27,756 research outputs found
FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code
We present FitSKIRT, a method to efficiently fit radiative transfer models to
UV/optical images of dusty galaxies. These images have the advantage that they
have better spatial resolution compared to FIR/submm data. FitSKIRT uses the
GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo
radiative transfer code. Genetic algorithms prove to be a valuable tool in
handling the multi- dimensional search space as well as the noise induced by
the random nature of the Monte Carlo radiative transfer code. FitSKIRT is
tested on artificial images of a simulated edge-on spiral galaxy, where we
gradually increase the number of fitted parameters. We find that we can recover
all model parameters, even if all 11 model parameters are left unconstrained.
Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral
galaxy NGC4013. This galaxy has been modeled previously by other authors using
different combinations of radiative transfer codes and optimization methods.
Given the different models and techniques and the complexity and degeneracies
in the parameter space, we find reasonable agreement between the different
models. We conclude that the FitSKIRT method allows comparison between
different models and geometries in a quantitative manner and minimizes the need
of human intervention and biasing. The high level of automation makes it an
ideal tool to use on larger sets of observed data.Comment: 14 pages, 10 figures; accepted for publication in Astronomy and
Astrophysic
Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications
This paper discusses a tool for optimization of econometric models based on genetic algorithms. First, we briefly describe the concept of this optimization technique. Then, we explain the design of a specifically developed algorithm and apply it to a difficult econometric problem, the semiparametric estimation of a censored regression model. We carry out some Monte Carlo simulations and compare the genetic algorithm with another technique, the iterative linear programming algorithm, to run the censored least absolute deviation estimator. It turns out that both algorithms lead to similar results in this case, but that the proposed method is computationally more stable than its competitor. --Genetic Algorithm,Semiparametrics,Monte Carlo Simulation
The Time Machine: A Simulation Approach for Stochastic Trees
In the following paper we consider a simulation technique for stochastic
trees. One of the most important areas in computational genetics is the
calculation and subsequent maximization of the likelihood function associated
to such models. This typically consists of using importance sampling (IS) and
sequential Monte Carlo (SMC) techniques. The approach proceeds by simulating
the tree, backward in time from observed data, to a most recent common ancestor
(MRCA). However, in many cases, the computational time and variance of
estimators are often too high to make standard approaches useful. In this paper
we propose to stop the simulation, subsequently yielding biased estimates of
the likelihood surface. The bias is investigated from a theoretical point of
view. Results from simulation studies are also given to investigate the balance
between loss of accuracy, saving in computing time and variance reduction.Comment: 22 Pages, 5 Figure
PhysicsGP: A Genetic Programming Approach to Event Selection
We present a novel multivariate classification technique based on Genetic
Programming. The technique is distinct from Genetic Algorithms and offers
several advantages compared to Neural Networks and Support Vector Machines. The
technique optimizes a set of human-readable classifiers with respect to some
user-defined performance measure. We calculate the Vapnik-Chervonenkis
dimension of this class of learning machines and consider a practical example:
the search for the Standard Model Higgs Boson at the LHC. The resulting
classifier is very fast to evaluate, human-readable, and easily portable. The
software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.htmlComment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commu
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