Advertisement

Full Event Interpretation

  • Thomas KeckEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

The Full Event Interpretation (FEI) is a tagging algorithm based on machine learning. It exploits the unique experimental setup of \(\text {B}\) factory experiments such as the Belle and Belle II experiment. Both experiments operate on the \(\Upsilon (4\text {S})\) resonance, which decays at least \(96 \%\) of the time into exactly two \(\text {B}\) mesons. Conceptually, the event is divided into two sides: The signal-side containing the tracks and clusters compatible with the assumed signal \(\text {B}_{\mathrm {sig}} \) decay the physicist is interested in, e.g. \(\text {B}^{+}\rightarrow \tau ^{+}\nu _{\tau }\); and the tag-side containing the remaining tracks and clusters compatible with an arbitrary \(\text {B}_{\mathrm {tag}} \) meson decay. Figure 4.1 depicts this situation.

References

  1. 1.
    A.J. Bevan et al., The physics of the B factories. Eur. Phys. J. C 74, 3026 (2014).  https://doi.org/10.1140/epjc/s10052-014-3026-9ADSCrossRefGoogle Scholar
  2. 2.
    T. Keck, The full event interpretation for Belle II. M.A. thesis. KIT, 2014, https://ekp-invenio.physik.uni-karlsruhe.de/record/48602
  3. 3.
    M. Feindt et al., A hierarchical neurobayes-based algorithm for full reconstruction of B mesons at B factories. Nucl. Instrum. Methods A654, 432–440 (2011).  https://doi.org/10.1016/j.nima.2011.06.008ADSCrossRefGoogle Scholar
  4. 4.
    W.D. Hulsbergen, Decay chain fitting with a Kalman filter. Nucl. Instrum. Methods A552, 566–575 (2005).  https://doi.org/10.1016/j.nima.2005.06.078ADSCrossRefGoogle Scholar
  5. 5.
    W. Waltenberger, W. Mitaroff, F. Moser, RAVE—a Detector-independent vertex reconstruction toolkit. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 581(1–2), 549–552 (2007),  https://doi.org/10.1016/j.nima.2007.08.048ADSCrossRefGoogle Scholar
  6. 6.
    R. Fruhwirth, Application of Kalman filtering to track and vertex fitting. Nucl. Instrum. Methods (1987).  https://doi.org/10.1016/0168-9002(87)90887-4CrossRefGoogle Scholar
  7. 7.
    G. M. Amdahl, Validity of the single processor approach to achieving large scale computing capabilities, in Proceedings of the Spring Joint Computer Conference, pp. 483–485, 18–20 April 1967,  https://doi.org/10.1145/1465482.1465560
  8. 8.
  9. 9.
    K. Kirchgessner, Semileptonic tag side reconstruction. M.A. thesis. KIT, 2012, http://ekp-invenio.physik.uni-karlsruhe.de/record/48181
  10. 10.
    I. Adachi et al., Belle design study report (2008), arXiv: 0810.4084 [hep-ex]
  11. 11.
    B. Kronenbitter et al., Measurement of the branching fraction of \(B^{+}\rightarrow \tau ^{+}\nu _\tau \) decays with the semileptonic tagging method. Phys. Rev. D 92(5), 051102 (2015).  https://doi.org/10.1103/PhysRevD.92.051102ADSCrossRefGoogle Scholar
  12. 12.
    J. Schwab, Calibration of the full event interpretation for the Belle and the Belle II experiment. M.A. thesis, KIT, 2017, https://ekp-invenio.physik.uni-karlsruhe.de/record/48931
  13. 13.
    A. Sibidanov et al., Study of exclusive \(B \rightarrow X_{u}l\nu \) decays and extraction of \(|V_{ub}|\) using full reconstruction tagging at the Belle experiment. Phys. Rev. D (2013).  https://doi.org/10.1103/PhysRevD.88.032005CrossRefGoogle Scholar
  14. 14.
    J. Grygier, Search for \(B\rightarrow h\nu \nu \) decays with semileptonic tagging at Belle (2017), arXiv: 1702.03224 [hep-ex]
  15. 15.
    A. Heller, Search for \(B^{+} \rightarrow \ell ^{+} \nu \gamma \) decays with hadronic tagging using the full Belle data sample. Ph.D. thesis. KIT, 2015, https://ekp-invenio.physik.unikarlsruhe.de/record/48743
  16. 16.
    M. Huschle, Measurement of the branching ratio of \(B \rightarrow D^{(*)}\tau \nu _\tau \) relative to \(B \rightarrow D^{(*)}\ell \nu _\ell \) decays with hadronic tagging at Belle. Ph.D. thesis. KIT, 2015, https://ekp-invenio.physik.uni-karlsruhe.de/record/48622
  17. 17.
    Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015).  https://doi.org/10.1038/nature14539ADSCrossRefGoogle Scholar
  18. 18.
    A. Santoro et al., A simple neural network module for relational reasoning (2017), arXiv: 1706.01427 [cs.CL]
  19. 19.
    J. Schmidhuber, Deep learning in neural networks: An overview. Neural Netw. (2015).  https://doi.org/10.1016/j.neunet.2014.09.003CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Experimental Particle PhysicsKarlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations