML</a>, (2022).<br> [2]: D. E. Kaplan, K. Rehermann, M. D. Schwartz, and B. Tweedie, Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks, Phys. Rev. Lett. 101, 142001 (2008).</p>
Top quark pair events for heavy flavour tagging and vertexing at the LHC
<p>This data contains jets from top anti-top decays. The event and parton shower are simulated in Pythia 8 with a centre of mass energy of 13 TeV, with detector response modelled in the Delphes framework. The detector response is modelled on the ATLAS detector, and a mean pileup of 50 was used.</p> <p>The dataset consists of jets, jet constituents, and truth heavy-flavour hadrons. For each jet, up to 50 charged constituents and 5 truth hadrons are included. Each constituent includes a link to the truth hadron associated, if such a link exists. </p> <p>Provided are 5 files, which are detailed below</p> <ul> <li>class_dict.yaml - Details the relative weights for classification labels, based on the frequency of occurrence for a given entry. Labels are detailed below.</li> <li>norm_dict.yaml - Contains the means and standard deviations of variables that can be used for training, allowing for scaling.</li> <li>pp_output_train.h5 - 13.5 million training jets, consisting of 4.5 million b-jets, c-jets, and light-flavoured jets. Resampling is applied over the jet pT and eta, to ensure equivalent kinematic distributions</li> <li>pp_output_val.h5 - 1.35 million jets for validation, consisting of 450,000 of each jet flavour. Kinematics are resampled in the same way as the training file.</li> <li>pp_output_test_ttbar.h5 - 1.35 million jets for evaluation, consisting of 450,000 of each jet flavour, with no kinematic resampling applied.</li> </ul> <p>Each h5 file contains the following groups:</p> <ul> <li>Jets - (N,) - N jets, including variables such as jet kinematics, flavours, and summary statistics on the number of hadrons and constituents in the jet.</li> <li>Consts (N,50) - Up to 50 charged constituents per jet. Includes details on constituent kinematics and identification. A variable 'valid' is True for tracks in the jet, and False for all other tracks. The additional variable 'truth_hadron_idx' details which hadron (if any) a constituent is associated to.</li> <li>Hadrons (N, 5) - Up to 5 truth heavy-flavour hadrons per jet. Each hadron includes details on kinematics. The variable 'hadron_idx' represents an ID for the hadron, and matches to the constituent variable 'truth_hadron_idx'.</li> </ul> <p>Each group contains both variables that can be used in training, and truth labels, which are as follows:</p> <ul> <li>Jets <ul> <li>flavour - Flavour ID of the jet, 5 for b-jets (containing at least 1 b-hadron within a 0.4 dR(jet, hadron) match), 4 for c-jets (no b-hadrons, and contains at least 1 c-hadron), 0 for light-flavoured jets (contains no b- or c-hadrons)</li> </ul> </li> <li>Consts <ul> <li>truth_hadron_idx - integer that refers to hadron that produced the constituent. '-1' for padded tracks, or tracks with no truth heavy flavour hadron (e.g, pileup, hadronisation).</li> <li>truth_vertex_idx - integer that refers to the vertex a constituent came from - if two tracks have an equivalent truth_vertex_idx, they originate from the same vertex.</li> </ul> </li> <li>Hadrons <ul> <li> <div>hadron_idx - The idx of the hadron, value is '-1' for padded hadrons, 0-4 for remaining hadrons. If a constituent 'truth_hadron_idx' matches with this value, then the constituent came from this heavy flavour hadron decay.</div> </li> <li>flavour - The flavour of the hadron. '5' if the hadron contains at least 1 b-quark, '4' if there is no b-quark but a c-quark is present, '-1' for padded hadrons</li> <li>pt, lxy, dr, mass - The transverse momentum (pt) [GeV], transverse distance between hadronic decay vertex and the primary vertex (lxy) [mm], dR(Jet, Hadron), and the hadron truth mass [GeV]</li> </ul> </li> </ul> <p><span>This dataset allows for studies into algorithms that aim to perform vertex reconstruction.</span></p>
ScienceDex guides
Understand access before you commit
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.