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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
The VLT Science Archive System
M. A. Albrecht, E. Angeloni, A. Brighton, J. Girvan, F. Sogni, A. J.
Wicenec and H. Ziaeepour
European Southern Observatory, send e­mail to: malbrech@eso.org
Abstract. The ESO 1 Very Large Telescope (VLT) will deliver a Science
Archive of astronomical observations well exceeding the 100 Terabytes
mark already within its first five years of operations. ESO is undertaking
the design and development of both On­Line and O#­Line Archive Facil­
ities. This paper reviews the current planning and development state of
the VLT Science Archive project.
1. Introduction
The VLT Archive System goals can be summarized as follows: i) record the
history of VLT observations in the long term; ii) provide a research tool ­ make
the Science Archive another VLT instrument; iii) help VLT operations to be
predictable by providing traceability of instrument performance; iv) support
observation preparation and analysis.
1999 2000 2001 2002 2003 2004
UT1 isaac 4 4 4 4 4 4
fors1 0.5 0.5 0.5 0.5 0.5 0.5
conica 1.5 1.5 1.5 1.5 1.5
conica (speckle) 40 40 40 40 40
UT2 TestCam 0.5 0.5
uves 2.5 2.5 2.5 2.5 2.5 2.5
fuegos 2 2 2 2
fors2 0.5 0.5 0.5 0.5 0.5
UT3 TestCam 0.5 0.5
vimos 20 20 20 20 20
visir 1 1 1 1
UT4 TestCam 0 . 5 0 . 5
nirmos 48 48 48 48
Typical mix
(GB/night) 3.0 19.1 55.6 55.6 55.6 55.6
TB/Year 1.07 6.80 19.81 19.81 19.81 19.81
TB cumulative 1.07 7.87 27.68 47.49 67.30 87.11
The data volume expected from the di#erent instruments over the next
years is listed in Table 1. Figures are given in gigabytes for a typical night
during steady state operations. Estimated total rates per night are derived by
making assumptions on a mixture of instrument usage for a typical night.
1 http://www.eso.org/
363

364 Albrecht, et al.
OLAS
Intermediate
Storage
Database
On­line
Archive
On­line Archive Facility
User Data &
Archive Data
ASTO
Archive File
Database
Data Stage
Pipeline
VCS
SAS
Processing
Stage
Database
Science
Archive
Science Archive Facility
User Data
ASTO
Archive File
Database
Data Stage
Replication
SARE
Figure 1. Overview of the VLT Archive System Architecture.
In order to achieve the goals listed above, a system is being built that will
include innovative features both in the areas of technology and functionality.
Among its most distinct features, the system a) will be scalable through quasi on­
line data storage with DVD Jukeboxes and on­line storage with RAID arrays and
HFS; b) will include transparent replication across sites; c) will be data mining­
aware through meta­databases of extracted features and derived parameters.
2. System Architecture
The main components of the VLT Archive System are (see figure 1): the On­Line
Archive Facility (OLAF) and the o#­line Science Archive Facility (SAF). The
On­Line Archive System (OLAS) takes care of receiving the data and creates
the Observations Catalog while the Archive Storage system (ASTO) saves the
data products onto safe, long­term archive media. The SAF includes a copy of
ASTO used mainly for retrieval and user request handling, the Science Archive
System (SAS) and the Science Archive Research Environment (SARE). The SAS
stores the Observations Catalog in its Science Archive Database. All the data
is described in an observations catalog which typically describes the instrument
setup that was used for the exposure. Other information included in the catalog
summarize ambient conditions, engineering data and the operations log entries
made during the exposure. In addition to the raw science data, all calibration
files will be available from the calibration database. The calibration database
includes the best suitable data for calibrating an observation at any given time.
The Science Archive Research Environment (SARE) provides the infras­
tructure to support research programmes on archive data. Figure 2 shows an
overview of the SARE setup. Archive Research Programmes are either user de­
fined or ESO standard processing chains that are applied to the raw data. Each
of the processing steps is called a Reduction Block (RB). Typically the first
reduction block would be the re­calibration of data according to the standard
calibration pipeline. A reduction block consist of one or more processes which
are treated by the system as black boxes, i.e., without any knowledge of its im­

The VLT Science Archive System 365
SAS
raw data
on­the­fly
recalibration
RB RB RB
user block 1 user block n
. . .
Data
Mining
DB
derived parameters
Archive Query
Archive Research Programme
Figure 2. Overview of the VLT Science Archive Research Environment.
plementation. However, the reduction block interface (input and output data)
do comply to a well defined specification. This feature allows any reduction
module to become part of the chain. In fact, this flexible architecture also al­
lows the research programme to analyze di#erent kinds of data from images and
spectra to catalogs and tables of physical quantities. The output of an archive
research programme will be derived parameters that are fed into the data mining
database.
3. Data Mining in the Science Archive Research Environment
Observation data will be stored within the VLT Science Archive Facility and
will be available to Science Archive Research programmes one year after the
observation was made.
However, in face of the very large data amounts, the selection of data for a
particular archive research project becomes quickly an unmanageable task. This
is due to the fact that even though the observations catalog gives a precise de­
scription of the conditions under which the observation was made, it doesn't tell
anything about the scientific contents of the data. Hence, archive researchers
have to first do a pre­selection of the possibly interesting data sets on the basis
of the catalog, then assess each observation by possibly looking at it (preview)
and/or by running some automated task to determine its suitability. Such pro­
cedure is currently used for archive research with the HST Science Archive and
is acceptable when the data volume is limited (e.g., 270 GB of WFPC2 science
data within the last 3.5 years of HST operations).
Already after the first year of UT1 operations, the VLT will be deliver­
ing data quantities that make it not feasible to follow the same procedure for
archive research. New tools and data management facilities are required. The
ESO/CDS Data Mining Project aims at closing the gap and develop meth­
ods and techniques that will allow a thorough exploitation of the VLT Science
Archive.
One approach at tackling this problem is to extract parameters from the
raw data that can be easily correlated with other information. The main idea

366 Albrecht, et al.
Published Results
Data
object classes,
magnitudes, etc.
centroids, colors,
SIMBAD, Catalogs, etc.
Images, Spectra, etc.
Data Mining Database
categories, etc.
Figure 3. Overview of the data mining environment.
here is to create an environment that contains both extracted parametric infor­
mation from the data plus references to existing databases and catalogs. In its
own way, this environment then establishes a link between the raw data and the
published knowledge with the immediate result of having the possibility to de­
rive classification and other statistical samples. Figure 3 illustrates the general
concept.
An example of a semi­automatic parameter extraction is the object detec­
tion pipeline used by the ESO Imaging Survey (EIS) Project. Every image in
the survey is subject of a set of reduction steps that aim at extracting object pa­
rameters such as 2­D Gaussian fitted centroids, integrated magnitudes, etc. The
cross­correlation of parameters of this kind with selected databases and catalogs
(e.g., eccentric centroids with galaxy catalogs) would provide a powerful tool
for a number of science support activities from proposal preparation to archive
research.
4. Conclusions
The VLT Archive System being developed will provide the infrastructure needed
to o#er the Science Archive as an additional instrument of the VLT. The main
capabilities of the system will be a) handling of very large data volume, b) routine
computer aided feature extraction from raw data, c) data mining environment
on both data and extracted parameters and d) an Archive Research Programme
to support user defined projects.