Hyperatlas is an open standard intended to facilitate the large-scale
federation of image-based data. The subject of hyperatlas is the space of
sphere-to-plane projection mappings (the FITS-WCS information), and the
standard consists of coherent collections of these on which data can be
resampled and thereby federated with other image data. We hope for a
distributed effort that will produce a multi-faceted image atlas of the sky,
made by federating many different surveys at different wavelengths and
different times. We expect that hyperatlas-compliant imagery will be published
and discovered through an International Virtual Observatory Alliance (IVOA)
registry, and that grid-based services will emerge for the required resampling
and mosaicking.Comment: Published in ADASS XIII proceeding

Djorgovski, S. G.

Feldmann, M. T.

Jacob, J. C.

Williams, R. D.

English

arXiv

Hyperatlas is an open standard intended to facilitate the large-scale federation of image-based data. The subject of hyperatlas is the space of sphere-to-plane projection mappings (the FITS-WCS information), and the standard consists of coherent collections of these on which data can be resampled and thereby federated with other image data. We hope for a distributed effort that will produce a multi-faceted image atlas of the sky, made by federating many different surveys at different wavelengths and different times. We expect that hyperatlas-compliant imagery will be published and discovered through an International Virtual Observatory Alliance (IVOA) registry, and that grid-based services will emerge for the required resampling and mosaicking

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Hyperatlas: A New Framework for Image Federation
R. D. Williams, S. G. Djorgovski, M. T. Feldmann, and J. C. Jacob
California Institute of Technology, Pasadena, California
Abstract. Hyperatlas is an open standard intended to facilitate the
large-scale federation of image-based data. The subject of hyperatlas is
the space of sphere-to-plane projection mappings (the FITS-WCS infor-
mation), and the standard consists of coherent collections of these on
which data can be resampled and thereby federated with other image
data. We hope for a distributed effort that will produce a multi-faceted
image atlas of the sky, made by federating many different surveys at differ-
ent wavelengths and different times. We expect that hyperatlas-compliant
imagery will be published and discovered through an International Vir-
tual Observatory Alliance (IVOA) registry, and that grid-based services
will emerge for the required resampling and mosaicking.
1. Introduction
In this paper we discuss a new and largely unexplored data type in astron-
omy: large-scale federated imagery. By this, we mean a set of sky images that
have been resampled (reprojected) to the same pixel space. The set of images
might represent the sky at different wavelengths, times, resolution, polarization,
etc. Because they are on the same pixel plane, these resampled images can
be stacked and mined together – they can be federated. The benefits of such
federation can outshine a possible loss of data quality from resampling. We
will describe scientific possibilities of image federation, define the meaning of an
atlas, then describe a proposed set of standard atlases (Hyperatlas) that will
enable interoperability of images that have been resampled by different groups.
When a sky survey is made, the telescope images different parts of the sky
at different times, wavebands, etc, creating a mosaic of images. Traditionally,
the images are converted to star/galaxy catalogs through source-extraction, and
much of the scientific knowledge is mined from these catalogs. Catalogs are
federated by cross-matching, and further knowledge mined from the joint cat-
alog. However, the intrinsic limitation of any source extraction process is that
object detection relies on some filter, which is usually optimized for a particular
source size and/or contrast, and may not be efficient in detecting useful signals
which fall outside its parameter range; an example are low surface brightness ob-
jects. Thus, images may contain valuable information not present in the source
catalogs.
In this paper, we consider a new paradigm for mining knowledge from the
images of the sky surveys: by federating the images directly to a uniform pixel
space, so that multiple image sets can be stacked, then doing source detection,
1
2 Williams, Djorgovski, Feldmann, & Jacob
pattern matching and other data mining in the resulting multi-channel image.
If the pixel spaces (pages) are standardized, then it is much easier to federate
data made by different groups in different ways.
2. Federation
Even with an ”optimal” source extraction, in some cases even the absence of
a statistically significant signal represents a useful information, but only in the
context of other imagery; an example are high-redshift objects detected with the
”dropout” techniques. Image federation is thus useful in the detection of faint
sources [Szalay 1998], which may not be detected in a particular (or any given)
channel, but may be detected in others; or, a collective of individually marginal
detections may have a high significance when considered jointly. We can go
fainter in image space because we have more photons from the combined images
and because the multiple detections can be used to enhance the reliability of
sources at a given threshold.
Astronomers are more and more reaching into the time domain (synoptic),
imaging the sky repeatedly for discovery of transient and variable sources. Near-
Earth asteroids and Kuiper-belt objects, cataclysmic variables, active galactic
nuclei, and quasars are all interesting, as well as lensing events, orphan after-
glows, and most exciting would be a rich set of new types of transients. Image
federation shows promise for analysis of this kind of data, by forming an average
sky, a maximal brightness sky, a sky specialized to non-Gaussian noise sources,
and a sky where information from dozens of channels is represented in a single
color image. Very faint periodic sources could be found by time autocorrelation
of pixel brightness.
A prerequisite for finding differences between sky images is the perfect mu-
tual positioning of sources. We will focus on reconciling images with very dif-
ferent inherent spatial resolution by micro-optimization of astrometry through
a fitting process. More generally, we can subtract out “well-fitted” objects (e.g.,
PSF-like), to leave only the faint, complex structure that may contain new
knowledge.
Image federation enables robust detection and flux measurement of complex,
extended sources over a range of size, wavelength, and time scales. Larger ob-
jects in the sky may have both extended structure (requiring image mosaicking)
and a much smaller active center, or diffuse structure entirely. We will be able to
combine multiple instrument imagery to build a multi-scale, multi-wavelength
picture of such extended objects. We will also make statistical studies of ex-
tended, complex sources that vary in shape with wavelength.
3. Pages and Atlases
Federated imagery is a collection of mosaicked image products, all on the same
pixel plane – we call these channels. More precisely:
• We define a Page to be a smooth, continuous, invertable map between the
celestial sphere and a plane, with a “pointing center”, at which Jacobian
of the map is unit, meaning free of distortion. A Page is just the specifica-
Hyperatlas: A New Framework for Image Federation 3
tion of the projection: it is semantically equivalent to the WCS keywords
CRVAL (pointing center), CTYPE (projection type), CDELT (scale), and
CD/CROTA (rotation matrix). A page may have a pixel plane of infinite
extent, for example the TAN projection stretches to infinity.
• We define a Plate to be a rectangular area on the plane of a Page together
with single-channel pixel data filling that rectangle, and each pixel of the
plate having the same data types as the FITS standard. Thus a Plate is
semantically equivalent to a single-plane FITS image. Besides the data
content, a Plate adds the WCS keywords NAXIS (numbers of pixels),
CRPIX (shift from the pointing center).
• An Atlas is a coherent collection of Pages. Generally all pages in an atlas
have the same scale and projection, and the pointing centers are laid out
rationally, perhaps to cover the whole sky, perhaps just a particular region
of interest like the galactic plane.
• We define a Channel to be a uniform collection of Plates that is rendered
to a given atlas, so that there is at most one pixel for any position in the
sky – ie. a monochrome covering. In general there will be overlap of plates
– the same point of the sky shown on multiple pages – in which case the
channel-values of that point mus be the same independent of which page
it is on. The channel could represent fluxes in different wavelength bands,
times, polarizations, etc, or it may have been derived from other channels
by computation.
4. Hyperatlas Standard
In the interests of interoperability, we have specified a number of standard atlases
and called the collection the Hyperatlas standard. For more information see
[Williams 2003a]. We hope that large surveys and vital data can all be resampled
to the same set of pages, meaning that their images are on exactly the same pixel
grid, and can be compared directly.
Part of the atlas definition is the collection of pointing centers of its pages.
In Hyperatlas, there are currently two choices for this layout: TM and HV. The
former puts pointing centers on lines of constant declination, the latter on the
centers of Hierarchical Triangular Mesh (HTM) [Kunzst 1999] triangles. Each
layout comes with a parameter: for the TM layout, the parameter means the
separation between the declination lines; and for the HV layout it is the level
of the HTM hierarchy. In the figure is an atlas made from the TM-5 configura-
tion of pointing centers. Each of these layout types is defined by an algorithm
or pseudocode (see [Williams 2003]), and also available through redundantly-
implemented web services.
The atlas is further specified by the projection type (SIN, TAN, AIT, etc.
from the WCS library), and by the nominal scale at the pointing center. We
have discretized the scale numbers in powers of two: scale 20 is one arcsecond
per pixel, and generally scale S is 220−S arcseconds per pixel.
For example the atlas TM-5-TAN-20 consists of 1734 pages covering the
celestial sphere, with every point of the sphere at most 3.5 degrees (= 5/
√
2)
from a pointing center, a scale of 1 arcsecond per pixel, and the projection
TAN. Hyperatlas is not only documents, but is also implemented as web services
4 Williams, Djorgovski, Feldmann, & Jacob
TM-5 atlas
Figure 1. A layout of pages where every point of the celestial sphere is
at most 3.5 degrees from a pointing center. In this illustration, each page is
illustrated with a plate 5 degrees on a side.
[Williams 2003]. There is a service to provide all the pages of an atlas, or specific
page metadata by number or by sky position.
Recently, the International Virtual Observatory Alliance (IVOA) agreed on
a flexible and extensible framework for registering astronomical resources in a
distributed virtual registry [IVOA 2003]. We expect to enhance the Hyperat-
las standard by providing an extension schema for this registry. This would
allow Hyperatlas-compliant imagery to be published and discovered through
the Virtual Observatory, bringing to full flower the promise of image federation
technology.
Acknowledgments. We are grateful to the National Science Foundation
for support of this work through the National Partnerships for Advanced Com-
putational Infrastructure and through the National Virtual Observatory project.
SGD thanks the NASA AISRP program for support. JCJ thanks NASA for his
support.
References
IVOA, International Virtual Observatory Registry Framework, 2003,
http://www.ivoa.net/twiki/bin/view/IVOA/IvoaResReg
Szalay, A. S., Connolly, A. J., Szokoly, G. P., Simultaneous Multicolor De-
tection of Faint Galaxies in the Hubble Deep Field, AJ, 117, 68, also
astro-ph/9811086.
Williams, R. D., The NVO Hyperatlas Standard, 2003,
http://bill.cacr.caltech.edu/usvo-pubs/files/hyperatlas.pdf
Williams, R. D., Feldmann, M. T., Atlasmaker, Software to build Atlases of
Federated Imagery, 2003,
http://www.cacr.caltech.edu/projects/nvo/atlasmaker/


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