30,161 research outputs found
Nuclear multifragmentation within the framework of different statistical ensembles
The sensitivity of the Statistical Multifragmentation Model to the underlying
statistical assumptions is investigated. We concentrate on its micro-canonical,
canonical, and isobaric formulations. As far as average values are concerned,
our results reveal that all the ensembles make very similar predictions, as
long as the relevant macroscopic variables (such as temperature, excitation
energy and breakup volume) are the same in all statistical ensembles. It also
turns out that the multiplicity dependence of the breakup volume in the
micro-canonical version of the model mimics a system at (approximately)
constant pressure, at least in the plateau region of the caloric curve.
However, in contrast to average values, our results suggest that the
distributions of physical observables are quite sensitive to the statistical
assumptions. This finding may help deciding which hypothesis corresponds to the
best picture for the freeze-out stageComment: 20 pages, 7 figure
The first analytical expression to estimate photometric redshifts suggested by a machine
We report the first analytical expression purely constructed by a machine to
determine photometric redshifts () of galaxies. A simple and
reliable functional form is derived using galaxies from the Sloan
Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method
automatically dropped the and bands, relying only on , and
for the final solution. Applying this expression to other SDSS-DR10
galaxies, with measured spectroscopic redshifts (), we achieved a
mean and a scatter when averaged up to . The method was
also applied to the PHAT0 dataset, confirming the competitiveness of our
results when faced with other methods from the literature. This is the first
use of symbolic regression in cosmology, representing a leap forward in
astronomy-data-mining connection.Comment: 6 pages, 4 figures. Accepted for publication in MNRAS Letter
Impact of Power Allocation and Antenna Directivity in the Capacity of a Multiuser Cognitive Ad Hoc Network
This paper studies the benefits that power control and antenna directivity can bring to the capacity of a multiuser cognitive radio network. The main objective is to optimize the secondary network sum rate under the capacity constraint of the primary network. Exploiting location awareness, antenna directivity, and the power control capability, the cognitive radio ad hoc network can broaden its coverage and improve capacity. Computer simulations show that by employing the proposed method the system performance is significantly enhanced compared to conventional fixed power allocation
Using gamma regression for photometric redshifts of survey galaxies
Machine learning techniques offer a plethora of opportunities in tackling big
data within the astronomical community. We present the set of Generalized
Linear Models as a fast alternative for determining photometric redshifts of
galaxies, a set of tools not commonly applied within astronomy, despite being
widely used in other professions. With this technique, we achieve catastrophic
outlier rates of the order of ~1%, that can be achieved in a matter of seconds
on large datasets of size ~1,000,000. To make these techniques easily
accessible to the astronomical community, we developed a set of libraries and
tools that are publicly available.Comment: Refereed Proceeding of "The Universe of Digital Sky Surveys"
conference held at the INAF - Observatory of Capodimonte, Naples, on
25th-28th November 2014, to be published in the Astrophysics and Space
Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice,
6 pages, and 1 figur
Torsion-Adding and Asymptotic Winding Number for Periodic Window Sequences
In parameter space of nonlinear dynamical systems, windows of periodic states
are aligned following routes of period-adding configuring periodic window
sequences. In state space of driven nonlinear oscillators, we determine the
torsion associated with the periodic states and identify regions of uniform
torsion in the window sequences. Moreover, we find that the measured of torsion
differs by a constant between successive windows in periodic window sequences.
We call this phenomenon as torsion-adding. Finally, combining the torsion and
the period adding rules, we deduce a general rule to obtain the asymptotic
winding number in the accumulation limit of such periodic window sequences
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