Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Refining mass formulas for astrophysical applications: a Bayesian neural
  network approach   Exotic nuclei, particularly those near the driplines, are at the core of one
of the fundamental questions driving nuclear structure and astrophysics today:
what are the limits of nuclear binding? Exotic nuclei play a critical role in
both informing theoretical models as well as in our understanding of the origin
of the heavy elements. Our purpose is to refine existing mass models through
the training of an artificial neural network that will mitigate the large model
discrepancies far away from stability. The basic paradigm of our two-pronged
approach is an existing mass model that captures as much as possible of the
underlying physics followed by the implementation of a Bayesian Neural Network
(BNN) refinement to account for the missing physics. Bayesian inference is
employed to determine the parameters of the neural network so that model
predictions may be accompanied by theoretical uncertainties. Despite the
undeniable quality of the mass models adopted in this work, we observe a
significant improvement (of about 40%) after the BNN refinement is implemented.
Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the
rms deviation relative to experiment is reduced from rms =0.503MeV to rms=0.286
MeV. These newly refined mass tables are used to map the neutron drip lines (or
rather "drip bands") and to study a few critical r-process nuclei. The BNN
approach is highly successful in refining the predictions of existing mass
models. In particular, the large discrepancy displayed by the original "bare"
models in regions where experimental data is unavailable is considerably
quenched after the BNN refinement. This lends credence to our approach and has
motivated us to publish refined mass tables that we trust will be helpful for
future astrophysical applications.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph