This thesis develops a method for obtaining the star formation histories of a
mixed, resolved population through the use of color-magnitude diagrams (CMDs).
The method is applied to the derivation of the local star formation rate,
modeling observations of the Hipparcos satellite wigth synthetic CMDs computed
for different star formation histories with an updated stellar evolution
library. Parallax and photometric uncertainties are included explicitly and
corrected using the Bayesian Richardson-Lucy algorithm. We find that the solar
neighborhood star formation rate has a characteristic timescale for variation
of about 6 Gyr, with a maximum activity close to 3 Gyr ago. This suggests a
global, rather than local, star forming event. The summary and conclusions are
included here, the full thesis is available at the URL listed above.Comment: PhD thesis/Physics 2006, University of Pisa, 167 pages, complete pdf
  version is available at:
  http://etd.adm.unipi.it/theses/available/etd-04032006-122227

Cignoni, Michele

English

arXiv

The project, described in this thesis, explores new methods to extract information through the use of color-magnitude diagrams (CMDs). In particular, the purpose of this thesis is to provide insight into the star formation rate (SFR)in the solar neighborhood, analyzing the observations of the Hipparcos satellite.
An original technique of comparison has been devised: 1) we employ the Richardson-Lucy algorithm to the analysis of the observational errors in the CMDs by converting the CMD into an image (in effect, a CMD is an image, the intensity being the number of stars in a bin of effective temperature and luminosity, affected by a point spread function that originates from the error distributions derived from the known sources of error); 2) A synthetic population is built via Monte Carlo extractions of masses and ages, according the assumed IMF and star formation rate (SFR). Then, a suitable age-metallicity relation (AMR) gives the metallicity. The extracted synthetic stars are placed in the CMD by interpolations on the adopted stellar evolution tracks. To evaluate the goodness of the assumed model, we transform the theoretical and observational CMDs in two dimensional histograms, choosing bin sizes in color and in absolute magnitude. Once the number of theoretical and observational objects is known in each bin, we quantify the differences with a likelihood function (chi square). The confidence limit of the best model is evaluated through a bootstrap technique.
In order to check the sensitivity of the recovered SFR to the different parametrical inputs (IMF, binaries, AMR), the method is tested on artificial data. Finally, the analysis is repeated on the real Hipparcos data, previously “cleaned” by the Richardson-Lucy algorithm

Star formation rate in the solar neighborhood

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