Recovering physical properties from narrow-band photometry

Our aim in this work is to answer, using simulated narrow-band photometry data, the following general question: What can we learn about galaxies from these new generation cosmological surveys? For instance, can we estimate stellar age and metallicity distributions? Can we separate star-forming galaxies from AGN? Can we measure emission lines, nebular abundances and extinction? With what precision? To accomplish this, we selected a sample of about 300k galaxies with good S/N from the SDSS and divided them in two groups: 200k objects and a template library of 100k. We corrected the spectra to $z = 0$ and converted them to filter fluxes. Using a statistical approach, we calculated a Probability Distribution Function (PDF) for each property of each object and the library. Since we have the properties of all the data from the {\sc starlight}-SDSS database, we could compare them with the results obtained from summaries of the PDF (mean, median, etc). Our results shows that we retrieve the weighted average of the log of the galaxy age with a good error margin ($\sigma \approx 0.1 - 0.2$ dex), and similarly for the physical properties such as mass-to-light ratio, mean stellar metallicity, etc. Furthermore, our main result is that we can derive emission line intensities and ratios with similar precision. This makes this method unique in comparison to the other methods on the market to analyze photometry data and shows that, from the point of view of galaxy studies, future photometric surveys will be much more useful than anticipated.