Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.adass.org/adass/proceedings/adass94/peronm.ps Äàòà èçìåíåíèÿ: Tue Jun 13 20:52:43 1995 Äàòà èíäåêñèðîâàíèÿ: Tue Oct 2 00:51:06 2012 Êîäèðîâêà: Ïîèñêîâûå ñëîâà: ï ï ï ï ï ï ï ï ï ï ï ï ï

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.
Organizing Observational Data at the Telescope
M. Peron, D. Baade, M. A. Albrecht, and P. GrosbÜl
European Southern Observatory, Karl­Schwarzschild­Straúe 2, D­85748
Garching, Germany
Abstract. The MIDAS Data Organizer, a customizable utility to ana­
lyze and identify associations in a database of astronomical observations,
is described. Its implementation in a data acquisition environment is
discussed as a particular application.
1. The Problem
There is a number of situations where users of astronomical observations would
benefit from advanced software tools which can analyze the composition of the
available data pool:
Data Acquisition Control: Spectroscopists may wish to take after each sci­
entific spectrum a new arc spectrum if, for instance, the spectrograph tilt
angle has changed by more than a certain amount since the previous arc
exposure. (Presumably, a new arc spectrum would not be required fol­
lowing acquisition images.) Given the numerous other on­line activities,
a system knowledgeable enough to issue an automatic reminder to the
observer, when and if necessary, would be very advantageous.
On­line Data Reduction: The main difficulty of automatic on­line reduction
procedures is to identify the optimal calibration files that are applicable
to a given science exposure. These data may be either acquired during
the observing run or supplied by an observatory­maintained database. If
calibration data are temporarily unavailable, the on­line reduction tasks
may also need to be suspended and resumed when all the necessary data
become available.
Off­line Data Reduction: This situation is very similar to the on­line case,
except that the volume of data is larger and the user may no longer re­
member (or not know) the structure of the database. A system that can
group science exposures and calibration data according to user­definable
criteria would be a considerable asset, especially if it can also be interfaced
to subsequent standard reduction procedures.
Archival Research: The lack of a suitable overview of the structure and con­
tents of the database is the standard problem of archival researchers who
need to specify the calibration files they want to extract. Observatory staff
are facing a similar situation when they wish to perform trend analyses
for instruments.
1

2
2. Derived Requirements
The above scenarios have in common that the user wants to define a context­
specific structure on the observational database. From this, the following re­
quirements on the desired tool can be extracted: It must be able to classify each
file into user­definable categories, and it must be able to establish relations be­
tween files of different categories (e.g., associate to a given science frame a set of
suitable calibration files). The Data Organizer (DO) (Peron et al. 1993) allows
users to perform these tasks in an easily customizable way. The main charac­
teristics of the on­line implementation of the DO at the ESO New Technology
Telescope (NTT) are described below.
2.1. Observation Summary Table
The database for all operations is a MIDAS (ESO­IPG 1993) table, the so­called
Observation Summary Table (OST) which contains all relevant parameters ex­
tracted from the FITS keywords. Because the Data Organizer is built on existing
capabilities of the MIDAS Table File System (Peron et al. 1992), standard rela­
tional database operations (e.g., select, merge, copy, and project) may be used.
2.2. Classification of Exposures
The information contained in the OST is used to classify the images according
to different sets of attributes (e.g., optical elements, calibration sources, etc.).
The classification is achieved by applying user­definable rules, and the result of
the classification (e.g., an optical path) is saved in the OST. EMMI (Melnick
et al. 1992), one of the instruments mounted at the NTT, allows a wide range
of observing modes, from wide­field imaging to high­dispersion spectroscopy,
including long­slit and multi­object spectroscopy. Because of its complexity,
EMMI has been chosen for the first implementation of the DO in an on­line
environment.
2.3. Association of Exposures
The association of scientific frames with suitable calibration images is achieved
by selecting and ranking all calibration frames which for a given scientific expo­
sure match a set of user defined selection criteria. One may expand the search
by submitting a ``second choice'' set of criteria when not enough frames match
the original one. For instance, a search may be expressed in natural language
as: ``Find for each scientific frame two dark exposures that have been observed
within the same night, and for which the mean detector temperature did not
vary by more than 1 degree. If unsuccessful, look for dark exposures observed
within the same observing run, and for which the mean detector temperature did
not vary by more than 2 degrees.'' Selected frames can be ranked by applying
weights to the attributes invoked in the same process. For example, one may
give more importance to the time difference than to the detector temperature
difference.

3
3. On­line Implementation of the DO at the NTT
The DO has been conceived as a general purpose tool, whereas the implemen­
tation at the NTT provides very specific services. In particular, it knows about
FITS keywords that characterize EMMI files. This customization was achieved
by providing a set of NTT specific configuration tables which contain definitions
of different exposure types (e.g., SCI, FFDOME, FFSKY, and BIAS) and in­
strument modes (e.g., blue imaging, red medium­dispersion spectroscopy). Each
time a new file is delivered by the acquisition system, the exposure is classified
according to a predefined set of rules, and the result is appended to the OST.
Because it is essential that observers can interact efficiently with the tools of­
fered to them in an on­line environment, a versatile Graphical User Interface
has been fitted to the DO. It is shown and further explained in Figure 1.
4. Further Applications
After the adaptation of the association part of the DO to the particular re­
quirements of EMMI, the basis for automatic on­line data reduction will be
available. The first MIDAS package to be interfaced to it will be the CCD pack­
age. Observers and archival researchers will receive their data together with the
corresponding OST. At the NTT, an observatory­supplied calibration database
will be maintained. A dedicated OST will provide the overview of that database
and enable observers to identify additional calibration observations which could
be applicable to their own data. It is expected that at the Very Large Telescope
(VLT) the Data Organizer will play a more central role in the on­line data flow.
References
ESO­IPG 1993, in MIDAS Users Guide, ESO Operating Manual, No. 1 (Garch­
ing, ESO)
Peron, M., Ochsenbein, F., & GrosbÜl, P. 1992, in Astronomy from Large Data­
bases II, ESO Conference and Workshop Proceedings, No. 43, eds. A.
Heck & F. Murtagh (Garching, ESO), p. 433
Peron, M., Albrecht, M. A., & GrosbÜl, P. 1994, in Handling and Archiving
Data from Ground­Based Telescopes, ESO Conference and Workshop
Proceedings, No. 50, eds. M. A. Albrecht & F. Pasian (Garching, ESO),
p. 57
Melnick, J., Dekker, H., D'Odorico, S., & Giraud, E. 1994, in EMMI & SUSI,
ESO Operating Manual, No. 15, Version 2.0

4
Figure 1. The Graphical User Interface to the Data Organizer as in­
stalled at the NTT. The main window in the upper left corner shows
(part of) the OST which is the database on which all operations sup­
ported by the DO are performed. A table­like widget (''Classification
rules'') can be activated by a pushbutton to edit and modify classifi­
cation rules. The rule ``BIAS'' for classifying BIAS exposures is being
edited in the figure. Another table­like widget (``Classify'') can be used
for entering classification rules to be applied as well as the character
string which in the OST will identify the selected frames.