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Hierarchical $$\mathsf{k}_{\mathsf{t}}$$ jet clustering for parallel architectures. (English) Zbl 1383.81004
Summary: The reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately.
More granular parallelization of the $$\mathsf{k}_{\mathsf{t}}$$ cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The $$\mathsf{k}_{\mathsf{t}}$$ method allows to know the development of particles due to the collision of high-energy nucleus-nucleus.
The hierarchical clustering algorithms works on graphs, so the particle information used by the standard $$\mathsf{k}_{\mathsf{t}}$$ algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit’s standard longitudinal invariant $$\mathsf{k}_{\mathsf{t}}$$ implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be $$1.6$$ times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a $$12$$ times faster computing performance, also being scalable enough to efficiently run on GPUs.
##### MSC:
 81-05 Experimental work for problems pertaining to quantum theory 81Vxx Applications of quantum theory to specific physical systems 65Y10 Numerical algorithms for specific classes of architectures 65Y05 Parallel numerical computation
FastJet
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##### References:
 [1] A. Ali, G. Kramer, Jets and QCD: A historical review of the discovery of the quark and gluon jets and its impact on QCD Eur. Phys. J. H 36 (2011) 245-326. [arXiv:1012.2288 [hep-ph]]. ⇒196 [2] G. Arnison et al. [UA1 Collaboration], Hadronic jet production at the CERN proton-antiproton collider, Phys. Lett. B 132 (1983) 214. ⇒199 [3] R. Atkin, Review of j-et reconstruction algorithms, Journ. of Phys.: Conf. Ser. 645 (2015) 012008. ⇒196, 200 [4] D. Bader, J. McCloskey, Modularity and graph algorithms, SIAM AN10 Min- isymposium on Analyzing Massive Real-World Graphs (2009) 12-16. ⇒204 [5] F. Beaudette [CMS Collaboration], Performance of the particle ow algorithm in CMS, PoS ICHEP 2010 (2010) 002. ⇒197 [6] J.W. Berry, B. Hendrickson, R.A. LaViolette, C. A. Phillips, Tolerating the community detection resolution limit with edge weighting, Phys. Rev. E 83, 5 (2011) 056119. ⇒204 [7] G. C. Blazey, J. R. Dittmann, S. D. Ellis, V. D. Elvira, K. Frame, S. Grinstein, R. Hirosky and R. Piegaia et al., Run II Jet Physics: Proc. of the Run II QCD and Weak Boson Physics Workshop, [arXiv:hep-ex/0005012] ⇒199 [8] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment 10 (2008) doi: [9] M.G. Bowler, Femptophysics, Pergamon Press 1990. ⇒196 [10] U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, D. Wagner, On modularity clustering, IEEE Trans. Knowl. Data Eng. 20, 2 (2008) 172-188. ⇒204 · Zbl 1141.68519 [10] M. Cacciari, G. P. Salam, Phys. Rev. Lett B641 (2006) 57-61 [hep-ph/0512210] ⇒202 [11] M. Cacciari, G. P. Salam, G. Soyez. The anti-Kt jet clustering algorithm, JHEP 0804 (2008) 063 [arXiv:0802.1189 [hep-ph]]. ⇒199, 201 · Zbl 1369.81100 [12] M. Cacciari, G. P. Salam, G. Soyez, FastJet user manual, Eur. Phys. J. C. 72 (2012) 1896, arXiv:1111.6097v1 ⇒200, 202, 209 · Zbl 1393.81007 [13] S. Catani, Y.L. Dokshitzer, M. H. Seymour, B. R. Webber, Longitudinally invariant Kt clustering algorithms for hadron hadron collisions. Nucl. Phys. B 406 (1993) 187224. ⇒199, 201 [14] S. D. Ellis, D. E. Soper, Successive combination jet algorithm for hadron collisions Phys. Rev. D 48, 7 (1993) 3160. ⇒201 [15] S. D. Ellis, J. Huston, K. Hatakeyama, P. Loch, M. Tonnesmann, Jets in hadronhadron collisions Prog. Part. Nucl. Phys. 60 (2008) 484 [arXiv:0712.2447 [hepph]]. ⇒196 [16] R. Forster, Louvain community detection with parallel heuristics on GPUs, 20th Jubilee IEEE Int. Conf. on Intelligent Engineering Systems 20 (2016), ISBN:978- 1-5090-1216-9, doi: [17] R. Forster, A. Fülöp, Parallel kt jet clustering algorithm, Acta Univ. Sapientiae Informatica 9, 1 (2017) 49-64. ⇒196, 209 [18] R. Forster, A. Fülöp, Jet browser model accelerated by GPUs, Acta Univ. Sapi- entiae Informatica 8, 2 (2016) 171-185. ⇒196 · Zbl 1407.82006 [19] R. Forster, A. Fülöp, Yang-Mills lattice on CUDA, Acta Univ. Sapientiae, Inf., 5, 2 (2013) 184-211. ⇒196 · Zbl 1292.81134 [20] S. Fortunato, Community detection in graphs, Phys. Rep. 486, 35 (2010) 75-174, http://dx.doi.org/10.1016/j.physrep.2009.11.002. ⇒204 [21] B. Hendrickson, T. G. Kolda, Graph partitioning models for parallel computing, Parallel Comput. 26, 12 (2000) 1519-1534. ⇒204 · Zbl 0948.68130 [22] M. Hodgkinson, Missing ET performance in ATLAS, in Proc. 34th Interna- tional Conference in High Energy Physics (ICHEP08), Philadelphia, 2008, eConf C080730 [arXiv:hep-ex/0810.0181] ⇒201 [23] H. Lu, M. Halappanavar, A. Kalyanaraman, Parallel heuristics for scalable community detection, Parallel Computing 47 (2015) 1937 ⇒205, 206 [24] S. Moretti, L. Lonnblad, T. Sjostrand, New and old jet clustering algorithms for electron-positron events JHEP 9808 (1998) 001 [arXiv:hep-ph/9804296]. ⇒196 [25] T. Muta, Foundation of Quantum Chrodinamics, World Scientific Press 1986. ⇒196 [26] M.E.J. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E 69, 2 (2004) 026113. ⇒204 [27] M. E. Peskin, D. V. Schroeder, Quantum Field Theory, Westview Press, 1995. ⇒196, 197 [28] D. Rohr, S. Gorbunov, A. Szostak, M. Kretz, T. Kollegger, T. Breitner, T. Alt, ALICE HLT TPC Tracking of Pb-Pb events on GPUs, Journal of Physics: Conference Series 396 (2012) doi: [29] G. P. Salam, Towards Jetography, Eur. Phys. J. C67 (2010) 637-686. [arXiv:0906.1833 [hep-ph]]. ⇒196 [30] G. P. Salam, G. Soyez, A partical seedless infrared-safe cone jet algorithm, JHEP 0705 (2007) 086 [arXiv:0704.0292 [hep-ph]]. ⇒199 [31] G. Sterman, S. Weinberg, Jets from quantum chromodynamics, Phys. Rev. Lett. 39 (1977) 1436. ⇒196 [32] V. A. Traag, P. Van Dooren, Y. Nesterov, Narrow scope for resolution-limit-free community detection, Phys. Rev. E 84, 1 (2011) 016114. ⇒204 [33] CMS collaboration, A Cambridge-Aachen (C-A) based jet algorithm for boosted top-jet tagging. CMS PAS JME-09-001, 2009 ⇒199
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