Orateur
M.
Hongliang LÜ
(GANIL and Normandie University)
Description
One of the biggest challenges in nuclear physics
is to synthesize new super-heavy elements (SHE)
and thus to extend the periodic table. It is
commonly known that SHE cannot be found in nature.
According the shell model, they have been predicted
to exist thanks to a regain of stability. Experimentally,
by making use of heavy-ion collisions, SHE can be
artificially produced in laboratories. However, owing
to extremely low formation probabilities, such
experiments become increasingly difficult.
More powerful experimental set-ups are therefore
needed to overcome this issue. For instance, within
the promising project SPIRAL2 at GANIL, the study
of super-heavy elements has been selected as one
of priority research subjects for the coming years.
On the other hand, the complete reaction mechanism
for the synthesis of super-heavy elements still
remains unclear. One of the most serious problems is the
so-called fusion hindrance phenomenon that is
well understood only qualitatively but not
quantitatively. Many theoretical attempts have
been made over the past few decades. However,
there exist large discrepancies in the predictions
provided by different theoretical models. More concretely,
this might be due to either uncertainties
in model parameters or models themselves.
Hence, a natural question arises: How to
clarify both uncertainty contributions
so as to constrain the fusion models?
Using the Monte-Carlo method,
we tried to estimate the uncertainties related
to the parameters entering the model, in order
to investigate whether they can explain
such discrepancies observed among various
fusion models. Here, we mainly focused on the
last phase of the reaction, namely statistical decay
of the compound nucleus, by means of the KEWPIE2 code.
Moreover, we only have access to very few and
poor experimental data. Another interesting
question needed to be addressed is that, what
can we learn about model parameters from
experimental data? As an inverse problem,
Bayesian statistics would be a perfect
candidate. I will also give a brief
introduction to this theory as well as
some simple applications. Our recent work
will be presented at the end of this talk.
Auteur principal
M.
Hongliang LÜ
(GANIL and Normandie University)
Co-auteur
Dr
David BOILLEY
(GANIL and Normandie University)
