2002
DOI: 10.1051/0004-6361:20020760
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Automatic observation rendering (AMORE)

Abstract: Abstract.A new method, AMORE -based on a genetic algorithm optimizer, is presented for the automated study of colourmagnitude diagrams. The method combines several stellar population synthesis tools developed in the last decade by or in collaboration with the Padova group. Our method is able to recover, within the uncertainties, the parameters -distance, extinction, age, metallicity, index of a power-law initial mass function and the index of an exponential star formation rate -from a reference synthetic stell… Show more

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Cited by 22 publications
(24 citation statements)
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“…Additionally, for the stars between the limits and outside the orange regions, a 3σ clipping in color-at fixed magnitude-is used to exclude outliers. We use this new technique instead of the one described in the above work or CMD template fitting (see e.g., Harris & Zaritsky 2001;Dolphin 2002;Ng et al 2002;Vergely et al 2002;Cignoni et al 2006;Aparicio & Hidalgo 2009) because for our problem, repeatedly simulating individual CMDs is less computationally intensive than computing the tables of likelihood functions required for the Gennaro et al (2015) approach or the CMD templates required for the template fitting approach. The mechanics of the fitting procedure can be broken into two largely independent parts: a function for quantitatively comparing CMDs, and an algorithm for using that comparison to explore a probability distribution.…”
Section: Fitting Techniquementioning
confidence: 99%
“…Additionally, for the stars between the limits and outside the orange regions, a 3σ clipping in color-at fixed magnitude-is used to exclude outliers. We use this new technique instead of the one described in the above work or CMD template fitting (see e.g., Harris & Zaritsky 2001;Dolphin 2002;Ng et al 2002;Vergely et al 2002;Cignoni et al 2006;Aparicio & Hidalgo 2009) because for our problem, repeatedly simulating individual CMDs is less computationally intensive than computing the tables of likelihood functions required for the Gennaro et al (2015) approach or the CMD templates required for the template fitting approach. The mechanics of the fitting procedure can be broken into two largely independent parts: a function for quantitatively comparing CMDs, and an algorithm for using that comparison to explore a probability distribution.…”
Section: Fitting Techniquementioning
confidence: 99%
“…The result of these assumptions was that the underlying density distributions within d ⌢ Ω were to a good approximation constant (if the survey was not too deep in magnitude and hence r hel not too deep). To solve the starcount equation under these approximations was a trivial exercise and in the past decades it has been indeed done by several works in this research area (e.g., Robin et al 2003;Méndez et al 2000;Vallenari et al 2006;Ng et al 2002;Girardi et al 2005, , and references therein). If the hypothesis of small d ⌢ Ω = dldb cos b is to be relaxed, the computing of this number has to be performed numerically as follows:…”
Section: Star-count Equation For Large Sky Coveragementioning
confidence: 99%
“…In the process of CMD fitting the genetic algorithm has a long history in the Padua group starting from the works of Ng et al (2002) and has been implemented in the kinematic fitting of observational data in Vallenari et al (2006). The algorithm has been run on true data to reproduce radial velocities (Gilmore, Wyse & Norris 2002), the GSC-II proper motion catalogue Vallenari et al (2006) and the RAVE dataset equipped with 2MASS proper motions in Pasetto et al (2012b), and Pasetto et al (2012c).…”
Section: Machine Learningmentioning
confidence: 99%
“…To explore this wide parameter space, we have combined a well-tested genetic algorithm (GA), Pikaia 18 , with a local search routine. As shown in various papers (see, e.g., Ng et al 2002;Aparicio & Hidalgo 2009;Small et al 2013), GAs allow us to find a global optimum more efficiently than a local search alone. In our approach, the synergy of the GA and a local search combines the advantages of both worlds.…”
Section: Stellar Massmentioning
confidence: 99%