[gov3009-l] Mandel on "Hierarchical Bayesian Models for Supernova Light Curves"

Matt Blackwell mblackwell at iq.harvard.edu
Mon Apr 4 16:25:45 EDT 2011


Hi all,

We are really excited about this week'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! You will find an abstract and a
link to a paper below. As always, we will serve a light lunch and the
talk will begin around 12:15p.

“Hierarchical Bayesian Models for Type Ia Supernova Light Curves,
Dust, and Cosmic Distances”
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.

Paper: http://arxiv.org/abs/1011.5910

Cheers,
matt.


~~~~~~~~~~~
Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/


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