Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.adass.org/adass/proceedings/adass94/malkovo1.ps Äàòà èçìåíåíèÿ: Tue Jun 13 20:50:05 1995 Äàòà èíäåêñèðîâàíèÿ: Tue Oct 2 01:53:43 2012 Êîäèðîâêà: Ïîèñêîâûå ñëîâà: m 97

Astronomical Data Analysis Software and Systems IV
ASP Conference Series, Vol. 77, 1995
R. A. Shaw, H. E. Payne, and J. J. E. Hayes, eds.
Classification of Objects in the Guide Star Catalog
O. Yu. Malkov, O. M. Smirnov
Institute of Astronomy of the Russian Academy of Sciences,
48 Pyatnitskaya Str., Moscow 109017 Russia
Abstract. A status report of the The GSC Object Classifier (GOC)
project is presented.
1. Introduction
As was reported at the ADASS III meeting (Malkov and Smirnov 1994a), soft­
ware for retrieving data from the GSC (GUIDARES) and mapping of object
regions has been developed. The GSC contains multiple entries for some objects
due to plate overlap. It classifies objects as stars, non­stars, galaxies, blends
and potential artifacts; the latter three classifications are rare, and only exist in
GSC version 1.1, to which they were added manually. It should also be noted
that many objects, especially near plate edges, are misclassified in the catalog.
The original GUIDARES had only two ways of dealing with multiple­entry
objects (MEOs); compute a weighted average of the data, or report all entries
for the object. We are working on a third way of looking at the GSC: analyzing
the nature of the object, using data from the GSC, to come up with a ``correct''
classification. We do not have the intent nor the means to revise the GSC; rather,
the object of the GOC project is to come up with extensions to the GUIDARES
software that will be able to read the GSC and produce correct classifications
within any region specified by the user. To implement GOC, we shall examine
a set of object parameters, among which are; the brightness of an object, its
coordinates (equatorial, galactic, and ecliptic), plate quality, distance from plate
center, and others. Multiplate analysis will be done as well. We plan to establish
probabilities for appearances of stars, galaxies, close binaries, ``rapid'' objects
(like minor planets), as well as blends, defects and artifacts, as functions of these
parameters. Artificial intelligence methods will be explored as well. The goal
is to produce an automatic classifier that can overcome the original catalog's
misclassifications by making use of all the other available information.
2. GSC classifications vs. GOC
GSC 1.1 contains the following classifications:
0 ---star,
1 ---galaxy,
2 ---blend or incorrectly resolved blend,
1

2
3 ---non­star,
5 ---potential artifact.
We are designing GOC to recognize the following objects: stars, galaxies, close
binaries, variable stars, objects in overcrowded stellar fields, Solar System ob­
jects, plate defects, and artifacts.
3. Implementation
The GOC will perform classification with the help of three separate modules.
The first is a rule­based expert system that either produces definite classifications
or fails. The second is a probability engine, which computes probabilities of
the object belonging to one of the above classes, and the third is an artificial
intelligence (AI) classifier that uses the test vote method to see if the object is
similar to a class of predefined training objects. A fourth module will act as an
arbitrator between the three classifiers to produce final results.
3.1. The basics: a Preliminary Analysis
To perform a preliminary analysis in the expert system, we will compare three
values: (1) the number of GSC entries for the object (E), (2) the number of
entries with the stellar (0) classification (S), and (3) the number of plates that
overlap this point on the sky (P ). Some obvious rules are, for example, P =
S = E ) the object is a star; E ? 1 ) the object exists on the sky (i.e., is
not a plate defect) and has a ``constant'' position and brightness (i.e., is not
a rapid object). We regard original GSC classifications of type 1, 2, and 5 as
unconditionally correct (since they were added manually).
3.2. Carrying on: Multi­plate Analysis
For a multi­plate analysis, the entries originating from higher­quality plates, or
those positioned closer to plate center (i.e., not distorted) are considered to be
more ``trustworthy'' than those on low­quality plates and/or near plate edges.
GOC will use this information when analyzing the different entries. Near plate
edges, the original object images become distorted and often lead to incorrect
classifications. The effective edge of plate is the threshold beyond which infor­
mation from the plate should no longer be considered valid (Malkov & Smirnov
1994b).
3.3. Estimation of Probabilities
Can it be a galaxy? The probability of the object being a galaxy (in­
dependent of how the GSC classifies it) is a function of brightness and galactic
latitude. GOC will combine this with the E­S­P analysis to produce final prob­
ability estimates.
Can it be a rapid (Solar System) object? The probability is a func­
tion of brightness, ecliptic latitude, date of plate exposure, and result of the
E­S­P analysis (see above).

3
3.4. Internal Databases of Special Objects
The GOC will include a small database of special objects that can severely
affect the original GSC classifications, for example, bright stars and clusters, or
artifacts, which are usually expected in the vicinity of bright stars. The database
can be used to quickly establish their locations (since searching the GSC itself
can take much longer), and overcrowded regions in the vicinities of star clusters,
which again can be established with the help of the database.
3.5. Artificial Intelligence Methods
A lot of artifacts are close to bright stars, and form line­ and arc­like patterns.
The distance from the nearest bright star, magnitude, and orientation relative
to neighboring objects can indicate the nature of the object. AI methods will
be used to analyze this information.
Close binaries are fully resolved on some plates, and have a single ``non­star''
or ``stellar'' classification on others. By using a catalogue of close binaries in
multiparametric GSC­analysis, we hope to find an implicit dependence between
separation, magnitude difference, plate quality, and some other parameters. The
test vote method will be used to find this dependence and apply it to GSC
objects.
4. Current Status and Future Plans
At the moment some principles of GOC are fully realized, others are taking
shape, and others are still under discussion. When ready, GOC will be built
into GUIDARES as an optional tool.
Acknowledgments. This presentation was made possible by financial sup­
port from ST ScI. Part of this work is funded by International Science Founda­
tion Grant number R3Z000.
References
Malkov, O. Yu., & Smirnov, O. M. 1994a, in Astronomical Data Analysis Soft­
ware and Systems III, ASP Conf. Ser., Vol. 61, eds. D. R. Crabtree, R. J.
Hanisch, & J. Barnes (San Francisco, ASP), p. 183malkov1
Malkov, O. Yu., & Smirnov, O. M. 1994b, in Astronomical Data Analysis Soft­
ware and Systems III, ASP Conf. Ser., Vol. 61, eds. D. R. Crabtree, R. J.
Hanisch, & J. Barnes (San Francisco, ASP), p. 187malkov2