Speaker
Dr
Somak Raychaudhury
(University of Birmingham)
Description
Multivariate datasets in astrophysics can be large, with the
increasing volume of information now becoming available from a range
of observations, from ground and Space, across the electromagnetic
spectrum. The observations are in the form of raw images and/or
spectra, and tables of derived quantities, obtained at multiple epochs
in time. Large archives of images, spectra and catalogues are now
being assembled into publicly-available databases: one example is the
emerging global effort towards the Virtual Observatory. This
necessitates the development of techniques that will allow fast,
automated classification and extraction of key physical properties for
very large datasets, and the ability to visualise the structure of
highly multi-dimensional data, for extracting and studying
substructures in a flexible way. Automated algorithms for clustering and outlier
detection are necessary for a wide range of Astrophysical
problems involving these growing datasets.
The applicability of commercial data mining tools is
limited, since these do not incorporate the handling of errors in a
principled manner, which is central to the analysis of Astronomical
data, as it is in other branches of Physics. I will review how
techniques used in the field of machine learning are being adapted for
use in classification and clustering problems. Examples will include
the use of topographic mapping to classify light curves of eclipsing
binary stars, showing that this is an efficient way of searching for
transiting extrasolar planets in large datasets, and robust density
modelling for determining clusters and outliers, resulting in finding
high-redshift quasars.
Primary author
Dr
Somak Raychaudhury
(University of Birmingham)
