The evolution of the Sun's birth cluster and the search for the solar siblings with Gaia

We use self-consistent numerical simulations of the evolution and disruption of the Sun's birth cluster in the Milky Way potential to investigate the present-day phase space distribution of the Sun's siblings. The simulations include the gravitational N-body forces within the cluster and the effects of stellar evolution on the cluster population. In addition the gravitational forces due to the Milky Way potential are accounted for in a self-consistent manner. Our aim is to understand how the astrometric and radial velocity data from the Gaia mission can be used to pre-select solar sibling candidates. We vary the initial conditions of the Sun's birth cluster, as well as the parameters of the Galactic potential. We show that the disruption time-scales of the cluster are insensitive to the details of the non-axisymmetric components of the Milky Way model and we make predictions, averaged over the different simulated possibilities, about the number of solar siblings that should appear in surveys such as Gaia or GALAH. We find a large variety of present-day phase space distributions of solar siblings, which depend on the cluster initial conditions and the Milky Way model parameters. We show that nevertheless robust predictions can be made about the location of the solar siblings in the space of parallaxes ($\varpi$), proper motions ($\mu$) and radial velocities ($V_\mathrm{r}$). By calculating the ratio of the number of simulated solar siblings to that of the number of stars in a model Galactic disk, we find that this ratio is above 0.5 in the region given by: $\varpi \geq 5$mas, $4 \leq \mu \leq 6$masyr$^{-1}$, and $-2\leq V_\mathrm{r} \leq 0$kms$^{-1}$. Selecting stars from this region should increase the probability of success in identifying solar siblings through follow up observations [Abridged].Comment: 13 pages, 7 figures. Accepted for publication in MNRA

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

Crossover of thermal to shot noise in chaotic cavities

We study the crossover between thermal and shot-noise power in a chaotic quantum dot in the presence of non-ideal contacts at finite temperature. The result explicitly demonstrates that the temperature affect the suppression-amplification effect present in the main quantum noise. In particular, the weak localization contribution to the noise has an anomalous thermal behavior when one let the barriers vary, indicating the presence of a critical point related to specific value of the tunneling barriers. We also show how to get to the opaque limit of the quantum dot at finite temperature.Comment: 6 pages, 5 figures. To be published in Europhysics Letter