• SOLAR NEIGHBORHOOD
• RICHARDSON-LUCY ALGORITHM
• MAXIMUM LIKELIHOOD
• IMF
• IMAGE RESTORATION
• HIPPARCOS
• GALAXY
• COLOR MAGNITUDE DIAGRAM
• BAYESIAN ANALYSIS
• AGE METALLICITY RELATION
• STAR COUNTS
• STAR FORMATION RATE
• STELLAR EVOLUTION

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.