9,649 research outputs found
The solar siblings in the Gaia era
We perform realistic simulations of the Sun's birth cluster in order to
predict the current distribution of solar siblings in the Galaxy. We study the
possibility of finding the solar siblings in the Gaia catalogue by using only
positional and kinematic information. We find that the number of solar siblings
predicted to be observed by Gaia will be around 100 in the most optimistic
case, and that a phase space only search in the Gaia catalogue will be
extremely difficult. It is therefore mandatory to combine the chemical tagging
technique with phase space selection criteria in order to have any hope of
finding the solar siblings.Comment: To be published in the proceedings of the GREAT-ITN conference "The
Milky Way Unravelled by Gaia: GREAT Science from the Gaia Data Releases", 1-5
December 2014, University of Barcelona, Spain, EAS Publications Series, eds
Nicholas Walton, Francesca Figueras, and Caroline Soubira
Time-Reversal Symmetry Breaking and Decoherence in Chaotic Dirac Billiards
In this work, we perform a statistical study on Dirac Billiards in the
extreme quantum limit (a single open channel on the leads). Our numerical
analysis uses a large ensemble of random matrices and demonstrates the
preponderant role of dephasing mechanisms in such chaotic billiards. Physical
implementations of these billiards range from quantum dots of graphene to
topological insulators structures. We show, in particular, that the role of
finite crossover fields between the universal symmetries quickly leaves the
conductance to the asymptotic limit of unitary ensembles. Furthermore, we show
that the dephasing mechanisms strikingly lead Dirac billiards from the extreme
quantum regime to the semiclassical Gaussian regime
Modeling the input history of programs for improved instruction-memory performance
When a program is loaded into memory for execution, the relative position of
its basic blocks is crucial, since loading basic blocks that are unlikely to be
executed first places them high in the instruction-memory hierarchy only to be
dislodged as the execution goes on. In this paper we study the use of Bayesian
networks as models of the input history of a program. The main point is the
creation of a probabilistic model that persists as the program is run on
different inputs and at each new input refines its own parameters in order to
reflect the program's input history more accurately. As the model is thus
tuned, it causes basic blocks to be reordered so that, upon arrival of the next
input for execution, loading the basic blocks into memory automatically takes
into account the input history of the program. We report on extensive
experiments, whose results demonstrate the efficacy of the overall approach in
progressively lowering the execution times of a program on identical inputs
placed randomly in a sequence of varied inputs. We provide results on selected
SPEC CINT2000 programs and also evaluate our approach as compared to the gcc
level-3 optimization and to Pettis-Hansen reordering
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