Abstract
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.
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Notes
- 1.
Caption and figure taken from the Hipparcos site at http://www.rssd.esa.int/index.php?project=HIPPARCOS.
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Acknowledgements
I acknowledge Valentina Luridiana for developing the probabilistic theory of population synthesis over several years. I also acknowledge the third author of the [5] paper (only available in the astro-ph version of the paper) for a practical example of wild distribution in real life. I acknowledge Luisma Sarro Baró opportunity to attend this meeting, among many others things. This work was supported by the MICINN (Spain) through Grants AYA2007-647124 and AYA2010-15081.
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Cerviño, M. (2012). Probabilistic Description of Stellar Ensembles. In: Sarro, L., Eyer, L., O'Mullane, W., De Ridder, J. (eds) Astrostatistics and Data Mining. Springer Series in Astrostatistics, vol 2. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3323-1_8
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DOI: https://doi.org/10.1007/978-1-4614-3323-1_8
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