Speaker
Dr
James Monk
(MCnet/Cedar)
Description
Data analyses in hadron collider physics depend on background simulations
performed by Monte Carlo (MC) event generators. However, calculational
limitations and non-perturbative effects require approximate models with
adjustable parameters. In fact, we need to simultaneously tune many
phenomenological parameters in a high-dimensional parameter-space
in order to make the MC generator predictions fit the data. It
is desirable to achieve this goal without spending too much time or computing
resources iterating parameter settings and comparing the same set of plots over
and over again.
I will present extensions and improvements to the MC tuning system, Professor,
which addresses the aforementioned problems by constructing a fast analytic model of a MC
generator which can then be easily fitted to data. Using this procedure it is
for the first time possible to get a robust estimate of the uncertainty of
generator tunings. Furthermore, we can use these uncertainty estimates to study the
effect of new (pseudo-) data on the quality of tunings and therefore decide if a
measurement is worthwhile in the prospect of generator tuning. The potential
of the Professor method outside the MC tuning area is presented as well.
Authors
Dr
Andy Buckley
(University of Edinburgh, UK)
Prof.
Heiko Lacker
(Humboldt-Universität zu Berlin)
Dr
Hendrik Hoeth
(IPPP, Durham, UK)
Dr
Holger Schulz
(Humboldt-Universität zu Berlin)
Dr
James Monk
(MCnet/Cedar)
Dr
Jan-Eike von Seggern
(Humboldt-Universität zu Berlin)
