36,429 research outputs found
Using Java for plasma PIC simulations
Plasma particle-in-cell (PIC) simulations model the interactions of charged particles with the surrounding fields. This application has been recognized as one of the grand challenge problems facing the high-performance computing community due to its huge computational requirements. Recently, with the explosive development of Internet, Java is receiving increasing attention and is thought as a potential candidate for high-performance computing. In this paper, we present our approach to developing 2- and 3-dimensional parallel PIC simulations in Java. We also report the execution times for both versions from performance experiments on a symmetric multi-processor (Sun E6500) and a Linux cluster of Pentium III machines. Those results are also compared with benchmark measurements of the corresponding Fortran version of the same algorithm
Microlensing of Sub-parsec Massive Binary Black Holes in Lensed QSOs: Light Curves and Size-Wavelength Relation
Sub-parsec binary massive black holes (BBHs) are long anticipated to exist in
many QSOs but remain observationally elusive. In this paper, we propose a novel
method to probe sub-parsec BBHs through microlensing of lensed QSOs. If a QSO
hosts a sub-parsec BBH in its center, it is expected that the BBH is surrounded
by a circum-binary disk, each component of the BBH is surrounded by a small
accretion disk, and a gap is opened by the secondary component in between the
circum-binary disk and the two small disks. Assuming such a BBH structure, we
generate mock microlensing light curves for some QSO systems that host BBHs
with typical physical parameters. We show that microlensing light curves of a
BBH QSO system at the infrared-optical-UV bands can be significantly different
from those of corresponding QSO system with a single massive black hole (MBH),
mainly because of the existence of the gap and the rotation of the BBH (and its
associated small disks) around the center of mass. We estimate the half-light
radii of the emission region at different wavelengths from mock light curves
and find that the obtained half-light radius vs. wavelength relations of BBH
QSO systems can be much flatter than those of single MBH QSO systems at a
wavelength range determined by the BBH parameters, such as the total mass, mass
ratio, separation, accretion rates, etc. The difference is primarily due to the
existence of the gap. Such unique features on the light curves and half-light
radius-wavelength relations of BBH QSO systems can be used to select and probe
sub-parsec BBHs in a large number of lensed QSOs to be discovered by current
and future surveys, including the Panoramic Survey Telescope and Rapid Response
System (Pan-STARRS), the Large Synoptic Survey telescope (LSST) and Euclid.Comment: 18 pages, 17 figures, accepted for publication in the Astrophysical
Journa
Tensor Rank Estimation and Completion via CP-based Nuclear Norm
Tensor completion (TC) is a challenging problem of recovering
missing entries of a tensor from its partial observation. One main
TC approach is based on CP/Tucker decomposition. However, this
approach often requires the determination of a tensor rank a priori.
This rank estimation problem is difficult in practice. Several
Bayesian solutions have been proposed but they often under/overestimate
the tensor rank while being quite slow. To address this
problem of rank estimation with missing entries, we view the weight
vector of the orthogonal CP decomposition of a tensor to be analogous
to the vector of singular values of a matrix. Subsequently,
we define a new CP-based tensor nuclear norm as the L1-norm of
this weight vector. We then propose Tensor Rank Estimation based
on L1-regularized orthogonal CP decomposition (TREL1) for both
CP-rank and Tucker-rank. Specifically, we incorporate a regularization
with CP-based tensor nuclear norm when minimizing the
reconstruction error in TC to automatically determine the rank of
an incomplete tensor. Experimental results on both synthetic and
real data show that: 1) Given sufficient observed entries, TREL1 can
estimate the true rank (both CP-rank and Tucker-rank) of incomplete
tensors well; 2) The rank estimated by TREL1 can consistently
improve recovery accuracy of decomposition-based TC methods;
3) TREL1 is not sensitive to its parameters in general and more
efficient than existing rank estimation methods
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