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Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression

Yıl 2018, Cilt 42, Sayı 3, 273 - 282, 01.06.2018

Öz

  The investigation and analysis of the correlation between the mean transverse momentum (<PT>) of charged particles and the charged particle multiplicity (Nch) allow physicists to understand the contribution of multiple-parton interactions to the particle production mechanism. A symbolic regression (SR) method, based on gene expression programming (GEP), is proposed for mining a function that describes the <PT>-Nch correlation in proton-proton and proton-antiproton (pp and pp(bar)) collisions at collision energies from IRS to LHC. The discovered function simulates and models the correlation between <PT> and Nch in wide energy range s1/2. In the framework of the proposed GEP model for <PT>Nch correlation, the equation obtained describes the main features of the experimental data. Predictions for <PT>Nch correlations at the future LHC collision energy of 14 TeV are obtained. The accuracy of the calculated and predicted results is assessed by comparing them with the available experimental data and the theoretical ones.

Kaynakça

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  • Schmidt, M.; Lipson, H. Science 2009 , 324 , 81-85.
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  • Bodnya, E. O.; Kovalenko, V. N.; Puchkov, A. M.; Feo lov, G. A. In AIP Conference Proceedings , vol. 1606, no. 1, 2014, pp. 273-282.
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  • Bohm, G.; Zech, G. Introduction to Statistics and Measurement Analysis for Physicists. DESY: Hamburg Germany, 2010.
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  • Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.;. Abolin, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; et al. ATLAS collaboration, Phys. Lett. B 2016 , 758 , 67-88.
  • Sherrod, P. DTREG Predictive Modeling Software (2008), Manual for software available online: www.dtreg.com.
  • Friedrich, R.; Siegert, S.; Peinke, J.; Luck, S.; Siefert, M.; Lindemann, M. M; Raethjen, J.; Deuschl, G.; P ster G. Phys. Lett. A 2000 , 271 , 217-222.
  • Darwin, C. On the Origin of Species by Means of Natural Selection, or, the Preservation of Favoured Races in the Struggle for Life ; London, UK: J. Murray, 1859.
  • Mendel, G. Experiments in Plant Hybridization (pp.1-39). Brunn: Proceedings of the Natural History Society of Brunn. 1865, 3-47.

Yıl 2018, Cilt 42, Sayı 3, 273 - 282, 01.06.2018

Öz

Kaynakça

  • Rutherford, E. Philos. Mag . 1911 , 21 , 669-688.
  • Bohr, N. Philos. Mag . 1913 , 25 , 10-31.
  • Bustamante, M.; Cieri, L.; Ellis, J. arXiv: hep-ph/0911.4409 (2012) .
  • Feynman, R. P. Int. J. Theor. Phys. 1982 , 21 , 467-488.
  • Dzeroski, S.; Langley, P.; Todorovski, L. In Computational Discovery of Scienti c Knowledge ; Dzeroski, S.; Todor- ovski, L., Eds. Springer: Berlin, Germany, 2007, pp. 69-97.
  • Todorovski, L.; Dzeroski, S. In Proceedings of the Fourteenth International Conference on Machine Learning, 1997. San Mateo: Morgan Kaufmann Publishers Inc., 1997, p. 376.
  • Haykin, S. Neural Networks and Learning Machines ; Prentice Hall: Upper Saddle River, NJ, USA, 2008.
  • Holland, J. H. Adaptation in Natural and Arti cial Systems; an Introductory Analysis with Application to Biology, Control, and Arti cial Intelligence ; University of Michigan Press: Ann Arbor, MI, USA, 1975.
  • Koza, J. R.; Keane, M. A.; Streeter, M. J.; Mydlowec, W.; Yu, J.; Lanza, G. Genetic Programming IV: Routine Human-Competitive Machine Intelligence , Vol. 5. Springer Science & Business Media: Berlin, Germany, 2006.
  • Link, J. M.; Yager, P. M.; Anjos, J. C.; Bediaga, I.; Castromonte, C.; Gobel, C.; Pepe, I. M. Nucl. Instr. Meth. Phys. Res. A 2005 , 551 , 504-527.
  • Teodorescu, L.; Sherwood, D. Comp. Phys. Comm . 2008, 178 , 409-419.
  • El-dahshan, E.; Radi, A.; El-Bakry, M. Y. Int. J. Mod. Phys. C 2009 , 20 , 1817-1825.
  • El-dahshan, E.; Radi, A.; El-Bakry, M. Y. Int. J. Mod. Phys. C 2008 , 19 , 1787-1795.
  • Schmidt, M; Lipson, H. In Genetic Programming Theory and Practice VII, Genetic and Evolutionary Computation ; Riolo, R.; O'Reilly, U. M.; McConaghy, T. Eds. Springer: Ann Arbor, MI, USA, 2010, pp. 73-85.
  • Kiesling, C.; Zimmermann, J. In Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004 . Proceedings of the 2004 IEEE, pp. 325.
  • Ferreira, C. Gene Expression Programming: Mathematical Modeling by an Arti cial Intelligence (Vol. 21) , 2nd ed.; Springer-Verlag: Berlin, Germany, 2006.
  • Schmidt, M.; Lipson, H. Science 2009 , 324 , 81-85.
  • Derouich, M.; Radi, A.; Barklem, P. S. Astron. Astrophys. 2015 , 584 , A64.
  • Cai, W.; Pacheco-Vega, A.; Sen, M.; Yang, K. T. Int. J. Heat Mass. Tran . 2006 , 49 , 4352-4359.
  • Quade, M.; Abel, M.; Sha , K.; Niven, R. K.; Noack, B. R. Phys. Rev. E , 2016 , 94 , 012214-1-012214-15.
  • Hills, D. J.; Grutter, A. M.; Hudson, J. J. Peer J. Comput. Sci. 2015 , 1 , e31.
  • Kurse, M. U.; Lipson, H.; Valero-Cuevas, F. J. IEEE T. Bio. Eng. 2012 , 59 , 1572-1582.
  • Yang, G.; Li, X.; Wang, J.; Lian, L.; Ma, T. Energy Policy 2015 , 82 , 48-61.
  • Murari, A.; Peluso, E.; Gelfusa, M; Lupelli, I.; Lungaroni, M.; Gaudio, P. Plasma Phys. Control. Fusion 2015 , 57 , 014008.
  • Golafshani, E. M.; Ashour, A. Automat. Constr. 2016 , 64 , 7-19.
  • Guven, A.; Aytek, A. Eng. J. Hydraul Eng. 2009 , 14 , 812-820.
  • Guven, A.; Aytek, A.; Yuce, M. I.; Aksoy, H. Clean-Soil Air Water 2008 , 36 , 905-912.
  • Arnison, G.; Astbury, A.; Aubert, B.; Bacci, C.; Bernabei, R.; Bezaguet, A.; Bock, R.; Bowcock, T. J. V.; Calvetti, M.; Carroll, T.; et al. Phys. Lett. B 1982 , 118 , 167-172.
  • Alexopoulos, T.; Allen, C.; Anderson, E. W.; Areti, H.; Banerjee, S.; Beery, P. D.; Cole, P.; Choi, Y.; De Bonte, R.; Erwin, A.; et al. Phys. Rev. Lett. 1988 , 60 , 1622.
  • Breakstone, A.; Campanini, R.; Crawley, H. B.; Dallavalle, G. M.; Deninno, M. M.; Doroba, K.; Drijard, D.; Fabbri, F.; Firestone, A.; Fischer, H. G.; et al. Phys. Lett. B 1983 , 132 , 458-462.
  • D'Enterria, D.; Engel, R.; Pierog, T.; Ostapchenko, S.; Werner, K. Astropart Phys. 2011 , 35 , 98-113.
  • Monteno, Marco, and ALICE Collaboration, Journal of Physics: Conference Series. Vol. 5. No. 1. IOP Publishing: Bristol, UK, 2005.
  • Bodnya, E. O.; Kovalenko, V. N.; Puchkov, A. M.; Feo lov, G. A. In AIP Conference Proceedings , vol. 1606, no. 1, 2014, pp. 273-282.
  • Luettig, P.; Appelshaeuser, H.; Buesching, H.; Schuchmann, S.; Otwinowski, J. Correlation between mean transverse momentum and multiplicity in pp collisions at p s = 900 GeV in ALICE  . GSI Scienti c Report 2009. 2010, p. 247.
  • McLerran, L.; Praszalowicz, M. arXiv: hep-ph/1006.4293 (2010).
  • Bohm, G.; Zech, G. Introduction to Statistics and Measurement Analysis for Physicists. DESY: Hamburg Germany, 2010.
  • Bodnia, E.; Derkach, D.; Feo lov, G.; Kovalenko, V.; Puchkov, A. arXiv: hep-ph /1310.1627 (2013).
  • Armesto, N.; Derkach, D. A.; Feo lov, G. A. Phys. Atom. Nuclei 2008 , 71 , 2087-2095.
  • Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; Abou Zeid, O. S.; Abraham, N. L.; Abramowicz, H.; et al. ATLAS Collaboration: Phys. Lett. B 2016 , 758 , 67-88.
  • Piparo, D. Measurements of Hadron Production and Underlying Event Studies at CMS . (No. CMS-CR-2010-244). 2010.
  • Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Aguilo, E.; Bergauer, T.; Dragicevic, M.; Ero, J.; Fabjan, C.; et al. The CMS Collaboration, Eur. Phys. J. C. 2012 , 72 .10, 1-37.
  • Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M., Ero, J.; Fabjan, C.; Friedl, M.; Fruhwirth, R.; et al. CMS Collaboration, Phys. Rev. Lett. 2010 , 105 , 022002-1-022002-15.
  • Abelev, B.; Adam, J.; Adamova, D.; Adare, A. M.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agocs, A. G.; Agostinelli, A.; Ahammed, Z.; et al. ALICE collaboration, Phys. Lett. B 2013 , 727 , 371-380.
  • Tasevsky, M. arXiv: hep-ex/1802.02818 ( 2018 ).
  • Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.;. Abolin, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; et al. ATLAS collaboration, Phys. Lett. B 2016 , 758 , 67-88.
  • Sherrod, P. DTREG Predictive Modeling Software (2008), Manual for software available online: www.dtreg.com.
  • Friedrich, R.; Siegert, S.; Peinke, J.; Luck, S.; Siefert, M.; Lindemann, M. M; Raethjen, J.; Deuschl, G.; P ster G. Phys. Lett. A 2000 , 271 , 217-222.
  • Darwin, C. On the Origin of Species by Means of Natural Selection, or, the Preservation of Favoured Races in the Struggle for Life ; London, UK: J. Murray, 1859.
  • Mendel, G. Experiments in Plant Hybridization (pp.1-39). Brunn: Proceedings of the Natural History Society of Brunn. 1865, 3-47.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

El-sayed EL-DAHSHAN Bu kişi benim

Yayımlanma Tarihi 1 Haziran 2018
Yayınlandığı Sayı Yıl 2018, Cilt 42, Sayı 3

Kaynak Göster

Bibtex @araştırma makalesi { tbtkphysics490310, journal = {Turkish Journal of Physics}, issn = {1300-0101}, eissn = {1303-6122}, address = {}, publisher = {TÜBİTAK}, year = {2018}, volume = {42}, number = {3}, pages = {273 - 282}, title = {Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression}, key = {cite}, author = {El-dahshan, El-sayed} }
APA El-dahshan, E. (2018). Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression . Turkish Journal of Physics , 42 (3) , 273-282 . Retrieved from https://dergipark.org.tr/tr/pub/tbtkphysics/issue/40696/490310
MLA El-dahshan, E. "Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression" . Turkish Journal of Physics 42 (2018 ): 273-282 <https://dergipark.org.tr/tr/pub/tbtkphysics/issue/40696/490310>
Chicago El-dahshan, E. "Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression". Turkish Journal of Physics 42 (2018 ): 273-282
RIS TY - JOUR T1 - Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression AU - El-sayedEl-dahshan Y1 - 2018 PY - 2018 N1 - DO - T2 - Turkish Journal of Physics JF - Journal JO - JOR SP - 273 EP - 282 VL - 42 IS - 3 SN - 1300-0101-1303-6122 M3 - UR - Y2 - 0201 ER -
EndNote %0 Turkish Journal of Physics Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression %A El-sayed El-dahshan %T Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression %D 2018 %J Turkish Journal of Physics %P 1300-0101-1303-6122 %V 42 %N 3 %R %U
ISNAD El-dahshan, El-sayed . "Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression". Turkish Journal of Physics 42 / 3 (Haziran 2018): 273-282 .
AMA El-dahshan E. Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression. Turkish Journal of Physics. 2018; 42(3): 273-282.
Vancouver El-dahshan E. Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression. Turkish Journal of Physics. 2018; 42(3): 273-282.
IEEE E. El-dahshan , "Function mining for the < P T > -N ch correlations in pp and pp(bar) collisions based on symbolic regression", Turkish Journal of Physics, c. 42, sayı. 3, ss. 273-282, Haz. 2018