Daniel Murnane

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Paolo CalafiuraSenior Scientist, Lawrence Berkeley National LaboratoryVerified email at lbl.gov
Xiangyang JuLawrence Berkeley National LaboratoryVerified email at cern.ch
Steven FarrellLawrence Berkeley National LaboratoryVerified email at lbl.gov
Lindsey GrayScientist, FermilabVerified email at fnal.gov
Jean-Roch VlimantCalifornia Insitute of TechnologyVerified email at caltech.edu
Thomas KlijnsmaResearch Associate, Fermi National Accelerator LaboratoryVerified email at fnal.gov
Savannah ThaisColumbia UniversityVerified email at columbia.edu
Mark NeubauerUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Kazuhiro TeraoSLAC National Accelerator Laboratory / Stanford UniversityVerified email at nevis.columbia.edu
Alina LazarProfessor of Computer Science, Youngstown State UniversityVerified email at ysu.edu
Gage DeZoortDepartment of Physics, Princeton UniversityVerified email at princeton.edu
Sean ConlonUniversity of California, BerkeleyVerified email at berkeley.edu
Aristeidis TsarisOak Ridge National LaboratoryVerified email at ornl.gov
Nicholas ChomaNew York UniversityVerified email at cs.nyu.edu
Shih-Chieh HsuProfessor of Physics, University of WashingtonVerified email at uw.edu
Joosep Patasenior researcher, KBFIVerified email at kbfi.ee
Sylvain CaillouIngénieur de recherche, CNRSVerified email at l2it.in2p3.fr
Anthony G WilliamsProfessor of Physics, University of AdelaideVerified email at adelaide.edu.au
Sanmay GangulyProject Assistant Professor, International Center for Elementary Particle Physics - University of TokyoVerified email at icepp.s.u-tokyo.ac.jp

Daniel Murnane

Niels Bohr Institute, University of Copenhagen & Berkeley Lab

Verified email at nbi.ku.dk - Homepage

graph neural networks particle track detection high energy physics phenomenology composite


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Graph neural networks for particle reconstruction in high energy physics detectors X Ju, S Farrell, P Calafiura, D Murnane, L Gray, T Klijnsma, K Pedro, ... arXiv preprint arXiv:2003.11603, 2020	124	2020
ColliderBit: a GAMBIT module for the calculation of high-energy collider observables and likelihoods GAMBIT Scanner Workgroup:, C Balázs, A Buckley, LA Dal, B Farmer, ... The European Physical Journal C 77, 1-36, 2017	105	2017
Performance of a geometric deep learning pipeline for HL-LHC particle tracking X Ju, D Murnane, P Calafiura, N Choma, S Conlon, S Farrell, Y Xu, ... The European Physical Journal C 81, 1-14, 2021	62	2021
Graph neural networks in particle physics: Implementations, innovations, and challenges S Thais, P Calafiura, G Chachamis, G DeZoort, J Duarte, S Ganguly, ... arXiv preprint arXiv:2203.12852, 2022	29	2022
Symmetry group equivariant architectures for physics A Bogatskiy, S Ganguly, T Kipf, R Kondor, DW Miller, D Murnane, ... arXiv preprint arXiv:2203.06153, 2022	24	2022
Track seeding and labelling with embedded-space graph neural networks N Choma, D Murnane, X Ju, P Calafiura, S Conlon, S Farrell, G Cerati, ... arXiv preprint arXiv:2007.00149, 2020	22	2020
Accelerating the inference of the Exa. TrkX pipeline A Lazar, X Ju, D Murnane, P Calafiura, S Farrell, Y Xu, M Spiropulu, ... Journal of Physics: Conference Series 2438 (1), 012008, 2023	14	2023
Graph neural network for object reconstruction in liquid argon time projection chambers J Hewes, A Aurisano, G Cerati, J Kowalkowski, C Lee, W Liao, A Day, ... EPJ Web of Conferences 251, 03054, 2021	10	2021
Constraining fine tuning in Composite Higgs Models with partially composite leptons J Barnard, D Murnane, M White, AG Williams Journal of High Energy Physics 2017 (9), 1-31, 2017	9	2017
Exploring fine-tuning of the next-to-minimal composite Higgs model D Murnane, M White, AG Williams Journal of High Energy Physics 2019 (4), 1-27, 2019	7	2019
Heterogeneous graph neural network for identifying hadronically decayed tau leptons at the high luminosity LHC A Huang, X Ju, J Lyons, D Murnane, M Pettee, L Reed Journal of Instrumentation 18 (07), P07001, 2023	6	2023
Semi-equivariant GNN architectures for jet tagging D Murnane, S Thais, J Wong Journal of Physics: Conference Series 2438 (1), 012121, 2023	6	2023
Graph neural networks for particle reconstruction in high energy physics detectors (2020) X Ju, S Farrell, P Calafiura, D Murnane, LG Prabhat, T Klijnsma, K Pedro, ... arXiv preprint arXiv:2003.11603, 0	6
Hierarchical Graph Neural Networks for Particle Track Reconstruction R Liu, P Calafiura, S Farrell, X Ju, DT Murnane, TM Pham 21th International Workshop on Advanced Computing and Analysis Techniques in …, 2023	5	2023
ATLAS ITk Track Reconstruction with a GNN-based pipeline S Caillou, P Calafiura, C Rougier, J Stark, DT Murnane, A Vallier, X Ju, ... ATL-COM-ITK-2022-057, 2022	5	2022
Equivariant graph neural networks for charged particle tracking D Murnane, S Thais, A Thete arXiv preprint arXiv:2304.05293, 2023	4	2023
Convergent Bayesian global fits of 4D composite Higgs models E Carragher, W Handley, D Murnane, P Stangl, W Su, M White, ... Journal of High Energy Physics 2021 (5), 1-77, 2021	3	2021
The landscape of composite Higgs models DT Murnane	3	2019
Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain S Caillou, P Calafiura, C Rougier, MT Pham, J Stark, DT Murnane, ... ATL-COM-SOFT-2023-124, 2023	2	2023
Reconstruction of Large Radius Tracks with the Exa. TrkX pipeline CY Wang, X Ju, SC Hsu, D Murnane, P Calafiura, S Farrell, M Spiropulu, ... Journal of Physics: Conference Series 2438 (1), 012117, 2023	2	2023

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