December 13, 2024 Dataset Open Access

Amram, Oz; Anzalone, Luca; Birk, Joschka; Faroughy, Darius A.; Hallin, Anna; Kasieczka, Gregor; Krämer, Michael; Pang, Ian; Reyes-Gonzalez, Humberto; Shih, David

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{"conceptdoi":"10.25592/uhhfdm.16504","conceptrecid":"16504","created":"2024-12-13T14:38:06.267739+00:00","doi":"10.25592/uhhfdm.16505","id":16505,"links":{"badge":"https://www.fdr.uni-hamburg.de/badge/doi/10.25592/uhhfdm.16505.svg","conceptbadge":"https://www.fdr.uni-hamburg.de/badge/doi/10.25592/uhhfdm.16504.svg","conceptdoi":"http://doi.org/10.25592/uhhfdm.16504","doi":"http://doi.org/10.25592/uhhfdm.16505"},"metadata":{"access_right":"open","access_right_category":"success","communities":[{"id":"uhh"}],"creators":[{"affiliation":"Fermi National Accelerator Laboratory","name":"Amram, Oz","orcid":"0000-0002-3765-3123"},{"affiliation":"University of Bologna","name":"Anzalone, Luca","orcid":"0000-0002-0399-8836"},{"affiliation":"Universit\u00e4t Hamburg","name":"Birk, Joschka","orcid":"0000-0002-1931-0127"},{"affiliation":"Rutgers University","name":"Faroughy, Darius A.","orcid":"0000-0002-4027-5477"},{"affiliation":"Universit\u00e4t Hamburg","name":"Hallin, Anna","orcid":"0000-0002-1551-814X"},{"affiliation":"Universit\u00e4t Hamburg","name":"Kasieczka, Gregor","orcid":"0000-0003-3457-2755"},{"affiliation":"RWTH Aachen University","name":"Kr\u00e4mer, Michael","orcid":"0000-0002-3089-6827"},{"affiliation":"Rutgers University","name":"Pang, Ian","orcid":"0000-0002-8225-7269"},{"affiliation":"RWTH Aachen University","name":"Reyes-Gonzalez, Humberto","orcid":"0000-0003-3283-5208"},{"affiliation":"Rutgers University","name":"Shih, David","orcid":"0000-0003-3408-3871"}],"description":"<p>This dataset contains approximately 180 M boosted jets, derived from open data collected by the CMS experiment at the Large Hadron Collider (LHC) in 2016 &mdash; specifically the JetHT datastream &mdash; and presented in a format suitable for Machine Learning (ML) applications. A detailed description of the dataset and how it was produced can be found in the companion paper, arxiv <a href=\"https://arxiv.org/abs/2412.10504\">2412.10504</a>.</p>\n\n<p>For each jet we store its transverse momentum (p_T), pseudorapidity (eta), and azimuthal angular coordinate (phi). We also store its mass, groomed with the softdrop algorithm as computed within the CMS reconstruction. Up to 150 constituents of the jet are stored. For each constituent, its 4-momentum is stored in the format (p_x, p_y, p_z, E).&nbsp;We additionally store its transverse impact parameter (d_0) and longitudinal impact parameter (d_z) with their uncertainties, the charge of the candidate, its particle-ID (PID) in the PDG format (note that neutral hadrons are assigned the PID=130 of the neutral kaon K_L^0, while positively/negatively charged hadrons are assigned PID=211 of the charged pion) and its weight from the PUPPI algorithm. We also include additional jet substructure quantities computed within the CMS reconstruction, including the number of constituents in the jet, N-subjettiness variables, various jet-tagging observables from the CMS implementation of ParticleNet and a regression of the jet mass from ParticleNet.</p>\n\n<p>Events are stored in h5 format with 4 keys:</p>\n\n<ul>\n\t<li>&#39;event_info&#39;, shape (N_jets, 3): [Run Number, LumiBlock, Event Number]</li>\n\t<li>&#39;jet_kinematics&#39;, shape (N_jets, 4): [pt, eta, phi, softdrop mass]</li>\n\t<li>&#39;PFCands&#39;, shape (N_jets, 150, 11): Zero padded list of up to 150 PFcandidates inside the jet.<br>\n\tInfo for each candidate is [px, py, pz, E, d0, d0Err, dz, dzErr, charge, PDG ID, PUPPI weight]</li>\n\t<li>&#39;jet_tagging&#39;, shape (N_jets, 13): Tagging info/scores for the AK8 jet.<br>\n\tInfo for each jet: [nConstituents, tau1, tau2, tau3, tau4, ParticleNet H4q vs QCD, ParticleNet Hbb vs QCD, ParticleNet Hcc vs QCD, ParticleNet QCD score, ParticleNet T vs QCD, ParticleNet W vs QCD, ParticleNet Z vs QCD, ParticleNet regressed mass]</li>\n</ul>\n\n<p>The code that was used to create Aspen Open Jets from CMS open data can be found at <a href=\"https://github.com/OzAmram/AOJProcessing\">https://github.com/OzAmram/AOJProcessing</a>, and the code used for the OmniJet-alpha model and its training can be found at <a href=\"https://github.com/uhh-pd-ml/omnijet_alpha\">https://github.com/uhh-pd-ml/omnijet_alpha</a>.</p>\n\n<p>&nbsp;</p>","doi":"10.25592/uhhfdm.16505","keywords":["Machine learning","Foundation models","Particle physics","Collider physics","LHC","Open data","Large dataset","Jet physics","Point clouds","Jet tagging","Boosted jets"],"license":{"id":"CC-BY-4.0"},"notes":"This work was initiated at the Aspen Center for Physics, supported by National Science Foundation grant PHY-2210452. We would like to thank Alexander M\u00fcck for discussions. The research of MK and HR-G is supported by the Deutsche Forschungsgemeinschaft DFG under grant 396021762 -- TRR 257: Particle physics phenomenology after the Higgs discovery. JB, AH and GK are supported by the DFG under the German Excellence Initiative -- EXC 2121  Quantum Universe \u2013 390833306, and by PUNCH4NFDI \u2013 project number 460248186. DAF, IP, and DS are supported by DOE grant DOE-SC0010008. OA is supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. LA is supported by the University of Bologna. Additionally, we acknowledge support from the Maxwell computational resources at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany, and computing resources provided by RWTH Aachen University under project rwth0934. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0027491.","publication_date":"2024-12-13","related_identifiers":[{"identifier":"10.25592/uhhfdm.16504","relation":"isVersionOf","scheme":"doi"}],"relations":{"version":[{"count":1,"index":0,"is_last":true,"last_child":{"pid_type":"recid","pid_value":"16505"},"parent":{"pid_type":"recid","pid_value":"16504"}}]},"resource_type":{"title":"Dataset","type":"dataset"},"title":"Aspen Open Jets: a real-world ML-ready dataset for jet physics","version":"1.0"},"owners":[709],"revision":3,"updated":"2024-12-17T14:26:43.230186+00:00"}