January 6

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.