608
R. Frühwirth et al.
55. A. Strandlie and R. Frühwirth, “Error analysis of the track fit on the Riemann sphere,” Nucl.
Instrum. Meth., vol. A480, p. 734, 2002.
56. A. Strandlie, J. Wroldsen, and R. Frühwirth, “Treatment of multiple scattering with the
generalized Riemann sphere track fit,” Nucl. Instrum. Meth., vol. A488, p. 332, 2002.
57. P. Laurikainen, W. Moorhead, and W. Matt, “Least squares fit of bubble chamber tracks taking
into account multiple scattering,” Nucl. Instrum. Meth., vol. 98, p. 349, 1972.
58. C. Kleinwort, “General broken lines as advanced track fitting method,” Nucl. Instrum. Meth.,
vol. A673, p. 107, 2012.
59. P. Billoir, R. Frühwirth, and M. Regler, “Track Element Merging Strategy and Vertex Fitting
in Complex Modular Detectors,” Nucl. Instrum. Meth., vol. A241, p. 115, 1985.
60. P. Billoir, “Track Fitting With Multiple Scattering: A New Method,” Nucl. Instrum. Meth.,
vol. A225, pp. 352–366, 1984.
61. R. Harr, “Calculation of track and vertex errors for detector design studies,” IEEE Trans.
Nucl. Sci., vol. 42, p. 134, 1995.
62. R. Frühwirth, “Track fitting with non-Gaussian noise,” Comput. Phys. Commun., vol. 100,
p. 1, 1997.
63. A. Strandlie and R. Frühwirth, “Discrimination between different types of material in track
reconstruction with a Gaussian-sum filter,” IEEE Trans. Nucl. Sci., vol. 53, p. 3842, 2006.
64. R. Frühwirth and A. Strandlie, “Track fitting with ambiguities and noise: A study of elastic
tracking and nonlinear filters,” Comput. Phys. Commun., vol. 120, p. 197, 1999.
65. A. Strandlie and R. Frühwirth, “Adaptive multitrack fitting,” Comput. Phys. Commun.,
vol. 133, p. 34, 2000.
66. R. Frühwirth, A. Strandlie, M. Winkler, and T. Todorov, “Recent results on adaptive track and
multitrack fitting,” Nucl. Instrum. Meth., vol. A502, p. 702, 2003.
67. S. Catani, Y. Dokshitzer, M. Olsson, G. Turnock, and B. Webber, “New clustering algorithm
for multi-jet cross-sections in e + e − annihilation,” Phys. Lett., vol. B269, p. 432, 1991.
68. S. Catani, Y. Dokshitzer, M. Seymour, and B. Webber, “Longitudinally invariant k ⊥ clustering
algorithms for hadron-hadron collisions,” Nucl. Phys., vol. B406, p. 187, 1993.
69. S. Moretti, L. Lönnblad, and T. Sjöstrand, “New and old jet clustering algorithms for electronpositron events,” J. High Energy Phys., vol. 08, p. 001, 1998.
70. W. Bartel et al., “Experimental Studies on Multi-Jet Production in e + e − Annihilation at
PETRA Energies,” Z. Phys., vol. C33, p. 23, 1986. [,53(1986)].
71. Y. Dokshitzer, G. Leder, S. Moretti, and B. Webber, “Better jet clustering algorithms,” J.
High Energy Phys., vol. 08, p. 001, 1997.
72. M. Seymour and C. Tevlin, “A Comparison of two different jet algorithms for the top mass
reconstruction at the LHC,” J. High Energy Phys., vol. 11, p. 052, 2006.
73. M. Cacciari and G. Salam, “Dispelling the N 3 myth for the k t jet-finder,” Phys. Lett.,
vol. B641, p. 57, 2006.
74. T. Sjöstrand, “The Lund Monte Carlo for e+ e- Jet Physics,” Comput. Phys. Commun., vol. 28,
p. 229, 1983.
75. S. Bethke, Z. Kunszt, D. Soper, and W. Stirling, “New jet cluster algorithms: Next-to-leading
order QCD and hadronization corrections,” Nucl. Phys., vol. B370, p. 310, 1992. [Erratum:
Nucl. Phys.B523,681(1998)].
76. J. Dorfan, “A Cluster Algorithm for the Study of Jets in High-Energy Physics,” Z. Phys.,
vol. C7, p. 349, 1981.
77. J. Kruskal, “On the shortest spanning subtree of a graph and the traveling salesman problem,”
Proc. Amer. Math. Soc., vol. 7, no. 1, p. 48, 1956.
78. L. Angelini, G. Nardulli, L. Nitti, M. Pellicoro, D. Perrino, and S. Stramaglia, “Deterministic
annealing as a jet clustering algorithm in hadronic collisions,” Phys. Lett., vol. B601, p. 56,
2004.
79. S. Chekanov, “A New jet algorithm based on the k-means clustering for the reconstruction of
heavy states from jets,” Eur. Phys. J., vol. C47, p. 611, 2006.
80. S.-L. Blyth et al., “A Cone jet-finding algorithm for heavy-ion collisions at LHC energies,”
J. Phys., vol. G34, p. 271, 2007.
R. Frühwirth et al.
55. A. Strandlie and R. Frühwirth, “Error analysis of the track fit on the Riemann sphere,” Nucl.
Instrum. Meth., vol. A480, p. 734, 2002.
56. A. Strandlie, J. Wroldsen, and R. Frühwirth, “Treatment of multiple scattering with the
generalized Riemann sphere track fit,” Nucl. Instrum. Meth., vol. A488, p. 332, 2002.
57. P. Laurikainen, W. Moorhead, and W. Matt, “Least squares fit of bubble chamber tracks taking
into account multiple scattering,” Nucl. Instrum. Meth., vol. 98, p. 349, 1972.
58. C. Kleinwort, “General broken lines as advanced track fitting method,” Nucl. Instrum. Meth.,
vol. A673, p. 107, 2012.
59. P. Billoir, R. Frühwirth, and M. Regler, “Track Element Merging Strategy and Vertex Fitting
in Complex Modular Detectors,” Nucl. Instrum. Meth., vol. A241, p. 115, 1985.
60. P. Billoir, “Track Fitting With Multiple Scattering: A New Method,” Nucl. Instrum. Meth.,
vol. A225, pp. 352–366, 1984.
61. R. Harr, “Calculation of track and vertex errors for detector design studies,” IEEE Trans.
Nucl. Sci., vol. 42, p. 134, 1995.
62. R. Frühwirth, “Track fitting with non-Gaussian noise,” Comput. Phys. Commun., vol. 100,
p. 1, 1997.
63. A. Strandlie and R. Frühwirth, “Discrimination between different types of material in track
reconstruction with a Gaussian-sum filter,” IEEE Trans. Nucl. Sci., vol. 53, p. 3842, 2006.
64. R. Frühwirth and A. Strandlie, “Track fitting with ambiguities and noise: A study of elastic
tracking and nonlinear filters,” Comput. Phys. Commun., vol. 120, p. 197, 1999.
65. A. Strandlie and R. Frühwirth, “Adaptive multitrack fitting,” Comput. Phys. Commun.,
vol. 133, p. 34, 2000.
66. R. Frühwirth, A. Strandlie, M. Winkler, and T. Todorov, “Recent results on adaptive track and
multitrack fitting,” Nucl. Instrum. Meth., vol. A502, p. 702, 2003.
67. S. Catani, Y. Dokshitzer, M. Olsson, G. Turnock, and B. Webber, “New clustering algorithm
for multi-jet cross-sections in e + e − annihilation,” Phys. Lett., vol. B269, p. 432, 1991.
68. S. Catani, Y. Dokshitzer, M. Seymour, and B. Webber, “Longitudinally invariant k ⊥ clustering
algorithms for hadron-hadron collisions,” Nucl. Phys., vol. B406, p. 187, 1993.
69. S. Moretti, L. Lönnblad, and T. Sjöstrand, “New and old jet clustering algorithms for electronpositron events,” J. High Energy Phys., vol. 08, p. 001, 1998.
70. W. Bartel et al., “Experimental Studies on Multi-Jet Production in e + e − Annihilation at
PETRA Energies,” Z. Phys., vol. C33, p. 23, 1986. [,53(1986)].
71. Y. Dokshitzer, G. Leder, S. Moretti, and B. Webber, “Better jet clustering algorithms,” J.
High Energy Phys., vol. 08, p. 001, 1997.
72. M. Seymour and C. Tevlin, “A Comparison of two different jet algorithms for the top mass
reconstruction at the LHC,” J. High Energy Phys., vol. 11, p. 052, 2006.
73. M. Cacciari and G. Salam, “Dispelling the N 3 myth for the k t jet-finder,” Phys. Lett.,
vol. B641, p. 57, 2006.
74. T. Sjöstrand, “The Lund Monte Carlo for e+ e- Jet Physics,” Comput. Phys. Commun., vol. 28,
p. 229, 1983.
75. S. Bethke, Z. Kunszt, D. Soper, and W. Stirling, “New jet cluster algorithms: Next-to-leading
order QCD and hadronization corrections,” Nucl. Phys., vol. B370, p. 310, 1992. [Erratum:
Nucl. Phys.B523,681(1998)].
76. J. Dorfan, “A Cluster Algorithm for the Study of Jets in High-Energy Physics,” Z. Phys.,
vol. C7, p. 349, 1981.
77. J. Kruskal, “On the shortest spanning subtree of a graph and the traveling salesman problem,”
Proc. Amer. Math. Soc., vol. 7, no. 1, p. 48, 1956.
78. L. Angelini, G. Nardulli, L. Nitti, M. Pellicoro, D. Perrino, and S. Stramaglia, “Deterministic
annealing as a jet clustering algorithm in hadronic collisions,” Phys. Lett., vol. B601, p. 56,
2004.
79. S. Chekanov, “A New jet algorithm based on the k-means clustering for the reconstruction of
heavy states from jets,” Eur. Phys. J., vol. C47, p. 611, 2006.
80. S.-L. Blyth et al., “A Cone jet-finding algorithm for heavy-ion collisions at LHC energies,”
J. Phys., vol. G34, p. 271, 2007.