329 research outputs found
Automated supervised classification of variable stars I. Methodology
The fast classification of new variable stars is an important step in making
them available for further research. Selection of science targets from large
databases is much more efficient if they have been classified first. Defining
the classes in terms of physical parameters is also important to get an
unbiased statistical view on the variability mechanisms and the borders of
instability strips. Our goal is twofold: provide an overview of the stellar
variability classes that are presently known, in terms of some relevant stellar
parameters; use the class descriptions obtained as the basis for an automated
`supervised classification' of large databases. Such automated classification
will compare and assign new objects to a set of pre-defined variability
training classes. For every variability class, a literature search was
performed to find as many well-known member stars as possible, or a
considerable subset if too many were present. Next, we searched on-line and
private databases for their light curves in the visible band and performed
period analysis and harmonic fitting. The derived light curve parameters are
used to describe the classes and define the training classifiers. We compared
the performance of different classifiers in terms of percentage of correct
identification, of confusion among classes and of computation time. We describe
how well the classes can be separated using the proposed set of parameters and
how future improvements can be made, based on new large databases such as the
light curves to be assembled by the CoRoT and Kepler space missions.Comment: This paper has been accepted for publication in Astronomy and
Astrophysics (reference AA/2007/7638) Number of pages: 27 Number of figures:
1
Galaxy Zoo: Morphological Classification and Citizen Science
We provide a brief overview of the Galaxy Zoo and Zooniverse projects,
including a short discussion of the history of, and motivation for, these
projects as well as reviewing the science these innovative internet-based
citizen science projects have produced so far. We briefly describe the method
of applying en-masse human pattern recognition capabilities to complex data in
data-intensive research. We also provide a discussion of the lessons learned
from developing and running these community--based projects including thoughts
on future applications of this methodology. This review is intended to give the
reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning
and Data Mining for Astronom
The Photometric Classification Server for Pan-STARRS1
The Pan-STARRS1 survey is obtaining multi-epoch imaging in 5 bands (gps rps
ips zps yps) over the entire sky North of declination -30deg. We describe here
the implementation of the Photometric Classification Server (PCS) for
Pan-STARRS1. PCS will allow the automatic classification of objects into
star/galaxy/quasar classes based on colors, the measurement of photometric
redshifts for extragalactic objects, and constrain stellar parameters for
stellar objects, working at the catalog level. We present tests of the system
based on high signal-to-noise photometry derived from the Medium Deep Fields of
Pan-STARRS1, using available spectroscopic surveys as training and/or
verification sets. We show that the Pan-STARRS1 photometry delivers
classifications and photometric redshifts as good as the Sloan Digital Sky
Survey (SDSS) photometry to the same magnitude limits. In particular, our
preliminary results, based on this relatively limited dataset down to the SDSS
spectroscopic limits and therefore potentially improvable, show that stars are
correctly classified as such in 85% of cases, galaxies in 97% and QSOs in 84%.
False positives are less than 1% for galaxies, ~19% for stars and ~28% QSOs.
Moreover, photometric redshifts for 1000 luminous red galaxies up to redshift
0.5 are determined to 2.4% precision with just 0.4% catastrophic outliers and
small (-0.5%) residual bias. PCS will create a value added catalog with
classifications and photometric redshifts for eventually many millions sources.Comment: Replaced with version accepted for publication in Ap
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
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