Data Processing for ’SUBARU’ Telescopeusing GRID
Y. Shirasaki M. Tanaka S. Kawanomoto S. Honda M. Ohishi
Y. Mizumoto
National Astronomical Observatory of Japan, 2-21-1, Osawa, Mitaka, Tokyo
181-8588, Japan

N. Yasuda
University of Tokyo, 5-1-5 Kashiwa-no-Ha, Kashiwa Chiba, 277-8582 Japan

Y. Masunaga
Ochanomizu Univerisity, 2-1-1 Otsuka Bunkyo-ku, Tokyo, 112-8610 Japan

Y. Ishihara J. Tsutsumi
Fujitsu Ltd., 4-1-1 Kamikodanaka Nakahara-ku, Kawasaki, 211-8588 Japan

H. Nakamoto Y. Kobayashi M. Sakamoto
Systems Engineering Consultants Co. Ltd., 22-4 Sakuraoka-cho Shibuya-ku,Tokyo, 150-0031 JapanAbstract
The amount of astronomical data is rapidly growing as the progress of telescope
technology and the detection technique of recent years. As a result, the way of traditional analysis is becoming insuﬃcient for utilizing the large amount of data eﬀectively and eﬃciently. The SuprimeCam, which is one of the instruments equippedwith the Subaru telescope, has been generating 6 TB of public data since its startof operation. It is almost impossible to transfer all the data to the local machine
to analyze them. It is, therefore, desirable to have an environment where the data
is analyzed where it is stored. In addition, it is not easy for an researcher who is
not familiar with the Subaru data to reduce the Subaru’s raw data. To overcome
these diﬃculties, we have applied grid technology to construct a system that carryout multiple jobs on multiple servers. Integrating this system to the Japanese Virtual Observatory, a user can easily utilize the grid system through the web browser
interface.

Preprint submitted to Elsevier

30 May 2007

Key words:GRID, Astronomy, Virtual Observatory, Web

1

Introduction

Thanks to the progress of telescope technology and the detection technique ofrecent years, it is expected that we will meet with a situation where a large
scale of high-quality data is continuously generated. The way of traditional
analysis, however, is becoming insuﬃcient for using the large amount of data
eﬀectively and eﬃciently, and for getting the maximum scientiﬁc results. Although many astronomers recognize the importance of research that uses themulti-wavelength data, such research actually needs considerable eﬀort. One
reason is that, for each data set, one needs to learn how to reduce and analyze the data, and even needs to know where the analysis tools are available(see top panel of Figure 1). To overcome such situation and maximize thescientiﬁc return from a big project like Subaru and ALMA, it is important
to construct an environment where user can access to the science-ready datawith very few eﬀort (see bottom panel of Figure 1). National AstronomicalObservatory of Japan (NAOJ) started its VO project (Japanese Virtual Observatory – JVO 1 ) in 2002. The objectives of the JVO project are to provide
a seamless access to the distributed data service of the world, and to provide
user-friendly analysis environment. To realize the interoperability among theastronomical databases of the world, it is crucial to deﬁne a world standardof the access protocol for the databases. The International Astrnomical Ob-Fig. 1. Current situation of astronomical study (top) vs astronomy of the future(bottom).
1

http://jvo.nao.ac.jp/2

servatory Aliance (IVOA 2 ) was formed in 2002 for that purpose. The IVOA
now comprises 16 VO projects from Armenia, Australia, Canada, China, Europe, France, Germany, Hungary, India, Italy, Japan, Korea, Russia, Spain,the United Kingdom, and the United States. This paper describes our recent
progress on the data analysis environment on JVO.

2Subaru Data Archive

Subaru 3 is an optical-infrared 8.2 m telescope operated by National Astronomical Observatory of Japan (NAOJ) at Mt. Mauna Kea, Hawai. Subaru
has seven open use instruments: CIAO, COMICS, FOCUS, IRCS, SuprimeCam, HDS and MOIRCS. Using these instruments, observation can be madefor wavelengths from optical (300 nm) to infrared (20 µm) with spectrum
resolution up to 105 (HDS).
As of October 2006, 8 TB of data is archived in the public area. More than
70% of the data are from the SuprimeCam, which is a mosaic of ten 2048
× 4096 CCDs and covers a 34’ x 27’ ﬁeld of view with a pixel scale of 0.20”(Figure 3). More than 90% of all the data requests are for the SuprimeCam, so
our current priority issue is how to improve the usability of the SuprimeCam
data. There is a plan to upgrade the SuprimeCam to the Hyper SuprimeCam(HSC) in 2011. The HSC will have ten times larger detection area than theSuprimeCam, and is expected to generate data in ten times hihger rate.
The data are registered in the Subaru Telescope Archive System (STARTS)
in Hawai as soon as the data are acquired by the instruments, so an observer
retrieves his data from STARS during and/or after the observation. The data

Fig. 2. Cumulative totla data size of theSubaru SuprimeCam.
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http://www.ivoa.net/
http://subarutelescope.org/

3Fig. 3. Subaru SuprimeCam CCDs
and an image taken by the SuprimeCam.

Fig. 4.

of STARS is mirrored to the Mitaka Advanced STARS (MASTARS) in Japan,
so the observer can retrieve the data also from the MASTARS when he returns to Japan. STARTS and MASTARS are not public data archive. To use
the system, you need to get an account on the Subaru computing system for
STARS or an account on the Mitaka computing system for MASTARS. The
data that passed 18 months of a proprietary period becomes publicly available through the SMOKA 4 and JVO. system. The SMOKA system provides
various query modes for the Subaru archive. The JVO system provides IVOA
standard access interface to the Subaru archive.

3

Data Reduction of SuprimeCam

The amount of data, especially of SuprimeCam, is very large, so it is important to provide a way to analyze the data without moving the data to ausers’ machine. One of the ways to do so is to login to the Subaru or Mitaka
computing system and analyze the data on the machine. It is, however, notpractical to use the visualization tool from a remote machine, especially whenaccessing through a slow network, and is also not easy for a novice user toanalyze the Subaru data with command line user interface. Another solution
is to provide a web service, through which one can access to the data analysis
software and visualize the data in a compact graphical format such as GIF,
JPEG and PNG. Recently a lot of open source framework are available formaking such a service, and it has been realized that interactivity of the web
based service can be improved by using Ajax technique as demonstrated by
the google map service.
To reduce the data of Subaru SuprimeCam, a lot of computing resources arerequired. To make one ﬁnal mosaic image from raw data, ﬂat-ﬁeld frames must
be prepared for each CCD (Figure 4). The ﬂat-ﬁeld frame is derived fromhundreds of observation frames by calculating their median values for each4

http://smoka.nao.ac.jp/

4

Fig. 5. Subaru data reduction pipeline architecture

2k×4k pixels. It typically takes two hours for creating one ﬂat-ﬁeld frame,
and at lease ten ﬂat-ﬁeld fames are required to make the ﬁnal mosaic image.
Thus it takes 20 hours for prepareing the ﬂat-ﬁeld frames, if the calculation isperfromed on just one CPU. After all the raw data are corrected by using theﬂat-ﬁeld frames, we need to coadd the frames to remove the gaps between theCCDs, to remove cosmic rays event, and to enhance the signal to noise ratio.
It takes one hour for typical observations.
Another important point is to provide the data in a form that detector andenvironment dependencies are removed. Those dependencies are usually not
known by an archive user, so it should be properly reduced by a data provider.
The quality of the data reduction is improved as the experience is accumulated
and the reduction software is also evolves continuously, so it is adequate toreduce the data on demand with the most developed algorithm.

4

Grid Computing System

It is expected that several tens user will submit data reduction jobs, and thenumber of users may increase unexectedly. So the computing resources need
to be integrated in a scalable manner. We have developed a Web service based
grid computing system. This system is composed of four services: Monitoringand Discovering Service (MDS), Data Analysis Service (DAS), Data SearchService (DSS) and Storage Service (SRS). Figure 5 shows a diagram of theSubaru SuprimeCam analysis system. The MDS manages the status of eachDAS server. Each DAS server periodically reports its status, such as loadaverage, number of running job and job status, and the status is stored on
an MDS database. A GRID client queries to the MDS to ask which server isfree for a job submission. The MDS returns a service endpoint URL that is
appropriate for the job submission. The client submit the job, and wait it toﬁnish. While waiting, the client periodically polling to the MDS for querying
5the job status. The DAS sends a query to the DSS to get Subaru RAW data,
the DSS returns the URLs for the data, then the DAS retrieves the data andstarts analysis. When a job status is changed to “ﬁnish”, the client queries
the DAS for an URL to retrieve the result. The URL is passed to the SRS,
the result is stored on the storage of the SRS, and the metadata of the result
is registered on the DSS.
We have integrated the GRID computing system to the JVO portal 5 so thatuser can easily access to the computing resources through a well-familiar interface, Web browser. The details of the JVO system were described in elsewhere(Shirasaki et al. 2006, Shirasaki et al. 2006b, Tanaka et al. 2006, Ohishi et al.
2006).

5Summary

The construction of the GRID-based Subaru analysis service has just started
last year (2006). Currently the data of SuprimeCam is a primary target ofthe development, but data of other instruments will be available. The gridcomputing system are constructed to obtain enough computing resource toanalyze all the SuprimeCam data in reasonably short time. Using the system,
we have demonstrated that all the SuprimeCam data can be reduced in lessthan one week. If the same thing is done without this sytem, it will take morethan one year to accomplish the work. The JVO portal service is available
for everyone at http://jvo.nao.ac.jp/portal, although currently only thelimited functionality is publicly available. To use all the JVO functionality,
user registration will be required.

References
Tanaka, M. et al. , 2006, in ASP Conf. Ser., Vol. 351, Astronomical Data
Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, &S. Enrique (San Francisco: ASP)
Shirasaki, Y. et al., 2006, Advanced Software and Control for Astronomy.
ed. Lewis, Hilton; Bridger, Alan. Proceedings of the SPIE, 6274, p42
Shirasaki, Y. et al. , 2006, in ASP Conf. Ser., Vol. 351, Astronomical Data
Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, &S. Enrique (San Francisco: ASP)
Ohishi, M. et al. , 2006, in ASP Conf. Ser., Vol. 351, Astronomical Data
Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, &S. Enrique (San Francisco: ASP)
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http://jvo.nao.ac.jp/portal

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