App Stats: Mandel on "Hierarchical Bayesian Models for Supernova Light Curves" 

 We are really excited about this week&#8217;s Applied Statistics Workshop this Wednesday, April 4th, 2011 when we will be happy to have  Kaisey Mandel  from the  Harvard-Smithsonian Center for Astrophysics . Kaisey will be presenting on  hierarchical Bayesian models in Astrophysics . This will be a great chance to see how the statistical methods that we use transport to other disciplines around the sciences. No prior knowledge of astrophysics required! As always, we will serve a light lunch and the talk will begin around 12:15p. 

 &#8220;Hierarchical Bayesian Models for Type Ia Supernova Light Curves, Dust, and Cosmic Distances&#8221;  
 Kaisey Mandel   
Harvard-Smithsonian Center for Astrophysics  
CGIS K354 (1737 Cambridge St.)  
Wednesday, April 4th, 2011 12 noon   

 Abstract: 

 
   Type Ia supernovae (SN Ia) are the most precise cosmological distance indicators and are important for measuring the acceleration of the Universe and the properties of dark energy. To obtain the best distance estimates, the photometric time series (apparent light curves) of SN Ia at multiple wavelengths must be properly modeled. The observed data result from multiple random and uncertain effects, such as measurement error, host galaxy dust extinction and reddening, peculiar velocities, and distances. Furthermore, the intrinsic, absolute light curves of SN Ia differ between individual events: different SN Ia have different intrinsic luminosities, colors and light curve shapes, and these properties are correlated in the population. A hierarchical Bayesian model provides a natural statistical framework for coherently accounting for these multiple random effects while fitting individual SN Ia and the population distribution. I will discuss the application of this statistical model to optical and near-infrared data for computing inferences about the dust, distances and intrinsic covariance structure of SN Ia. Using this model, I demonstrate that the combination of optical and NIR data improves the precision of SN Ia distance predictions by about a factor of 2 compared to using optical data alone. Finally, I will discuss some open research problems concerning statistical analysis of supernova data and their application to cosmology.