Abstract
A new jet clustering algorithm - ARCLUS - is presented. The main difference between this and conventional algorithms is that while the latter in each step join two clusters into one, ARCLUS joins three clusters into two. The performance of ARCLUS in terms of the size of hadronization corrections is studied for some jet-reconstruction tasks in e{sup +}e{sup -} and ep collisions, and is found to be as good as or, for some tasks in ep collisions, better than conventional clustering algorithms. (orig.).
Citation Formats
Loennblad, L.
ARCLUS. A new jet clustering algorithm inspired by the colour dipole model.
Germany: N. p.,
1992.
Web.
Loennblad, L.
ARCLUS. A new jet clustering algorithm inspired by the colour dipole model.
Germany.
Loennblad, L.
1992.
"ARCLUS. A new jet clustering algorithm inspired by the colour dipole model."
Germany.
@misc{etde_10148184,
title = {ARCLUS. A new jet clustering algorithm inspired by the colour dipole model}
author = {Loennblad, L}
abstractNote = {A new jet clustering algorithm - ARCLUS - is presented. The main difference between this and conventional algorithms is that while the latter in each step join two clusters into one, ARCLUS joins three clusters into two. The performance of ARCLUS in terms of the size of hadronization corrections is studied for some jet-reconstruction tasks in e{sup +}e{sup -} and ep collisions, and is found to be as good as or, for some tasks in ep collisions, better than conventional clustering algorithms. (orig.).}
place = {Germany}
year = {1992}
month = {Dec}
}
title = {ARCLUS. A new jet clustering algorithm inspired by the colour dipole model}
author = {Loennblad, L}
abstractNote = {A new jet clustering algorithm - ARCLUS - is presented. The main difference between this and conventional algorithms is that while the latter in each step join two clusters into one, ARCLUS joins three clusters into two. The performance of ARCLUS in terms of the size of hadronization corrections is studied for some jet-reconstruction tasks in e{sup +}e{sup -} and ep collisions, and is found to be as good as or, for some tasks in ep collisions, better than conventional clustering algorithms. (orig.).}
place = {Germany}
year = {1992}
month = {Dec}
}