Viviana Acquaviva (Rutgers)
Understanding the Spectral Energy Distribution of Galaxies
A galaxyŐs Spectral Energy Distribution (SED) contains information about its physical properties, such as redshift, stellar population age, mass, star formation rate, dust content, and metallicity. These properties can be inferred via SED fitting, the procedure of comparing theoretical templates to observations to find the properties of the models that best resemble the data. This idea is simple and powerful; however, it is essential that it is implemented while avoiding biasing assumptions on the shape of the probability distribution function, and while maximizing the accuracy in the reported uncertainties. I will show why Markov Chain Monte Carlo (MCMC) algorithms are suitable tools for this task, and introduce GalMC, our publicly available MCMC code for SED fitting. I will present the results obtained using GalMC for Lyman Alpha Emitting galaxies at z ~ 3 and z ~ 2 from the MUSYC survey, and discuss how the assumptions made in modeling the stellar populations influence our estimates of galaxy properties. I will also describe GalFish, an astronomical observations planning tool based on the Fisher Matrix formalism, and SpeedyMC, a less flexible but much faster MCMC SED fitting algorithm suitable for large galaxy surveys. I will conclude by presenting some of the problems that still limit our understanding of the physical nature of galaxies, and discussing how I plan to help solving them.