Bibcode
Cerviño, M.l
Referencia bibliográfica
Astrostatistics and Data Mining, Springer Series in Astrostatistics, Volume 2. ISBN 978-1-4614-3322-4. Springer Science+Business Media New York, 2012, p. 89
Advertised on:
2012
Número de citas
0
Número de citas referidas
0
Descripción
I describe the modeling of stellar ensembles in terms of probability
distributions. This modeling is primary characterized by the number of
stars included in the considered resolution element, whatever its
physical (stellar cluster) or artificial (pixel/IFU) nature. It provides
a solution of the direct problem of characterizing probabilistically the
observables of stellar ensembles as a function of their physical
properties. In addition, this characterization implies that intensive
properties (like color indices) are intrinsically biased observables,
although the bias decreases when the number of stars in the resolution
element increases. In the case of a low number of stars in the
resolution element (N<105), the distributions of intensive
and extensive observables follow nontrivial probability distributions.
Such a situation can be computed by means of Monte
Carlo simulations where data mining techniques would be applied.
Regarding the inverse problem of obtaining physical parameters from
observational data, I show how some of the scatter in the data provides
valuable physical information since it is related to the system size
(and the number of stars in the resolution element). However, making use
of such information requires following iterative
procedures in the data analysis.