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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Simulated particle-level events of ttbar and QCD with PU200 using Pythia8+Delphes3 for machine learned particle flow (MLPF)

Authors: Pata, Joosep; Duarte, Javier Mauricio; Vlimant, Jean-Roch; Pierini, Maurizio; Spiropulu, Maria;

Simulated particle-level events of ttbar and QCD with PU200 using Pythia8+Delphes3 for machine learned particle flow (MLPF)

Abstract

Dataset of 50,000 top quark-antiquark (ttbar) and 5,000 QCD events produced in proton-proton collisions at 14 TeV, overlaid with minimum bias events corresponding to a pileup of 200 on average. The dataset consists of detector hits as the input, generator particles as the ground truth and reconstructed particles from DELPHES for additional validation. The DELPHES model corresponds to a CMS-like detector with a multi-layered charged particle tracker, an electromagnetic and hadron calorimeter. Pythia8 and Delphes3 were used for the simulation. An explanation of the dataset and how to load it can be found in the included jupyter notebook delphes_dataset.ipynb. The simulated events are stored in Bzip2-compressed python pickle files in tev14_pythia8_{sample}_{seed}_{idx}.pkl.bz2, where {sample} is ttbar or qcd, {seed} is the random seed, {idx} is the file index. Each file contains 100 events. The Pythia8 configs are found in tev14_pythia8_ttbar.py and tev14_pythia8_qcd.py, the Delphes config in delphes_card_CMS_PileUp.tcl.

Keywords

high-energy physics, machine learning, particle physics

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
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