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Pan-STARRS - A New Generation Survey Telescope System

Nick Kaiser and the Pan-STARRS Team Institute for Astronomy, University of Hawaii

ABSTRACT

Pan-STARRS is a revolutionary optical/near-IR survey telescope system that is being developed by the U. Hawaii’s Institute for Astronomy. The optical design has a 1.8m primary aperture and delivers seeing limited images over a 7 square degree field of view with detectors having 1.4Bn pixels. The final four- aperture system will be sited on Mauna Kea, and a single aperture full-scale prototype is being deployed on Haleakala. In this talk, I discuss the technical background to the project; the broad range of science goals that the system will address; the design — optics; detectors; data processing pipelines; calibration systems — and the data products.

1. INTRODUCTION

Wide-field imaging surveys, as exemplified by the Palomar and UK-Schmidt sky surveys, fell into decline with the advent of CCDs in the ’70s. These gave great advantages of sensitivity and linearity but initially allowed only very small fields of view. Subsequent decades have seen exponential growth in area of detectors deployed on astronomical telescopes with a doubling time of about 18 months, conveniently matching the ‘Moore’s Law’ describing the growth of power of computer hardware. At the same time, there has been a major investment in image reduction software required to analyse the data from the ever-growing mosaic detectors. The current state of the art in wide-field imagers is exemplified by the 300M pixel Megacam on the 3.6m Canada France Hawaii Telescope and the 100M pixel Suprime detector on the 8m Subaru telescope. A parallel development has been the advent of surveys using purpose built dedicated observatories rather than competing for time on facilities developed to serve a broad user base. Examples of these are the Sloan Digital Sky Survey and the 2MASS sky survey. In recognition of these developments, and in anticipation of further rapid advances in software and detector technology, the US National Academy of Sciences Decadal Review proposed, as a high priority for the next decade, the development of a system they dubbed the “large synoptic survey telescope” with the basic requirement that is should be capable of surveying the entire sky to ∼ 24th mag in one week, and for which they argued that a 6m class telescope with something like a ∼ 7 square deg FOV would be adequate. Such an instrument would enable major advances in two separate areas of astronomy: • Repeated scans of the sky would advance the field of ‘time-domain astronomy’ — the study of variable, transient or moving objects — with applications ranging from minor planets in the solar system to cosmologically distant transient objects. • Stacking these images in the computer will, over an extended mission, result in cumulative static sky images from which faint galaxies may be detected, thus enabling a large range of other scientific investigations, mostly in the area of cosmology and including weak lensing and galaxy clustering to probe dark matter and thereby also dark energy. The Decadal Review was heavily influenced by the ‘Dark-Matter Telescope’, concept proposed by Tony Tyson and Roger Angel. The DMT concept has undergone various modifications over the years, with consideration of IR imaging and spectroscopic capability being discussed, but is currently conceived of as a 8.4m telescope, for which the proponents adopted the name LSST. Various subsequent reviews have endorsed the LSST concept, mostly in regard to its capability to probe dark energy.

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Table 1: Pan-STARRS Specifications optical design Ritchey-Chretien with 3 element wide field corrector field of view 7 square degrees collecting area 4x1.8m apertures detectors 1.4G-pixel cameras site Hawaii passbands g,r,i,z,y and w filters

Pan-STARRS is an alternative way to address the science goals set out by the Decadal Review. It is based on the following: • The observation that, given a site with excellent image quality such as Mauna Kea, it is possible to reach the 24th magnitude detection limit and scan rate requirements with a smaller net collecting area. • An aggressive approach to detector design, following the pioneering efforts of the detector group at the IfA who have repeatedly made major advances in cost/performance factors for mosaic CCD arrays. • That potential savings in telescope costs, and rapid delivery thereof, are allowed by a ‘distributed aperture’ design using multiple optical systems of modest size. and has the basic specifications shown in table 1: The fundamental figure of merit — the product of the collecting area and the field of view —- for Pan-STARRS is AΩ = 50 m2 deg2 which is several times larger than any existing facilities and a factor 6 times smaller than that proposed for the DMT/LSST. Pan-STARRS has also been designed to allow rapid readout in order to allow efficient detection of small objects in the inner solar system (this requires ∼< 30s integration times to avoid trailing losses and therefore also requires readout on the order of a few seconds to allow good duty cycle; this is another aspect in which Pan-STARRS represents a major advance over existing facilities that have readout times of tens of seconds). With these specifications, Pan-STARRS is capable of detecting objects with R = 24 in a 30s integration in median seeing conditions, and is capable of surveying ' 7, 000 square degrees per night, which is adequate to reach the Decadal Review top-level science requirements. Pan-STARRS is being developed as a phased effort. A single-aperture, but otherwise full-scale system, known as PS1 is being deployed on Haleakala. The telescope has been delivered and is undergoing testing. The next major step will be the installation of the wide-field corrector optics later this year and the deployment of the first large-area detector. This will be a 1/4 scale detector (300M pixels) that will be followed by the first (1.4) Giga-pixel camera (GPC1). Once commissioning is completed, PS1 will embark on a 3.5 year science mission, with operations funded by an international science consortium (PS1SC). The development and construction of the full-scale system (PS4) will parallel the PS1 science mission, with deployment planned for 2010 and with a ten year science mission 2010-2020. The following sections will describe the science goals and the top-level requirements flow-down, the design and the basic data products.

2. SCIENCE GOALS

2.1 Killer Asteroids and Solar System Science

A prime goal for Pan-STARRS is the detection of potentially hazardous asteroids. Analysis of existing survey results has revealed the distribution in size and orbital elements (semi-major axis, inclination and ellipticity) for objects in the inner solar system. The results allow one to derive the frequency of collisions with Earth as a function of energy, and show that ‘major global catastrophes’ — those events that result in most live being extinguished — happen every couple of million years or so, while collisions with ∼ 300m

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size bodies, which deliver energies on the order of 1000MT and which would cause massive regional damage, occur roughly every 70,000 years. While there is considerable uncertainty in estimates of the mortality rate, there is general consensus that this is dominated by the global catastrophe events generated by objects of size greater than about 1km in diameter and which can penetrate the oceans and deliver enough dust to the atmosphere to cause massive crop failures as in the ‘nuclear-winter’ scenario. While rare, the devastation of such events is so great that, statistically, one is as likely to die from an asteroid strike as in a plane crash. While this risk pales into insignificance as compared to some other forms of natural disasters, it is unique in that astronomical observations and orbital projections allow one to establish whether, in fact, Earth will be hit in the next century or so (it is difficult to to establish if an object will collide on longer time-scales owing to the chaotic nature of solar system dynamics). This realization led to the congressional ‘spaceguard’ mandate for NASA to support surveys to catalog such objects. The spaceguard goal is to reach 90% completeness by 2008. Currently, it is estimated that about 50% of km sized objects have been catalogued and that consequently about half of the collision risk that existed at the time of the initial spaceguard debated has now been eliminated. Once the spaceguard goal is reached, the residual risk will be distributed in roughly equal measure between the residual ∼ 10% of global catastrophe risk and the risk from smaller sub-km impactors. This is where Pan-STARRS comes in; its much greater sensitivity and surveying rate will allow it to detect much smaller objects. Currently, NASA has been tasked by congress to come up with a plan for a mission, or missions, to extend the survey completeness limits down to approximately 140m sized objects, below which size the risk falls because of the protection afforded by the atmosphere. By itself, Pan-STARRS will not be able to attain that goal, but it will, over a 10 year mission, be able to catalog 90% of objects of 300m or greater size, and will detect a large fraction of objects bigger than 140m, thus making major progress toward the new goal. The Pan-STARRS PS1 mission will also, in short order, be able to establish with much greater precision what is the actual risk as there is considerable uncertainty in the frequency of impacts. In the process of surveying for potentially hazardous asteroids Pan-STARRS will detect massive numbers of objects of all classes in the solar system. Figure 1 shows, for each class of object, how many are currently known and how many will be discovered by Pan-STARRS.

2.2 Stars and the Galaxy

Pan-STARRS will be a powerful tool for studying the contents and structure of the Milky Way. Currently available all-sky surveys are either restricted to bright stars (Tycho, Hipparcos) or are based on photographic surveys (e.g. USNO-B). The latter reaches limiting magnituded ∼ 20 and has astrometric precision of around 0”.2 (both for random statistical measurement error and for large-scale systematic errors arising from the plate-modelling process) and crude photometric precision. By comparision, Pan-STARRS will reach limiting magnitudes of 24th magnitude and, for somewhat brighter objects, will attain astrometric precision of 5-10 milliarcsec (per visit) and photometry at one hundredth of a magnitude precision or better. Ultimately, Pan-STARRS will itself be surpassed by GAIA, which will achieve astrometric precision at the tens of micro- arcsec level, but GAIA data products are not expected until around 2020 and, until then, Pan-STARRS will be a huge leap forward. The great astrometric precision will allow determination of parallaxes and proper motions. The former will provide a complete stellar census out to ∼ 100pc distances and down to ∼ 24th magnitude, and will result in great improvement in our knowledge of the low-mass initial mass function, extending to sub-stellar objects, and is estimated to increase the number of known brown dwarfs by 1-2 orders of magnitude. Multiple visits over the ten-year mission lifetime will allow the astrometric precision to be further improved, and will result in proper motions for huge numbers of stars with a precision of a few km/s at a distance on the order of a kilo-parsec. This, together with the precise multi-color photometry will be a vital tool for understanding the formation history of the galaxy. Having tens of visits per passband will allow Pan-STARRS to generate an enormous catalog of variable stars, and thereby allow the use of classical standard candles such as RR Lyrae and Cepheids for probing the structure of the galaxy and its local surroundings. The star counts and colors will be used to improve models for the thick disk, thin disk and halo structure of our galaxy, which is currently presenting an interesting challenge to conventional theories for galaxy

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Figure 1: Inventory of minor planets in the solar system. For each class of objects, the bar on the left shows the number of objects that are currently known and the middle and right bars show how many will be detected by Pan-STARRS (PS4) after 1 year and after 10 years. For outer solar system objects, most new discoveries are made in the first year.

formation.

2.3 Static Sky Cosmology

Pan-STARRS will survey in two major modes; surveys covering 3/4 of the sky and, in addition, deep and medium deep surveys of selected areas, with the latter surveyed at a cadence suitable for detection of supernovae etc. All of these images will be stacked, resulting in 5-passband static-sky images reaching ∼ 26th magnitude over 3π steradians and much greater depth in the selected areas. A major goal for Pan-STARRS is to constrain the cosmological parameters of the Universe, and, in particular, to probe the dark energy. The cumulative static sky images will allow this via two complementary approaches: One is weak lensing, which depends on the geometry of the Universe and on the evolution of the dark-matter structure, both of which depend on the dark energy. The other is the evolution of the clustering of the galaxies. As recently shown by the SDSS and 2dF surveys, the galaxy power spectrum displays the wiggles imposed on ∼ 100Mpc scales by hydrodynamical processes operating at z ∼> 1000 when the plasma was ionized and, locked together with the background radiation, supported acoustic oscillations generated from the primordial perturbations responsible for structure formation. This has generated intense interest in the possibility of determining the expansion rate using these wiggles as a ‘standard ruler’. In addition, the shape of the overall power spectrum is sensitive to the dark energy content.

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Both of these cosmological tests are quite challenging. To constrain the amount of dark energy (as characterised by the equation of state parameter w) and its evolution at better than the ∼ 10% level requires measurement of the structure, and its evolution, with a precision of better than a few percent. While the statistical precision afforded by e.g. weak lensing is in principle sufficient, realistically the results may be plagued by systematic errors. In this regard, Pan-STARRS will have significant advantages over current surveys, in that the very large area covered and the large number of visits to each field will either allow such systematics to be modeled and subtracted, or, if they prove to be unpredictable, they will tend to decrease as the inverse square root of the number of exposures. For both types of studies, photometry precision is critical in order to provide photometric redshift information which is uniform and has well understood error distribution. In addition, for weak lensing, modeling and control of the point spread function is at a premium. In addition to these probes of dark energy, the static sky images will be used to generate catalogs of clusters of galaxies, the evolution of which is another complementary probe of cosmological parameters; higher order statistics; and bias and galaxy formation by studying the clustering as a function of color, type and surface brightness. All of the above mentioned cosmological studies will rely heavily on massive numerical simulations of cosmological structure formation.

2.4 Supernova Cosmology and other Variable/Transient Phenomena

The medium deep survey areas will be visited at a ∼ 4 day cadence and will discover tens of thousands of supernovae. The SN1a can be used to improve the precision of the Hubble diagram on which the original indication of dark energy was based and, more generally, Pan-STARRS will be a great source of data for studying the physics of supernovae and also for probing the star formation history. Pan-STARRS will also be able to detect transient and variable objects of many types: • Active Galactic Nuclei. Variability, as opposed to color, selected samples will shed new light on the properties of such objects, and, additionally, optical variability can be usefully compared to variability at X-ray and shorter wavelengths from surveys such as GLAST. • Gamma-Ray Bursts. PS will detect many optical counterparts to GRBs and will be able to test theories for GRBs that predict that lower energy photons are emitted in a wider solid angle resulting in so-called ‘orphan’ events. • Occultations of stars by planet transits. Pan-STARRS is sensitive to Jupiters around sub-solar mass stars or Earths around brown dwarfs. • Stellar variability. As mentioned, Pan-STARRS survey cadences will allow detection of stars with all manner of intrinsic variability. In addition, dedicated surveys may be used to probe for microlensing events.

3. DESIGN

The major science goals described above lead to the top-level requirements from which the system design has been derived. In some cases these are fairly specific; for example, the inner solar system goals lead to the stringent requirements for short (∼ 30s) integration times to avoid trailing losses and correspondingly short read-out times. Other requirements, such as photometric precision, are critical for a wide range of science goals. Given the top-level science-driven requirements, the final design would ideally be derived by an optimization of performance vs. price. In practice this is difficult, if not impossible. Instead, in deriving engineering requirements we made considerable use of the known, unavoidable, limitations imposed by the environment and the basic laws of physics and the principle that the requirements for each component should be that the impact on overall figure of merit not be the dominating restriction on performance. While the figure of merit depends somewhat on the precise science goal, a reasonable single criterion is the efficiency for detection of faint point sources, for which the figure of merit is the etendue AΩ divided by

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the area of the point spread function ∆Ω and augmented by the overall quantum efficiency and various ‘duty-cycle’ effects. This gives a single objective criterion for comparing design performance with costs, and the constraints imposed on the final design were such that, for example, optical design, pixel size etc be allowed to degrade performance by ∼ 10 − 20% but not more, so that the final design allows performance that is reasonably close to the ideal allowed physically given the constraints of seeing limited observations against the sky background. UHAn-IfA overall view of the system components is shown in figure 2 and below we describe the opticalPa design;n-STARRS the camera;Pan-STARRS the data processing Overview pipeline and finally, the various auxiliary calibration systems.

Pan-STARRS Subsystems

• TEL – Telescopes

• CAM – Cameras

• OTIS – Observatory, Telescope & Instrument Software

• IPP - Image Processing Pipeline

• MOPS – Moving Object Processing Software

• PSPS – Published Science Data Products

AMOS Conference UNIVERSITY OF HAWAII 27 Sept 12th 2006 Figure 2: OverviewI ofNST Pan-STARRSITUTE FOR ASTRONO subsystems.MY

3.1 Optics

The optical design, shown in figure 3, delivers good image quality over the full 3 degree diameter field of view as shown in figure 4. The filter passbands and overall system quantum efficiency are shown in figure 5 which shows that the performance of Pan-STARRS in the near-IR will be outstanding.

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UH-IfA PS-1 Optical Layout with Baffles Pan-STARRS

Figure 3: Pan-STARRS optical design is a Ritchey-Chretien with 1.8m diameter primary and 0.9m diameter secondary mirrors and a three element wide field corrector consisting of refractive elements L1, L2, L3. The PS1 prototype will initially have a monolithic L1, but the design is such that this can later be replaced by a multi-element lens containing a prism with siloxane fluid between the glass elements to implement a atmospheric dispersion compensator.

AMOS Conference UNIVERSITY OF HAWAII 29 Sept 12th 2006 INSTITUTE FOR ASTRONOMY UH-IfA Spot diagrams Pan-STARRS

Figure 4: Spot diagrams for various field positions for the Pan-STARRS optical system. For reference, the bar at the side of the upper left diagram is one arc-second, so at all field positions any aberration from the telescope results in negligible broadening of the point spread function under realistic seeing conditions.

AMOS Conference UNIVERSITY OF HAWAII 30 Sept 12th 2006 INSTITUTE FOR ASTRONOMY

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3.2 Detectors

The large field of view (7 square degrees) and the high quality seeing provided by Hawaiian sites leads to the requirement of approximately 1.4 billion pixels per focal plane for adequate sampling. The physical pixel size is 10µm, corresponding to an angle of 0”.26. Together with the requirement of short (∼ 5s) readout commensurate with 30s exposure times this requires a massively parallel architecture. The solution adopted is an “array of arrays” in which the focal plane is constructed from a 8x8 chess-board of 5cm square monolithic devices and where each device is an 8x8 chess-board of independently addressable “cells”, each of size 600x600 pixels. To meet the read-out rate with acceptable read-noise requires that 8 cells on each device UH-IfA be read simultaneously, so overall there are 512 parallel read-out channels. In addition to parallelization Pan-STARRS of data flow,SDSS a further advantage and of Pan-STARRS the array design for the detectors B isandpasses that they allow enhanced yeild. In conventional designs, a single flaw in manufacturing can render an entire device non-functional whereas with the PS devices only that cell which contains the flaw will be affected. Since regions around bright stars will necessarily be lost for science, and the surveying strategy involves combining multiple images, losses of small regions do not seriously affect the overall system performance.

AMOS Conference UNIVERSITY OF HAWAII 34 Sept 12th 20Figure06 5: Pan-STARRS passbands and systemINST quantumITUTE FOR efficiency. ASTRONO TheMY g,r,i,z filter passbands have been designed to closely match those used by the Sloan Digital Sky Survey. An early design decision was to dispense with u-band imaging as it imposed difficult constraints on the optics, and the science goals are weighted more towards the red-end of the spectrum. The detectors are thick deep-depletion devices that allow excellent sensitivity out to the cut-off imposed by silicon.

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The detectors feature ‘orthogonal transfer’ (OT) technology. In conventional devices, there are 3 gates per pixel, allowing the charge to be transferred in one direction during read-out. In an OT device there is an extra gate, and this allows charge to be ‘clocked’ in 2 dimensions. This technique, developed by John Tonry and implemented on the OPTIC camera used on the UH 88” telescope, allows on-chip fast guiding which can cancel the effects of wind shake and ameliorate some of the effect of atmospheric seeing. The OT capability is nicely integrated with the array design of the detectors. Any cell that happens to contain a bright star can have the region around the star read-out at video rates in order to monitor the motion of the star’s centroid. The information from these guide stars is analyzed and used to generate guide signals to move the charge in the other cells to track the the motion of the images.

UH-IfA 3.3 Image Processing Pipeline and Data Products Pan-STARRS Pan-STARRSReal-Time will generate data at Image the rate of terabytesAnalysis per night, and an image processing pipeline (IPP) has been developed to process these data in near real time. The IPP will perform all of the usual steps involved in flat-fielding and sky background subtraction. In addition, it will perform registration to map pixel coordinates to sky coordinates and pixel values to intensity in order to generate cumulative static sky images and difference images from which transient, variable and moving objects can be detected.

PS-1 : N sequentially PS-4 : 4 simultaneous

raw stacked image The Static Sky - Image Combine static & sky Image Difference image + difference image

cleaned stacked NIVERSimITYa gOeF AWAII AMOS Conference U H 68 Sept 12th 20Figure06 6: Pan-STARRS image processingINSTIT pipeline.UTE FOR AS TheTRON IPPOMY has been designed to take as input a set of ‘dithered’ images of a patch of sky — for PS4 these would typically be taken simultaneously with the 4 separate optical systems. After independent flat-fielding and sky subtraction these images are combined, with cosmic ray rejection, to form the current image of the patch. A PSF matched template is then generated from the static-sky data store and is subtracted to generate a difference image that is blank, aside from transient, variable and moving objects. Finally, the stacked image will have the objects detected in the difference image removed, and the result will then be accumulated into the static sky.

The IPP will result in the following basic data products: • Instrumental catalogs. These are generated from the flat fielded and sky subtracted, but unwarped,

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images. These are useful for projects requiring the utmost in astrometric and photometric precision (whereas warping the images to sky coordinates necessarily involves some loss of information). These catalogs will include postage stamp images for brighter objects to allow advanced image processing techniques such as de-aliasing and de-convolution. • Static sky images. These consist of at least a ‘signal’ and ‘exposure’ map. In addition, there will be some redundancy built in to allow for recovery from data corruption. • Static sky catalogs. Photometry and astrometry of objects detected in the static sky images. These will include the time history of magnitude measurements to allow study of nuclear variability of galaxies. • Difference image detection stream. These may be augmented by the recent history of difference images in order that one can ask what a source did prior to triggering detection.

3.3 Calibration and Control of Systematics

Pan-STARRS requirements are to reach 1% photometric precision, and the goals are to do even better. Similarly, science goals such as weak lensing require careful control of the point-spread function. In order to achieve these demanding goals the observatory will be equipped with a number of auxiliary systems to provide calibration and control of image quality. Regarding photometric calibration, Pan-STARRS will depart from the standard procedure or simply observing Landolt standard fields occasionally. Absolute calibration of the detectors will be performed in the lab before deployment. A ‘calibration screen’ fed by a tunable laser will be used to provide absolute measurement of the total system throughput. These measurements will be made frequently in order to identify any changes in QE. An imaging sky probe (similar to that used on CFHT) will piggy-back on the telescope and will monitor atmospheric transparency using bright stars and will also monitor the air-glow. A spectroscopic sky probe is planned. This will take low dispersion spectra of both stars and air-glow. These data will be analyzed using the MODTRANS software to generate detailed models for the atmosphere and hence a high spectral resolution model of the air-glow — from which fringe frames can be synthesized — and of the atmospheric transmission — which can be folded through the transmission function to determine photometric zero points. The atmospheric dispersion compensator will improve the precision of photometry, especially in regions of high stellar density. A high priority for PS1 is to generate a dense grid of astrometric and photometric standards. The large number of independent exposures will help overcome many sources of systematic errors. Regarding control of the PSF, PS will feature two wave-front sensing systems. One of these is a deployable Shack-Hartmann system which can be used to diagnose problems. The second is a curvature sensing system similar to that planned for the VISTA telescope that will operate continuously to provide wave-front sensing. These measurements will be used to actively control the collimation and alignment of the telescope and to control the figure of the primary, which is supported on a network of pneumatic supports. Other features that will improve the PSF quality are the use of orthogonal transfer devices, which can remove quadrupole anisotropies arising from e,g, wind-shake, the ADC, and as with photometry, the ability to combine large numbers of images will help.

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______PAN-STARRS 2006 AMOS CONFERENCE Page 393 Maui, Hawaii TECHNICAL PAPERS ______

SSA Applications of the PS1 AP Catalog

David G. Monet U.S. Naval Observatory Flagstaff Station The Pan-STARRS Team Institute for Astronomy, University of Hawaii

ABSTRACT

The Pan-STARRS PS1 Astrometry and Photometry (AP) Catalog is described by [1] in these Proceedings. This discussion focuses on the Space Situational Awareness (SSA) applications of the astrometric accuracy of this catalog, and how the PS1 AP survey can meet these Requirements.

1. INTRODUCTION

The PS1 Survey, described more fully in [1], is expected to produce a catalog of astrometric data of unprecedented accuracy and depth. It will be useful for many scientific and functional tasks, and it will enable new SSA opportunities based on enhanced metric accuracy. One meter at the distance of the geo- synchronous orbit corresponds to 5 milliarcseconds of angular measure. The Requirements for the PS1 AP catalog are for an astrometric accuracy of 10 milliarcseconds, so this means that the error associated with a single catalog star corresponds to 2 meters at GEO. As shown by [2], this astrometric accuracy leads to metric observations of satellites clearly showing velocity changes of 1 meter/second. The SSA discussion presented here is an overview at the unclassified level.

2. SPACE SITUATIONAL AWARENESS

The traditional usage for high-accuracy star catalogs is in the area of in-frame metrics. The PS1 AP catalog should go faint enough (red magnitude of 20 or fainter) to guarantee that many catalog stars will be in the field of view of almost every SSA sensor. These catalog stars provide a mapping for every frame into the system of (α,δ) with an accuracy of better than 10 milliarcseconds, and provide a photometric calibration with an accuracy of better than 5%. This enables both metric and photometric data to be taken from the same image, and enables SSA observations during many classes of non-photometric sky conditions. The five colors in the PS1 AP catalog enable the correction of the catalog magnitudes into the system of the SSA sensor by means of a color term computed for each star.

The other area where high-accuracy metrics are important is obtaining attitude knowledge for a spacecraft. In many cases, particularly below the geo-synchronous orbit, the pointing accuracy for sensors and systems mounted on spacecraft is limited by the real-time knowledge of the attitude. Spacecraft position is usually less of an issue because high-accuracy GPS receivers have been space-qualified. High-accuracy star catalogs such as the PS1 AP catalog are necessary for improved attitude knowledge, but a new generation of star trackers will need to be developed to take full advantage of them.

3. THE ASTROMETRY CLIENT

Astrometric processing of the PS1 data is done in two places, the Image Processing Pipeline (IPP) and the Astrometry Client (AC). This design was chosen so as to minimize the impact of enhanced astrometric processing on the PS1 design. Centroiding is done in the IPP, and this is the only instance where astrometric processing involves processing of the image pixels. The remainder of the astrometric processing is done by the AC and involves computing mean positions for all objects in the catalog as well as secular changes in the positions of some objects resulting from proper motion, parallax, and perturbations caused by unseen companions.

The IPP performs many image processing tasks, but as far as astrometry is concerned the most important is computing the image centroid. Whereas there is a great deal of astrometric heritage for the processing of

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data from traditional charge coupled devices (CCDs), there is very little heritage for the processing of data from the orthogonal transfer CCDs (OTCCDs) that PS1 will use. In advance of the acquisition of real PS1 data, all that can be done is to prepare various centroiding algorithms for insertion into the IPP. Of particular interest are the traditional least squares fit to a circular Gaussian function and the centroids computed in the fit to the observed point spread function (PSF). Should neither of these perform according to expectation, a new algorithm will be developed. Fortunately, the design of the IPP supports the insertion (or replacement) of new modules with a minimal impact on the rest of the IPP.

The AC is responsible for the compilation of catalogs from the ensemble of all centroids computed for all objects detected on all frames. In the current design, the AC is a stand-alone computer cluster whose tasks are to accumulate the astrometric portion of the IPP output, to store these data for at least one year, and to compile the astrometric catalogs on an ongoing basis. When the PS1 Published Science Products Subsystem (PSPS) is available, the AC may be reconfigured to use a “crawler” in the PSPS database to replace the IPP output capture and storage functions.

Traditionally, the algorithm for astrometric catalog compilation is based on Block Adjustment (BA), and this has been implemented in the current version of the AC. This technique assumes that only a few free parameters are needed to describe the astrometric transformation of each frame, and that these should be computed from a single solution that minimizes the residuals from all frames. In most instances, BA is a relatively simple operation applied to a few tens or hundreds of frames. For PS1, the global minimization of residuals will need to treat tens or hundreds of thousands of frames in a single solution. The inversion of large matrices is a subtle art, and various compromises are needed to keep the numerical processing under control. The initial design uses the algorithm presented in [3].

A curious attribute of the AC processing is that there is no universal agreement as to the quantity that should be minimized by the least squares solution. One can argue that χ2 should be computed from the residuals between the observations and an external reference catalog. On the other hand, one could argue that χ2 should minimize the residuals between the individual observations and the catalog mean positions. Because of this, the formalism developed by [4] has been adopted.

2 2 2 2 χ = Σm Σf Σs wm((αfs - αm) cos(δ) + (δfs - δm) )

The sums over frames (f) and stars (s) are as usual. The difference is to include each of several different error models (m) with an appropriate weight (wm) that can be changed between solutions or during the minimization process. Currently, proposed models include traditional optical catalogs such as Hipparcos, Tycho-2, UCAC, USNO-B, and 2MASS; radio catalogs such as ICRF and RORF; and the internal measures of the same stars taken on different frames. A simulator+solver has been developed, and is being used to understand the impact of the various statistical models, and to characterize the behavior of solutions with tens or even hundreds of thousands of free parameters. (According to the current PS1 cadence simulator, PS1 will take about 100,000 exposures per year.) At the current level of algorithm development and processing power, it takes about 2 hours to solve a year’s worth of PS1 data in a single color.

Already, the AC simulator has demonstrated the difficulty of removing systematic errors from the astrometric solution. Whereas most of the scientific applications of the PS1 AP catalog are not overly concerned with the absolute astrometric accuracy (i.e., the fidelity of the catalog coordinates (α,δ) to the system of coordinates commonly referred to as J2000), the SSA applications are quite sensitive to this calibration because of the need for accurate orbital solutions. The formalism adopted for the AC allows for two different solution algorithms, and these can be compared to sense and remove systematic errors. The first approach is to compute the astrometric transformation between each PS1 frame and the reference catalog, and to use the BA solution to minimize the residuals averaged over the ensemble of frames. The second approach is to use the very sparse but very accurate radio catalogs to calibrate the PS1 frames that contain these objects, and to use BA to build the catalog between the calibrated frames in an iterative manner. Both approaches will be tried, but the optimal choice depends on the as yet unknown error distribution of PS1 data.

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4. CONCLUSIONS

The AC hardware has been purchased and delivered, and the initial design and build of the software has been completed. A simulator is being used to debug the software as well as to learn about the performance of the algorithms under various models for the error distribution. The next big step will be the acquisition and processing of PS1 data during the commissioning phase. As noted above, there is minimal heritage in processing of OTCCD data, and the camera and telescope stability have yet to be characterized. A preliminary version of the AC is available, and frantic developments are expected in the very near future. The development of the AC is a team effort, and the presenting author would like to acknowledge the very significant contributions made by many members of the PS Team.

4. REFERENCES

1. Chambers, K. et al., AMOS Technical Conference Proceedings, 2006. 2. Monet, D. et al., AMOS Technical Conference Proceedings, 2005. 3. Press, W. et al., Numerical Recipes in FORTRAN, Cambridge University Press, 1992. 4. McCallon, H. and Cutri, R., http://irsa.ipac.caltech.edu/Missions/2mass.html.

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The Pan-STARRS Moving Object Processing System

Robert Jedicke for the Pan-STARRS team Institute for Astronomy (IfA), University ofHawaii, Honolulu, HI

1. MOVING OBJECT PROCESSING SYSTEM Pan-STARRS+MOPS (Moving Object Processing System) will be the world’s first integrated asteroid detection, linking, orbit determination and database system. Combining the processes provides Pan-STARRS a tremendous advantage in sky area coverage because it can spread the requisite number of detections over many nights with a concomitant increase in the number of NEO discoveries. Furthermore, by retaining control over all the processes Pan-STARRS can determine the efficiency of the entire sub-system as well as the efficiency of every step. This capability is critical to monitoring the MOPS performance and de-biasing the science data to account for observational selection effects. Fig. 1 provides a high level concept of operations for the MOPS. Input consists of image meta-data (e.g. boresight, limiting magnitude, filter) and all source detections with S/N>3-σ from the Image Processing Pipeline’s (IPP) difference images [1]. This includes apparently stationary transients since they may be very distant slow moving objects, and also those trailed detections consistent with being images of fast moving objects.

Figure 1 - Moving Object Processing System concept of operations. Data in the upper left region surrounded by dotted lines is provided by the IPP. Systems below the horizontal dotted line are not part of the MOPS.

The combinatoric problem of properly linking detections of the same object observed on a few nights over a period of a couple weeks could computationally expensive. On the ecliptic and to a limiting magnitude of r~24 we expect that there will be about 250 real moving objects per deg2 or almost 2000 asteroids and comets per single Pan- STARRS field. Asteroid sky-plane density drops quickly off the ecliptic while the rate of false detections remains

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constant. At 5-σ we expect a maximum of a 1:1 ratio of false:real detections on the ecliptic (i.e. ~2000 false 5-σ detections per field) while at 3-σ we expect about 200,000 false detections per field or a false:real ratio of about 100:1!

Figure 2 - NEOs with H<18 (larger than 1km diameter) detected in a single lunation by PS4. The opposition region is the large area near 100deg RA and the other areas represent the morning and evening sweet spots.

The current survey strategy for solar system objects is to obtain two images of each field on a night separated in time by a transient time interval (TTI) of about 15 to 30 minutes. Roughly the same fields are visited 3-4× per lunation. We envisage a PS1 survey strategy [2] that combines a photometric & astrometric survey, a medium-deep survey and a solar system survey in an efficient manner that will allow 60% of surveying time to be utilized in a cadence suitable for linking solar system objects. An additional ~5% of surveying time will be devoted to surveying in the ‘sweet- spots’ [3], the sky within about 10˚ of the ecliptic and with solar elongation from 60˚ to 90˚. Fig. 2 shows a single lunation of surveying with this survey pattern where is it clear that the sweet-spots are particularly rich in NEOs due to looking along the Earth’s orbit where NEOs must pass by definition. It is the responsibility of MOPS to identify candidate moving object ‘tracklets’ in the images acquired within a TTI on each night. A tracklet is composed of detections that are within an angular distance and position consistent with their elongation and orientiation with respect to one another. As shown in Table 1 our efficiency for creating tracklets [4] is essentially 100% as determined from a realistic solar system model and survey simulation. All known objects that might appear in each field are associated with identified tracklets when available (a process referred to as attribution). Tracklets that can not be associated with known objects are stored for future use. Table 2 shows the degradation in tracklet accuracy if we do not make use of the trailing information (trail length and orientation) when forming tracklets. While we can maintain a high tracklet formation efficiency, the accuracy drops

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by more than a factor of two and this would, in turn, require an even larger number of false inter-night linkages to be erroneously identified.

Table 1 - Standard MOPS tracklet identification performance in the opposition and sweet spot regions for the full solar system model (SSM) with full density false detections in a single lunation. Columns are: Available - The number of possible synthetic tracklets that could be identified with detections separated by less than one hour; Efficiency - the percentage of synthetic tracklets that were actually identified; Accuracy - the percentage of all identified tracklets that were properly identified as being synthetic; Mixed - the percentage of identified tracklets consisting of synthetic detections from different objects; Bad - the percentage of identified tracklets consisting of both synthetic and false detections; False - the percentage of identified tracklets consisting of false detections.

Table 2 - As in Table 1 but for a non-standard MOPS implementation that ignores trailing information (orientation and length) for each detection. i.e. each detection as treated as a simple point.

Every time a new TTI image pair is acquired, the MOPS automatically searches the meta-data database of images acquired in the past 14 days to determine if there are 3 or more nights of images including the current image that might contain unknown moving objects that could be linked together. When a multi-night set of TTI image pairs are available the MOPS extracts all un-attributed tracklets from those images and attempts to link them together into candidate ‘tracks’ that might be consistent with a newly identified solar system object. The combinatoric difficulty in creating tracks has been solved using a variable kd-tree algorithm [4]. By implementing a few passes through the data appropriate for different classes of solar system objects we have achieved >99% efficiency in creating tracks for objects in a realistic but synthetic survey as shown in Table 3 and Table 4. Once tracks have been created each one must be tested for compatibility with a heliocentric orbit by attempting an initial orbit determination (IOD). Tracks for which the IOD provides a suitably low χ2 residual with respect to all the detections are then passed to a differential orbit determination routine (OD) that attempts to fit the orbital parameters to the observations and further reduce the residual while improving the orbit. We are currently studying the IOD and OD efficiency at producing useful orbits – the orbits do not necessarily have to be ‘correct’ if they can be used to link the object to future (attribution) or past (precovery) detections. E.g. for slow moving objects we may choose to use an IOD or OD with fixed eccentricity to allow convergence in the orbital solution. Our preliminary indications are that >95% of real tracks produce usable IODs and a large fraction of those produce usable ODs. We have usable prototype code in place for moving object attribution and also precovery detection identification but have not yet examined and tuned their performance. Similarly, the process of ‘orbit identification’, in which the same object may be separately identified and linked in different apparitions without it being possible to attribute or precover mutual detections, but where their identity may be discovered through the similarity of their orbit elements, also exists in prototype form.

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The MOPS incorporates efficiency determination software (EDS) directly into its architecture. The EDS requires a high-fidelity solar system model containing >107 synthetic asteroids and comets that we have developed for this purpose. The model contains realistic orbit and size distributions for all the objects including tri-axial ellipsoid shapes, random pole orientations and a spin period distribution. Each time new detections are provided by the IPP the MOPS generates synthetic detections that should appear in the image according to the solar system model and the meta-data from the IPP (e.g. limiting magnitude, trailing effects, chip gaps). The synthetic detections are injected into the MOPS pipeline and analyzed at the same time and in exactly the same manner as the real detections. Since we know what synthetic detections went into the MOPS we can monitor their progress through the system in nearly real-time and determine if there is a problem with any MOPS sub-system. The ability to determine the overall efficiency of the system is critical to correcting the data for observational selection effects.

Table 3 - Track formation efficiency for the full solar system model with realistic astrometric error and false detections. The object types are organized in terms of heliocentric distance with Near Earth Objects (NEO), Main Belt (MB), Trojan (TRO), Centaurs (CEN), Trans-Neptunian Objects, Scattered Disk Objects (SDO) and Comets (COM).

Table 4 -MOPS track identification performance for different solar system models (SSM). The SSM is the full density model and SSM/250 is every 250th object. The MB/N models have full densities of all SSM components except for the MB which includes only every N th object. Each model contains false detections at the full density level. Columns are: Objects - the number of different synthetic solar system objects included in the simulation; Density - density of objects in the model compared to the full model; Available - the number of synthetic tracks generated in the simulation; Linked - the number of synthetic tracks that were properly linked; Efficiency - the fraction of generated tracks that were correctly linked; Tracks - the total number of tracks found in the simulation; Accuracy - the percentage of identified tracks that represent synthetic tracks.

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2. PS1, MOPS AND THE EARTH IMPACT RISK The prototype Pan-STARRS telescope on Haleakala will provide the best measurement of the NEO population to date by providing a sample that is much larger than the entire data set currently in hand and, more importantly, doing so with a single well-calibrated detection system. This will dramatically decrease the current errors in the NEO’s orbit and size distribution (especially at small sizes) thereby allowing a much better characterization of the Earth impact risk. Collapsing the error bars on the size and orbit distribution is also important because it allows a better tuning of the survey characteristics of future NEO surveys. For instance, it is possible that the impact risk is larger than currently estimated because there may be local enhancements in the orbit distribution of PHOs. The fact that Tunguska (a once in a 1000 year event) and Apophis (a once in a tens of thousands of years event) occured within a century of one another makes it reasonable to speculate that the orbit distribution of NEOs is not as smooth as current models [5,6] would suggest. PS1 will resolve these issues.

3. SUMMARY The PS1 and Moving Object Processing System (MOPS) will be the world’s first integrated asteroid and comet discovery, linking, orbit determination and database system. We have developed new algorithms to handle the combinatoric problem of linking detections on a single night and between nights within a lunation. We have developed the world’s first comprehensive model of the solar system including over 107 objects that might be discovered by PS4 during the course of its ten year operational lifetime. The MOPS is the first asteroid and comet linking system in the world to embed an efficiency determination and monitoring system. By the end of 2007 the PS1 system should be discovering more asteroids each month than all other surveys in the world combined. When PS4 begins operations it will identify in a single month more asteroids than are currently known.

4. REFERENCES 1. Magnier et al., AMOS 2006. 2. Chambers et al., AMOS 2006. 3. Chesley, S. R., & Spahr, T. B. (2004). Earth impactors: orbital characteristics and warning times. In Mitigation of Hazardous Comets and Asteroids, p. 22 4. Kubica, J. et al. (2006). Efficient intra- and inter-night linking of asteroid detections using variable kd-trees. Submitted to Icarus. 5. Bottke, W. F., A. Morbidelli, R. Jedicke, J. Petit, H. F. Levison, P. Michel, and T. S. Metcalfe (2002). Debiased Orbital and Absolute Magnitude Distribution of the Near-Earth Objects. Icarus, 156, 399-433 6. Stuart, J. S., & Binzel, R. P. (2004). Bias-corrected population, size distribution, and impact hazard for the near-Earth objects Icarus, 170, 295

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The design of the Pan-STARRS telescope #1

Jeff Morgan, Walt Siegmund, Carole Hude, and the Pan-STARRS Project The University of Hawaii, Institute for Astronomy.

1. Introduction One of the main goals of the Pan-STARRS project is to collect multi-color imaging data of the entire sky visible from a single location that is limited in faintness, resolution, and photometric quality primarily by the sky conditions of a world class observing site. The goals of the project include acquiring this full sky coverage on a weekly basis in order to catalogue temporal changes in the night sky. The scientific impacts of a successful Pan- STARRS project are numerous and are discussed in other articles at this conference. It is not hyperbole to say that the Pan-STARRS project has the potential for having a significant impact on all aspects of modern day astronomical research.

The weekly coverage of approximately 31,000 square degrees of sky in 5 band-passes with a spatial resolution of ~0.6 arcseconds is a daunting challenge to the optics, the electronics, and to the software that will be used in the project. To achieve the required étendue, or throughput, the telescope must have a very wide field of view while preserving excellent image quality and the camera must have a pixel format of unprecedented size. To achieve the required efficiency of operation implied by these goals, the telescope must have the capability of rapid slews and quick settling and the camera must be capable of reading out in just a few seconds without the introduction of appreciable read noise. The telescope structure and enclosure must be designed to rapidly equilibrate so that significant time is not spent each night waiting for the optics to become useable. And the scheduling and nightly operation of the telescope must be automated to avoid inefficiencies associated with the stress and tedium of rapid and repetitive observations. All of this must, of course, be accomplished under continually changing weather conditions which makes the scheduling algorithms particularly complex and challenging.

During the development of the Pan-STARRS project it was decided that much of the technical challenge comes in the successful integration of the many required subsystems: camera, telescope, enclosure, filter mechanism, shutter, observatory scheduler, and image processing. For this reason it was concluded that producing a prototype telescope would be very beneficial for the project. The PS1 telescope is providing us the opportunity to test and debug the integration of all of these subsystems before the full telescope system is put together.

In addition to this benefit, the PS1 telescope is being assembled in order to acquire an initial view of the sky that will be used as the template with which subsequent measurements are differenced. When fully operational, the PS4 system will utilize difference images to determine what has changed in the sky between exposures. One of the main goals for the PS1 telescope is to acquire an initial astrometric and photometric survey of the sky which will be used by the PS4 array when once it is in operation.

The scientific goals, cadences, and instrument specifications are therefore slightly different from those of the full array which were discussed above. Below we will detail some of the critical design specifications for PS1 and try to point out where they differ from the PS4 specifications. In addition, there may very well be changes in the design of the full array which have been inspired by our experiences with PS1. However, that doesn’t mean that PS1 is not a very powerful instrument in its own rite. For instance, some have predicted that in just the first month of operation, PS1 will discover more asteroids than have been previously discovered throughout all previous history.

2. The PS1 Optics The layout of the PS1 optical design is given in Figure 1. The design consists of a modified Ritchey-Chrétien design with the addition of 3 refractive correctors. The primary mirror (M1) has an aperture of 1.8-m, the secondary mirror (M2) has a diameter of 0.9-m and the correctors (L1-L3) have diameters of approximately 0.6- m. The 3rd corrector (L3) forms the window of the CCD dewar and therefore must be thick enough to support the force of 1 atmosphere of pressure without deforming enough to have an impact on the image quality of the telescope.

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There are three layers of filters in the PS1 design. Two filters will be held by a filter mechanism on each layer, so that the system will have a maximum capacity of 6 filters. The PS1 filters are the largest astronomical quality filters ever produced. Each filter has a clear aperture of 0.48-m. The current complement of PS1 filters includes g, r, i, z, and y band-passes.

Figure 1. The PS1 Optical Layout

The PS1 optics support a very wide field of view, 3 degrees of angular diameter or 7 square degrees. The optical design utilizes aspheric surfaces on M1, M2, the lower surface of L1, and the upper surface of L3. The optics have an f/4.4 beam and a focal length of 8000 mm, which produces a plate scale of 38.8 μm/arcsecond. Even with the broadest band-pass filter which will be used for PS4 (the “w” filter has a band-pass of 400 nm), this optical 12/ 12/ design produces spot diagrams that have RMS radii ( r 2 ) of 6.1 μm on-axis. Since 2r2 = FWHM, this corresponds to on-axis spots with a FWHM of 8.6 μm, or 0.22 arcseconds. This design image quality is maintained across the entire field of view. The spot radii with the w filter rise only to 7.0 μm (FWHM= 0.25 arcseconds) at field angles of 1.5 degrees.

The glass for both the primary and secondary mirrors is ULE from Corning. This low expansion glass is used to minimize the impact of thermal variations in the telescope enclosure. The corrector lenses are all made of high grade fused silica. At the size of the corrector lenses, there are very few choices of optical materials available. BK7 would have been a cheaper alternative to fused silica, but this glass has a coefficient of thermal expansion (CTE) a factor of 10 higher than fused silica, a lower thermal conductivity, and impurity levels that are sometimes high enough to admit significant radioactive components into the glass. Radioactive impurities in the glass needed to be avoided especially for L3 owing to its proximity to the CCD camera which is sensitive to radioactive emissions.

The design of the L1 corrector lens is not an optimum solution for the PS1 optics. The optical performance of the telescope could be improved by decreasing the thickness of L1. However, the L1 design has been chosen because it allows the direct substitution of a unique 3-element Atmospheric Dispersion Corrector (ADC) in its place. One of the main scientific goals of both the PS1 telescope and the PS4 system is the detection of asteroids in our solar system. Surveys for asteroids benefit significantly by spending a large amount of time looking near the asteroid “sweet spots”, which are regions of the sky at dawn or dusk near 70 degrees zenith angles where the orbits of main belt asteroids are concentrated. At these large zenith angles, the addition of an ADC will improve the telescope Point Spread Function (PSF) and decrease the detection limit. It is hoped that the project will be able to eventually find the funds necessary for the ADC optics so that the design of the ADC can be tested on PS1.

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3. The Telescope Structure The PS1 telescope has been designed and fabricated by EOS Technologies. Although the PS1 primary mirror is only 1.8-m in diameter, the telescope has both a very large secondary and a very heavy Cassegrain instrument package. That and the desire to utilize pneumatic supports for the primary mirror drove us to use a telescope fork that was originally designed for a 2.4-m mirror. Figure 2 shows a rendering of the PS1 base, fork, primary mirror cell, gimbal, truss, and the instrumentation package.

Figure 2. The PS1 Telescope

One of the most important telescope structure specifications for the success of the PS1 surveys is that the telescope be capable of slewing a distance of 3 degrees and settling to within and RMS tracking error of 0.1 arcseconds within 5 seconds. Ideally, the telescope would be able to do this over the entire range of operational altitudes. While it currently appears that the telescope will be so capable, the enclosure will limit the time of the telescope step and settle at altitudes above about 60 degrees. Tests of the step and settle time of the telescope will begin in about two weeks from the time of this conference. Pointing and tracking tests of the telescope are currently underway. We will discuss some of the results of those tests later in this paper.

4. Collimation Requirements The wide field of view of the PS1 telescope comes at the obvious expense of the addition of the three refractive corrector optics. This is not a trivial matter. The aggregate cost of the corrector lenses rivals the cost of the primary mirror. But, there are additional costs to the wide field associated with the need to maintain very tight specifications on the collimation of the optics.

Table 1 shows the estimated collimation requirements for each of the telescope optics based on an image budget for the telescope that requires that the image PSF is dominated by the site seeing of approximately 0.8 arcseconds. To do this, the design optics need to deliver spot sizes that are approximately 0.4 arcseconds FWHM near zenith and spot sizes that are approximately 0.5 arcseconds FWHM at zenith angles near 70 degrees. The decenter and despace requirements on all the optics save the filters are tight. These are specifications that include all sources of error from the stack-up of all the structural elements that hold these elements in place.

Table 1. PS1 Collimation Requirements Optic Decenter (μm) Tip & Tilt (arcseconds) Despace (mm) M1 and M2 150 10 0.5 L1 and L2 150 60 0.25 L3 150 215 0.30 Filters 5000 3000 10

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To accomplish and hold the collimation of the telescope the mirror support design incorporates 4-axis control of the primary mirror and 5-axis control of the secondary mirror. The primary mirror is supported axially by 36 pneumatic “Bellofram” actuators. Figure 3 shows the layout of the M1 actuators in the primary mirror cell. In addition to the 36 axial actuators, there are 16 transverse Airpel actuators in a “push-pull” configuration that support the mirror when the telescope points towards the horizon. The Airpel cylinders are made by Airpot Corporation and provide very low stiction support of the mirror. The low stiction allows precise and repeatable control of the lateral actuator forces. The Bellofram actuators were modeled after the design of the APO 3.5-m and the Sloan 2.5-m actuators.

Figure 3. Primary Mirror Cell Actuator Layout (with permission from EOS Technologies)

The primary mirror actuators allow computer control of tilt, tip, piston, and translation of the mirror in the direction of gravity when the telescope is pointed toward the horizon. This direction is designated as the “y” direction for the telescope and is the vertical direction in Figure 3. The primary mirror is only manually adjustable in the “x” direction (the horizontal direction in Figure 3).

The automated y control of the primary mirror was included in the telescope design in order to compensate for flexures in the camera supports. Owing to their size, the camera, the filter mechanism, and the shutter on this telescope weigh about 1430 lbs (650 kg). To maintain excellent focus over the entire field of view, the tilt of the camera focal plane must be maintained perpendicular to the optical axis to within about 10 arcseconds. Despite a stiff design, the mirror cell itself is predicted to flex by approximately 8 arcseconds as the telescope points toward the horizon. The instrument support structure will add to these flexures. It is therefore necessary to compensate for these tilts as the telescope moves in altitude. To make the proper compensations both the primary and secondary must be adjustable in tilt, tip, and y motions as a function of altitude.

The use of pneumatic actuators in the primary mirror cell was chosen because they can be configured to work in unison using a minimal number of control circuits. And, they produce very little heat even when in full support of the mirror. For the tilt, tip, and piston motions only three control loops are required. A forth control loop is required for the lateral supports. The only heat that is dissipated in the mirror cell is from the operation of the high speed control valves that regulate the pressure to the actuators. Most of the heat for the mirror control is generated and spent three floors below the telescope where the air compressor for the system is located. The use of pneumatic actuators is also relatively safe for the glass. The geometry of the actuators themselves limits the amount of force that can be placed on the mirror.

The downside of this type of mirror support system is that it is very sensitive to any contamination in the air lines. In order to maximize the high frequency response of the system, the air lines must be kept small and the runs for the lines need to be kept as short as possible and nearly equal in length between actuators. The small size of the lines (1/16”) makes them susceptible to clogging by contaminants in the lines. The primary source of

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contamination is from water vapor in the air itself. It is therefore necessary to include a regenerative drying system with the air compressor.

In a Cassegrain telescope spherical and coma aberrations can be largely controlled by piston, tilt, tip, and decenters of the primary and secondary mirrors. Astigmatism is harder to control by these methods largely because these degrees of freedom are already being used to correct for the other aberrations. In a telescope with a conventional field of view this is often acceptable because astigmatism grows with field angle. On-axis it is often small enough to ignore. But with the PS1 telescope’s wide field of view, even small astigmatic errors in the wavefront will have a serious effect on the outer portions of the telescope’s field of view. It was therefore assumed in the specifications of the telescope that an independent way of correcting for astigmatism in the telescope wavefront was required.

Twelve of the 36 axial supports in the outer ring of actuators were configured to act as an astigmatism correction system. These actuators have 12 independent control loops that can apply up to about 14.5 lbs (65 N) of differential force to the perimeter of the mirror. This is calculated to be sufficient to induce or correct for approximately 2 waves of astigmatism in the wavefront. Load cells on each of these 12 actuators measure a differential force between them and the average force being placed on the mirror by the rest of the support actuators. Tests of this astigmatism correction system are currently being done.

Figure 4. M2x on the PS1 Truss

Figure 4 shows the PS1 secondary mirror on the telescope. The secondary is actuated in 6 axes by an off the shelf hexapod which is made by Physik Instrumente (PI). Five of these degrees of freedom will be commonly used to keep the telescope in collimation and focus during observations. The 6th degree of freedom, rotation of the secondary mirror, is only of marginal use, but comes as a natural result of the hexapod. The hexapod provides approximately 5 mm of piston while maintaining the ability to tilt and tip the mirror by about a degree. The hexapod motion is rapid and precise. It is able to move the mirror through the full range of piston in less than a minute and can position the mirror to an accuracy of about 2 μm.

The axial support of the secondary mirror has been accomplished by the use of a conventional “whiffle tree” structure. Six whiffle trees provide an 18 point support system for the secondary. This large number of support points is required owing to the large size of the secondary mirror and to its light weighting. These whiffle trees are attached to the mirror by use of flexures that provide several degrees of motion in two dimensions (tilt and tip). The lateral support of the secondary mirror is accomplished by a counterweight system behind each of the whiffle tree support points. Six of these counterweights can be seen in Figure 4. They are the “bars” that appear vertically behind the mirror perimeter in this figure. In early tests of the telescope pointing and tracking it was discovered that a dampening system is required on these counterweights. During slews these counterweights can become excited, causing unwanted vibration of the secondary which can persist for several minutes. Early attempts at applying simple foam dampening system seem to be effective at solving this problem.

One significant drawback of the use of the hexapod for the secondary mirror actuation was the limitation that this mechanism put on the weight of the secondary mirror. In order to fit both the secondary mirror and the secondary

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mirror counterweight system onto the hexapod, it was necessary to lightweight the secondary mirror itself. 42% of the mirror weight had to be removed. This was accomplished by cutting out two rings of material into the back of the mirror and the center hole. Compared to many light-weighting efforts, this is a modest reduction in weight. But it was mandatory for the use of the PI hexapod. The light-weighted secondary mirror weighs 129.9 lbs. The entire structure supported by the hexapod including counterweights, flexures, and whiffles trees weighs 266.8 lbs.

5. The Upper Cassegrain Core As shown in Figure 1, there are three corrector lenses in the PS1 optical layout. The third corrector lens (L3) forms the window to the CCD dewar and is therefore more properly discussed in the context of the PS1 camera. The point that we must make about L3 is that it has been calculated that when the camera cooling is on, the upper surface of L3 will drop below freezing. This requires the instrument package to provide a dry air environment to this lens surface to prevent frost from forming on this optic and has a large impact on the design of the instrument package that fits above the camera.

Figure 5. The PS1 Cassegrain Core

The first and second corrector lenses (L1 and L2) are supported in a structure that fits above the instrument rotator that is known as the Upper Cassegrain Core (UCC). Figure 5 shows a cut-away of the PS1 Cassegrain Core. The Primary Mirror Assembly (PMA) is the structure that holds the primary mirror, the primary mirror actuators and the instrument rotator. The red part in this figure is the Upper Cassegrain Core Support Structure. It supports two cylindrical lens cells for L1 (in yellow) and L2 (in green) that bolt on above and below it. The UCC Support Structure holds the L1 and L2 lens cells in place with 4 legs that have been designed to flex in a way that partially compensates for flexures in the PMA as a function of altitude. The turquoise section seen just below L2 is a neoprene membrane that helps to form the dry air chamber that extends from the lower surface of L2 to the upper surface of L3 (in white). The UCC Support Structure is attached to the fixed section of the instrument rotator. It therefore does not rotate as the instrument rotator moves.

The L1 and L2 lens cells are cylindrical steel structures that surround and support the corrector lenses though the use of a continuous ring of Corning 3112 RTV. The RTV rings are approximately 3 mm thick in the axial direction and about 50 mm thick parallel to the optical axis. They are designed to support the lenses without inducing any significant thermal stresses into the glasses. FEA calculations show that the design limits the stresses in the glass to about 50 psi over a temperature range of 40 C.

6. The PS1 Instrumentation As a specialized survey telescope, the PS1 will have a fixed instrument package. This package consists of the camera, a filter mechanism, and a shutter. This entire instrument package is referred to as the Lower Cassegrain Core (LCC).

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In Figure 5 the LCC Support Structure is shown in pink. This section is attached to the moving part of the instrument rotator. The pink structure holds the filter mechanism, the shutter, and the Giga-Pixel Camera (GPC). The LCC Support has been designed to allow the removal and servicing of the filter mechanism without detaching the camera from the telescope. The shutter is attached directly to the filter mechanism. They are removed from the telescope as a single unit and then disassemble. Inflatable seals are utilized to fill up the gaps needed for the installation and removal of the filter mechanism. The details of the design and installation of the filter mechanism are covered in another paper at this conference.

The shutter for the GPC was built at the University of Bonn and has a clear aperture of 480 mm square. It utilizes a dual blade design that is stepper motor driven. It is capable of providing accurate and uniform exposures over the entire GPC focal plane for exposures as short as 0.1 seconds. The accuracy and uniformity of exposure duration was specified to be better than 0.5% over the entire focal plane and for the full range of shutter exposures. This is the largest astronomical quality shutter that has been built to date. Full testing of this shutter will not begin until the GPC camera has been installed on the telescope. However, the shutter and the filter mechanism should be installed in the telescope in about 6 weeks.

7. The Telescope Baffles Figure 6 shows the PS1 baffles. These baffles have not yet been installed on the telescope, but should be within the next month. The PS1 baffle system is a three baffle system that was modeled after the Sloan 2.5-m baffle system. The use of the three baffle system as opposed to a two baffle solution allows greater system throughput at the expense of a very slight increase in the diffraction profile of the telescope point spread function. This three baffle design causes only 37.8% obscuration compared to the 46.7% that an equivalent 2-baffle design would have caused. The middle baffle in this design is supported by thin “piano wire” that runs from the middle of the truss to a strengthening ring in the middle of the central baffle. These wires are aligned with the secondary vanes, so on-axis they do not contribute to diffraction in the telescope. But, an increase in the off-axis diffraction is unavoidable with this design. Keeping the size of the support wires small keeps this increase to a very acceptable minimum. There is very little (11%) variation in the obscuration as a function of field angle with this baffle design. On-axis the obscuration is only 33.7% while the 2-baffle on-axis obscuration is 42.3%.

Figure 6. The PS1 Baffles.

8. The Current State of the Telescope Optics Most of the telescope optics have yet to be installed. The primary mirror is the only optic that is completely finished and in place. The polishing for this optic was done by Rayleigh Optical Corp. in Maryland (ROC). The polishing specification for both the primary and secondary mirrors was given in terms of a structure function which allows errors in the optical figure to rise with the large scale limitations that the atmosphere places on the optics. Figure 7 shows the specified surface errors (dashed blue line) along with the final polish errors on the

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mirror (red line) and the surface errors present in the polishing run just prior to mirror completion. The project is very satisfied with the fabrication of this mirror. ROC successfully met all of our specifications which were considered to be tight. The primary mirror was given a protected aluminum coating by the EMF Corporation in Newark.

Figure 7. The Primary Mirror Polish

Despite appearances, the fabrication of the secondary mirror is not yet complete. An accident occurred during the fabrication of the secondary mirror which has delayed its final installation into the telescope. While drilling one of the holes in the back of the secondary which are used to hold the secondary support flexures, a piece of the secondary mirror’s front face broke off and was pushed out. The polisher immediately purchased a new blank at their own expense as a replacement for the broken secondary. Facing this delay, the project decided to utilize the broken secondary, which we have labeled M2x by having the broken plug cemented back into its hole and by having the secondary refigured so that it could be used for on-axis imaging without the presence of the corrector optics.

Figure 8. M2x Profilometry

Since the need for M2x was immediate and temporary, we chose not to have a full polish put on the glass, but instead to stop the process at the point were profilometry measurements could no longer be used reliably to

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measure figure error. Figure 8 shows the current figure errors on M2x as measured by profilometry. As shown in the figure, these figure errors were fit by a model. This model was then applied in ray tracing calculations in order to estimate their effects on the telescope PSF in the absence of other errors in the telescope. Figure 9 shows a comparison of the measured and expected telescope PSFs based on the M2x profilometry shown in Figure 8. The actual telescope image of a star is shown on the left and the ray tracing calculations are shown on the right. The agreement is excellent.

Figure 9. A Comparison of Measured and Theoretical PSFs

The current telescope PSF has a core width of about 3-4 arcseconds, but the wings of the distribution are approximately 60 arcsecond in width. This is sufficient to allow us to do tracking, pointing, and step and settle tests of the telescope while we wait for the delivery of the final telescope secondary and the corrector optics. The secondary is currently expected to be delivered near the end of this year.

The delivery of the corrector optics and their support cells are consistent with the telescope having a full compliment of optics by the end of this year. All three of the corrector optics have been finished by the polishers and are now at coating facilities. We expect to have all three optics in hand to start their installation into the lens cells by the end of October. The lens cells themselves should be done by the end of September.

The filter complement for the PS1 telescope is slightly different than that chosen for the full PS4 array. In particular, the PS1 does not yet plan on utilizing a w filter, although this may happen in the future. All but one of the PS1 filters have already been delivered. Figure 10 shows a photograph of the PS1 i band filter along with measurements of its transmission as a function of radius on the filter. The solid red and blue lines in the transmission plot show the filter specification limits. Similar measurements are available for all of the delivered filters (g, i, r, and z) along with measurements of the filter transmission as a function of azimuth. These filters are scheduled to be potted into their frames in about 6 weeks.

Figure 10. The PS1 i Band Filter

9. Pointing Test Measurements Some of the first measurements that have been done on the telescope are pointing tests. For these tests a pointing model of the telescope is first produced using a series of “model stars” which are scattered uniformly across the

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sky. Then, using this model, the telescope is commanded to point to a number of “test stars” which are previously unmeasured stars. The differences between where the telescope pointed using the model and the actual test star positions are measured. During the development of the pointing model both pre-fit and post-fit measurements of the star positions are measured and an RMS post-fit residual is given which is a measure of how well the pointing model describes the data on which the model was made. If the model stars are truly representative of all stars in the sky, then the post-fit residual is also an indication of how well the telescope should point to a random star in the sky. The measurement of the test stars is a test of that assumption.

Figure 11. PS1 Pointing Model Residuals

Figure 11 shows the pre- and post-fit residuals of a 160 star mount model that was taken on 30 August 2006. The model fit included 11 terms. The post-fit residuals show an RMS scatter of only 2.47 arcseconds. After this model was applied, the pointing residuals to 70 test stars were measured. The scatter in the test star positions was found to have an RMS value of 2.64 arcseconds, which we consider to be in excellent agreement with the post-fit prediction. It is therefore clear that the model is an accurate description of the telescope pointing. The graphs in Figure 11 indicate that the post-fit residuals are fairly random with the exception of the Elevation Residual vs. Azimuth graph (lower left in the figure). In this case, it is clear that the model would benefit by the addition of an elevation term that varies as sin(3*Az). This term will be added in the future, but the pointing of the telescope is already far in excess of what we require that this improvement is not a high priority.

10. Acknowledgements The Pan-STARRS project gratefully acknowledges the truly significant and outstanding contributions that many at EOS Technologies, Inc. have had in the development of this instrumentation. In particular, we would like to thank Kevin Harris, Kerry Gonzales, Chris Lambert, Aaron Evers, and Britt Kayner. Without your help and dedication we would be lost!

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Filter Mounting and Mechanism Design for the Pan-STARRS PS1 Prototype Telescope System

Alan J. Ryan Institute for Astronomy Jeffrey Morgan Institute for Astronomy Walter Siegmund Institute for Astronomy Carole Hude Institute for Astronomy The Pan-STARRS Team

ABSTRACT

The Pan-STARRS PS1 telescope is a 1.8m Cassegrain telescope with a 7 square degree field of view and a 1.4 billion pixel CCD camera. The required clear aperture at the filters is 496mm in diameter, and therefore the filters needed are quite large. The Pan-STARRS filter complement consists of six octagonal shaped filters that have a distance between the flats of 514mm and a thickness of 10mm.

The automated mechanism that will move the filters needs to fit into a small area. A filter wheel would be prohibitively large, so the mechanism will consist of three layers with two athermally mounted filters that slide on each layer. Each layer will be identical to the other two to provide interchangeability and commonality in manufacturing. The layers will be stacked and held together with top and bottom cover plates to form a rigid structure. The shutter will be mounted to the bottom of the mechanism and they will be installed as one unit. A separate structure will be utilized to clamp the mechanism to the telescope cassegrain core registration points. This installation system will allow the mechanism to be isolated from other structural loads and be easily removed without affecting the camera.

1. INTRODUCTION

A sliding mechanism was chosen over the classical filter wheel and cassette type mechanism for a couple of reasons. In a filter wheel design, the size of the filters would create a large footprint that would be difficult to mount on a four-telescope system that is proposed for PS4. It would also create a large unbalance resulting in more counterweights and larger telescope bearings. The cassette type mechanism has a smaller footprint, but it is a more complicated mechanism and susceptible to numerous problems. This system also has an unbalance since the filters will be located in a cassette on one side. The layered design is similar to the cassette system since the filters will slide into the aperture, but there will be no cassette nor vertical movement of the filters. There is enough clearance lengthwise to have two filters on each layer so the overall thickness can be kept to a minimum. Three layers will be able to satisfy the six filter requirement. Each layer will be identical and will provide commonality in manufacturing and maintenance support.

The filters do not need to be precisely positioned in the aperture since they are far from the focus of the camera. Also, the flexure of the filters under their own weight does not cause any appreciable degradation to the Point Spread Function (PSF). The filters are very costly so protecting them from harm is a priority. They will be potted using an elastomer that will help reduce any stresses from thermal expansion and shock loads. Collisions between filters will be controlled through software and bumpers have been added to the filter frames as an extra safety measure. Motion control measures have been added to create a smooth and slow filter movement. Humidity will degrade the coatings on the filter so a nitrogen purge is incorporated with a slight positive pressure. Pneumatic cylinders were chosen over equipment with electrical motors because excessive internal heat would be detrimental to the optical quality and negate the gains from the future Atmospheric Dispersion Corrector (ADC).

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It would be very useful to have the mechanism removable without affecting the camera. The reasons are two fold. Maintenance can be performed quickly since only one component needs to be removed and the mechanism will not have to carry the load of the camera. A separate structure called the Lower Cassegrain Core (LCC) will be used to mount the filter mechanism, shutter and camera. The design will minimize flexures from the weight of each component and will mount directly to the telescope rotator. Some of the controls and cables for the filter mechanism will also be mounted to this structure.

2. OPTICAL CONSTRAINTS ON FILTERS

The telescope Point Spread Function (PSF) is not extremely sensitive to the location of the filters. Fig. 1 shows the optical layout of PS1 with baffles. Filters are labeled F1, F2, and F3. The three layer design implies that there are essentially no despace or decenter requirements on the filters. The tilt requirements on the filters are only ± 50 arcminutes and only changes the PSF by less than 0.3 percent.

Fig. 1. PS1 optical layout with baffles

A 10 mm (0.39 inches) thick fused silica filter has less than 5 microns distortion under self-gravity when pointing towards zenith (Fig. 2) [1]. The red arrow indicates the direction of gravity. None of these distortions from gravity are sufficient to have any impact on the telescope PSF. The largest increases are only six percent. At some fields of view, the distortions actually improve the telescope PSF.

Fig. 2. FEA analysis of a 10 mm thick filter pointing at zenith [1]

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The contrast of dust motes decrease rapidly as the distance from the focal plane increases. A theoretical dust mote of 1 mm in diameter at 20 mm from the focal plane has a shadow diameter of 8 mm (0.3 inches) and contrast of 8 percent (Fig. 3). The minimum distance that a dust mote can be from the focal plane is at the top surface of the dewar window which is 240 mm (9.4 inches). The closest filter is 448 mm (17.6 inches) from the focal plane and by scaling an f/4.4 beam for a 1 mm diameter sphere, the predicted mote shadow diameter is 202 mm (7.95 inches) and contrasts less than 0.01 percent. Therefore, dust motes on any optical surface will only decrease the general throughput by a very small amount and will not put structure in the flat fields and images.

Fig. 3. Dust mote from a 1mm sphere at 20mm above the focal plane

3. FILTER POTTING

To create an athermally mounted filter, the fused silica filters are potted into aluminum filter frames using Dow Corning 3112 RTV silicone rubber. This is two part elastomer provides a firm and flexible mechanical joint that cures at room temperature. The proper procedure for potting requires the filter frame and filter surfaces to be clean, dry and oil free before the Dow Corning 1201 primer is applied. The primer increases adhesion in bonding surfaces made of metal, glass and ceramic. After the primer is cured, there is a window of an hour for the potting of the RTV joint before the primer needs to be reapplied.

The potting joint size has been adjusted to compensate for the Coefficient of Thermal Expansion (CTE) differences between the glass and the aluminum frames. The stress on the filter from the RTV joint was analyzed using Finite Element Analysis (FEA). Using a 34 degree Celsius change in temperature, the 5mm thick RTV joint applied a stress of 0.8 MPa (120 psi) on the filter in the clear aperture (Fig. 4)[2]. This is eight times less than the safe maximum stress on the glass, 6.9 MPa (1000 psi).

Fig. 4. Local peak stresses in filter glass [2]

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The potting of the filter will require fixtures to keep the potting material in the correct area (Fig. 5). The entire outer perimeter of the filter will be lined on the upper and lower surfaces with a seal made of latex. Preliminary tests have shown that latex is the best material since the potting material does not adhere to it after curing. Silicone compounds have been found to be unfavorable because of sticking. It is undesirable to use a mold release because of the possibility of migration and contamination on the filter surface. The upper and lower seals will first be cast in a molding fixture and one half of each of these molds will be used as supports during the potting of the filter. A slight vacuum between the seal supports and the filter will ensure that a seal is maintained. The filter frame will be positioned upside down during the potting to utilize a flat reference surface. Alignment holes in the seal supports will correctly align the upper and lower seals to each other. A reference edge on the upper seal support will register both seals to the filter frame. By using jacking screws and temporary alignment blocks, the filter can be correctly positioned in the filter frame. The potting material will be injected from the upper seal location into the joint cavity. A mechanical lock between the filter frame and the potting material is achieved by machining in a groove on the inside surface of the filter frame. Additionally, the filter is mechanically locked to the potting material by potting the complete filter section including the bevel on the top and bottom surfaces.

Upper Seal Support

Filter Frame Upper Cast Seal

Filter

Lower Cast Seal

RTV Lower Seal Support

Fig. 5. Cross-section of potting fixture

4. FILTER MECHANISM DESIGN

All of the major components of the filter mechanism were made out of MIC-6 cast aluminum. These included the layer frames, top and bottom covers and the six filter frames. Each piece was rough cut using a water jet to reduce machining time. Fig. 6 shows the complete filter mechanism with the shutter attached to the bottom. The top cover plate is basically a cover plate, but also has the pads for the registration of the top feet on the LCC. The bottom plate is an adapter to mount the shutter that was fabricated by the University of Bonn in Germany. The filter mechanism and shutter will be removed from the telescope as one unit. All sections are fitted with an o-ring groove to make a sealed unit. Each section is also fitted with an alignment pin and mating half to aid in stacking. Around the perimeter of the layer frames, there are thirty-four standoffs that attach to the top and bottom cover plates. The layer frames are clamped together to form a rigid assembly. The total weight of the filter mechanism alone is 170 kg (375 lbs) and with the shutter attached it weighs 202 kg (445 lbs).

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Fig. 6. Filter mechanism with shutter

The overall height of each layer, 31.8mm (1.25 inches), is driven by the cross-section size of the pneumatic cylinder. The overall diameter of the Bimba C-09 model cylinder is 28.4mm (1.12 inches) and has a power factor of 0.9. At 100 psig air pressure, this cylinder can produce 90 pounds of force in both extending and retracting motions. The nose mounting of the cylinder makes assembly easy and an alignment coupler on the rod reduces the required mounting precision (Fig. 7). A bracket at the other end of the cylinder supports the cylinder in case of incidental contact. The cylinder is located in a separate compartment to reduce the possibility of contamination of the filters. A feature to protect the cylinder also helps protect the filters. An adjustable internal cushion starts to slow the rod speed at 19 mm (0.75 inches) from both ends. Lubrication of the cylinder is not needed for the entire life of the cylinder given the low number of cycles required for filter changing.

Filter Frame Shutter Filter Cylinder Valve

Filter Cylinder Standoffs Flanged Wheel Registration Mount

Fig. 7. Filter Mechanism with top cover plate removed

The linear motion of the filter frames is accomplished using profile guide rails and recirculating ball carriages (Fig. 8). The THK model SHW12HRM guide rails have an overall height of 12 mm (0.47 inches) and produce a high- precision and smooth linear motion. The tolerance over the entire travel of the filter, 565 mm (22.25 inches) is less than 15 microns (0.0006 inches). Three carriages will be used in a triangle pattern to support the filter frame. Two of the carriages are mounted on one rail and will hold the filter frame in all degrees of freedom except three, linear motion for the filter frame, rotation around that direction (rail) and rotation perpendicular to the surface of the filter (yaw). The third carriage is located on the opposite end of the filter frame on a second rail and will control rotation

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around the rail. It will be loosely mounted with belleville washers so that it will float along the rail. A rubber bumper is attached to the filter frame and will absorb any shock that may occur from a collision with the other filter frame on that layer. The filter frame also has eight clips attached to the top and bottom surfaces as a safety measure to keep the filter from falling in the unlikely event that the RTV bond fails. The total weight of the moving filter assembly is 10 kg (22 lbs) and will be over four times less than the capacity of the cylinder at 100 psig air pressure.

Bimba Cylinder

O-ring Groove

Filter Frame

THK Ball Slide

Clips

Filter

Fig. 8. Cross-section of filter layer

The flexure of the carriage on the rail will dominate the repeatability of the system. The worst case scenario is when the telescope is pointing at the horizon. The third carriage is designed to float and the other two carriages must carry that cantilevered load (Fig.9). Each carriage has a deflection of 7E-4 radians with this load. The load of a 20 mm thick filter and frame, 15 kg (33 lbs) was analyzed with a distance of 476 mm (18.7 inches) between carriages. The movement of each carriage translated into a movement of 39 microns (0.002 inches) at the edge of the clear aperture near the third carriage. This value is well within the requirement of less than 500 microns (0.020 inches).

Fig. 9. Repeatability of a 20 mm thick filter and frame. Telescope is pointing at the horizon.

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There are two limit switches for each filter and one is attached to each hard stop (Fig. 10). These hard stops are located at the retracted and extended positions of the cylinder. The hard stop and limit switch at the retracted position is to locate the filter when it is in the aperture. The extended position is for the home location. At each position, the filter will be held in place by the force of the cylinder. Air pressure will be in the cylinder at all times.

Filter Frame Hard Stop

Limit Switch

Layer Frame Alignment Pin

Alignment Coupler

Fig. 10. Hard stop and limit switch

5. INSTALLATION

The most useful feature of the filter mechanism is its removability. The camera will not be affected when it is time to perform maintenance or to correct a problem on the filter mechanism or shutter. The balcony crane is capable of lifting the filter mechanism and shutter along with its lift support structure in and out of the dome. For installation, the filter mechanism is placed on a modified lift cart made by Econo Lift (Fig. 11). The lift cart had been fitted with vee-type wheels that allow it to run on the angle bars on the observatory floor. The lift cart can also be used to install and remove the Upper Cassegrain Core (corrector lens L1 and L2) and the LCC.

The flanged wheels under the filter mechanism allow it to roll into the LCC. The lift support structure has some compliance built into the design that allows it to move independently from the lift cart. This allows the rails from the cart to align with the rails on the installation lift. Just before the filter mechanism comes into contact with the registration columns on the LCC, two spring loaded pawls are automatically activated under the wheels. The pawls are located on the installation lift rails and are a safety measure that keeps the filter mechanism from rolling out of the LCC at any time. During removal, these pawls will have to be manually released. Over center clamps on the registration columns hold the filter mechanism in place.

The registration columns locate the filter mechanism back to the same position every time. On one column there is a vee shape feature and on the other there is a flat. A third locating position is at the top feet on the LCC that contacts the top cover plate when the installation lift is raised using pneumatic cylinders from Bimba. Flow restrictors are used to provide a slow and smooth movement over the 2 mm (0.08 inches) distance. Over center clamps are also used on the installation lift as a secondary attachment method in the event that air pressure is lost. With these three hardened features the filter mechanism is constrained in all directions.

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Installation Lift LCC Bimba Cylinder

Filter Mechanism

Camera

Econo Lift Cart

Fig. 11. Filter mechanism installation using lift cart

Dry nitrogen will be used to purge the interior of the filter mechanism and the shutter. Problems can occur if the humidity is too high. The coatings on the filter are adversely affected and frost can occur on the dewar window, L3. All sections of the filter mechanism are sealed with an o-ring including the shutter, but when the filter mechanism is installed there are gaps that allow for rolling clearance. Two rubber inflatable seals made by Pawling Corporation are used to seal these gaps. One is located next to L3 and will inflate to seal against the shutter surface. The other is located on the LCC above the top cover plate. This will seal the filter mechanism surface. The area that is purged with dry nitrogen is from the L3 dewar window to the corrector lense, L2 which is located above the rotator. A slight positive pressure of less than 0.1 psig helps keep the environment inside the filter mechanism controlled. A specially made pressure relief valve is attached on the LCC and opens at the set pressure of less than 0.1 psig to ensure that there is only a slight extra load on the dewar window.

6. PNEUMATIC SYSTEM

A double solenoid valve made be Parker Hannifin controls the filter cylinders (Fig. 12). It is a four-port and two- position valve that either extends or retracts the cylinder. By adding flow restrictors to the inlet and outlet ports of the cylinder, the velocity of the filters can be controlled in a smooth motion. The flow restrictors are made by Mott Corporation and are made of a porous metal that is inserted into a standard Swagelok fitting. The parameters of the flow restrictors are a flow rate of 15 lpm and a supply pressure of 100 psig. At this rate, the cylinders will travel the complete stroke length in 14.5 seconds up and 8.5 seconds down. The orientation is taken in the vertical position, as this will be the largest and smallest load on the cylinder. Average velocities would be 1.5 inches per second and 2.6 inches per second, respectively. A two-port and two position valve will control the master air supply to the filter mechanism. This solenoid valve is a normally closed type and will stop all cylinder movement during a power failure or if other unsafe conditions occur.

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Fig. 12 Pneumatic diagram for the filter cylinder

7. CONTROL SYSTEM

A programmable controller is used to control and monitor the filter mechanism functions. The Z World BL2100 Smartcat is a high-performance single-board computer in a compact package. The program is written in Dynamic C and will monitor eighteen digital inputs, five analog inputs and control thirteen digital outputs. The digital inputs include the twelve limit switches in addition to six identification bits for the three layers. The filter layer identity bits are simply pins that are shorted to ground or left open. The analog inputs include the ambient internal temperature, ambient internal humidity, air supply pressure, inflatable seal pressure and dry nitrogen purge pressure. The digital outputs include the solenoids for each filter cylinder valve and the master air supply valve.

Only one filter from each layer can be in the aperture at the same time. In order to avoid collisions, partner filters are always checked and moved out (clearing move) before the desired filter is moved into the aperture (final move). If the clearing move times out, the system re-commands the clearing move. If the final move times out, the system reports an error, but does not take any corrective action because there is no danger of collision. The user will have to determine the appropriate course of action.

When the controller powers on, it assumes the worst as far as the interlocks are concerned. This state persists until the controller has taken enough measurements to determine that the interlock conditions are acceptable. As soon as that happens, the controller turns on the master air supply valve and sends all filters out of the aperture. It does this in case any solenoids have been replaced and are in a state that could drive filters into collision. There are two interlocks in the system. The humidity threshold will be set around 3 percent relative humidity. When the humidity rises above this threshold, the filters will move out to the home position and are diabled until the humidity returns to and acceptable level. The second interlock is the air supply pressure. When the air supply pressure drops below 50 psig, the main air supply valve will close and all filter movements will cease until the pressure becomes acceptable.

8. CURRENT STATUS

The fabrication of the filter mechanism, LCC and installation lift system has been completed by the University of Washington -Physics machine shop. The LCC and installation lift system are currently at the PS1 facility atop Haleakala in Maui. The filter mechanism is currently still at the machine shop in Seattle and is being tested with the controller and software. The plumbing and wiring for one layer is complete and a successful test of the controller hardware and software was performed. The next step is to perform a test on multiple layers. The filter potting

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procedure is being currently tested. The potting of filters will commence after successful performance of the procedure. The installation cart and removal system has been designed and is awaiting fabrication.

9. REFERENCES

1. J. Morgan, PS1 Filter Thickness Specification, University of Hawaii, Honolulu, Hawaii, 2004. 2. C. Hude, The PS1 Filter Mount Design;PSDC-300-021-00, University of Hawaii, Honolulu, Hawaii, 2005

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The Pan-STARRS Imaging Sky Probe

B.R. Granett, K.C. Chambers, E.A. Magnier, K. Fletcher and the Pan-STARRS Project Team University of Hawai’i Institute for Astronomy

Abstract Photometric performance is limited by systematic errors introduced by uncertainty in the trans- parency of the Earth’s atmosphere. Precision photometry required for programs including the character- ization of supernovae and photometric redshifts can especially benefit from independent knowledge of the atmospheric transmission. In particular, multicolor near-simultaneous observations are able to constrain the impact of aerosol, dust, and water vapor on the atmospheric transparency. To measure the atmospheric transmission function in real time, the Pan-STARRS PS1 prototype system will utilize auxiliary instruments: imaging, spectroscopic, and potentially u-band sky probes. These instruments will act synchronously with the primary survey camera to provide calibration data for the Image Processing Pipeline (IPP), and will also generate significant all-sky science data. For the Imaging Sky Probe (ISP), the broad-band transparency of the atmosphere will be measured in five survey bands matched to the Pan-STARRS grizy filter set. Atmospheric absorption and emission models will then be generated to produce a best-fit characterization of the atmosphere at the time of observation. Armed with measurements of the atmospheric transmission from the sky probe instruments, the Pan- STARRS IPP can precisely solve for the intrinsic source photometry. Such a system is unprecedented, and the Pan-STARRS PS1 system will act as the test bed for future development. The intent of this talk is to describe in detail the design and testing of the ISP which is the first Pan-STARRS sky probe, and we will present ISP images collected during the early commissioning of PS1.

1 A photometric probe for Pan-STARRS

The success of an all sky survey project such as Pan-STARRS (Panoramic Survey Telescope and Rapid Response System, [1]) depends upon its ability to produce uniform, high quality data over many years. Astronomical photometry is plagued by systematic errors introduced not only by the poorly characterized transmission properties of the telescope, but by the dynamic transmission properties of Earth’s atmosphere as well [2]. For Pan-STARRS we have developed the Imaging Sky Probe, a prototype five color imaging camera, to measure the wavelength dependent atmospheric attenuation in conjunction with the survey. The modest camera is designed to image each Pan-STARRS field in five survey bandpasses (grizy) in synchrony with the survey telescope to provide photometric calibration. The Sky Probe’s 9 square degree field of view encompasses the entire 7 square degree survey field, hence, it will provide a real-time measurement of the atmospheric attenuation in each bandpass over the entire survey field. The Imaging Sky Probe will begin measuring standard fields, and, over the first year of survey operations, build up an all-sky, internal five color photometric catalog to 17th magnitude. As of this conference, we have only begun characterizing the instrument. Here we introduce the design of the Imaging Sky Probe and its basic performance characteristics.

2 Motivations – atmospheric extinction

The attenuation of visible light in the atmosphere is the result of a combination of aerosol and Rayleigh scattering and line absorption by molecular species, namely water and oxygen (see fig. 1). Additionally, emission in the optical is produced by water vapor, hydroxyl and forbidden transitions, most prominently of atomic oxygen. These contributions vary over a range of time scales from minutes with local weather conditions, pressure and humidity, to days and months with seasonal changes. Atmospheric attenuation introduces fluctuations in the zero point magnitude, but more seriously, it produces color dependent shifts that vary with the shape of the source spectral energy distribution (SED). Systematic errors in photometry arise because the attenuation properties of the atmosphere vary over a wide bandpass and reshape the total telescope detection efficiency function. Consider observations made of a star as it sets, the effective bandpass becomes redder as airmass increases and blue light is increasingly filtered out. These second order effects depend on the SED of the source and the shape of the instrument

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Figure 1: The Imaging Sky Probe has five filters, grizy, to monitor the atmospheric attenuation. The atmospheric attenuation is a smooth function of wavelength imprinted with deep atomic and molecular line absorption. The smooth trend is the result of Rayleigh and aerosol scattering. The dominant lines are produced by oxygen and water. Most bandpasses are placed to avoid strong atmospheric lines. The i band is an exception: the 7600A˚ molecular oxygen line falls in the middle of the band.

response, and are not easily comparable between observatories, or even between observations taken over a single night as photometric conditions change. Color transformations are typically used to convert between photometric systems, but these color terms depend on the SED and are only good to the few percent level at best. Our goal is to precisely measure the atmospheric attenuation as a function of wavelength in real-time. The Imaging Sky Probe will be the first step by providing broad band photometric measurements. In addition, we are planning a dispersive instrument to measure the attenuation function (and sky emission spectrum) to moderate spectral resolution. With additional atmosphere modeling techniques in development, these observations will allow a precise characterization of the instantaneous atmosphere that the Pan-STARRS telescope is looking through. [3, 4].

3 The Imaging Sky Probe

The Imaging Sky Probe is designed as an off-the-shelf instrument. Only the filter wheel system and enclosure were designed in-house. It runs autonomously with an on-board mini computer, and is fully integrated into the observatory control system (OTIS). The components are listed in table 2. The Sky Probe mates to a frame bolted to the PS1 telescope mirror cell. It floats on a ball joint at the top of the frame allowing fine adjustments of pitch and yaw, before being bolted into place, see fig 4.

3.1 Camera The Sky Probe is equipped with an Apogee 4Mpixel CCD camera with a commercial 500mm focal length F/4 Nikon camera lens. The field of view is 9 square degrees with a 5 arc second pixel size. The camera has an integrated thermoelectric cooler which we have modified for glycol cooling. We have carried out basic linearity and gain tests (fig. 2). The device characteristics are summarized in table 2. We are in the initial stages of characterizing the camera performance. Preliminary zero points are listed in table 1. Further work must be completed to characterize the reproducibility of the shutter. The shutter is an iris integrated into the Apogee CCD. We find that the nonuniform illumination effects dominate over flat field artifacts for exposure times under 0.5 seconds. In addition, the Nikon lens shows astigmatism that is strongest in g band and improves towards the red. The impact on photometry has yet to be determined.

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Table 1: Initial Imaging Sky Probe zero points

g 19 r 19 i 18.5 z 17.5 y 14

3.2 Focus The filter set is not par-focal. We use a stepper motor to control the focus ring on the lens. High and low limit switches on the focus ring are enforced with magnets and hall switches.

3.3 Filter wheel The filter wheel was designed in-house to position the 140 mm diameter filters in front of the camera lens. It carries five survey filters: grizy. The wheel was designed in a folded five petal form to conserve space. It is driven by an Animatics Smart Motor which includes an integrated position encoder and brake to hold the wheel in position. It is homed by a single hall switch. We experience systematic offsets in the filter position on the order of 0.1% due to stretch of the drive train, that appear when the wheel is driven over multiple revolutions. The periodic effect may be avoided by simply limiting the travel of the wheel to one revolution. The positioning error due to the servo motor is a factor of 10 smaller, or roughly 1µm. Additionally, because the filter wheel is conical, there is a rotational offset produced by a transverse error. The error in the orientation of the normal vector of the filter is 0.04 degrees.

3.4 Cooling Glycol cooling is used throughout the Sky Probe enclosure to draw heat away from the telescope mirror cell. We use three RTD temperature probes to measure the temperature in the enclosure.

4 Sky Probe science

The Imaging Sky Probe will not only be an excellent monitor of atmospheric attenuation, but has great potential in providing science results on its own. Over the survey life time, the camera will generate an independent five color all-sky survey. The large pixel size of the instrument makes it well suited for a low surface brightness survey. In addition, It will provide variability data for bright sources in cadence with the Pan-STARRS survey.

References

[1] Kaiser, N., & Pan-STARRS Team 2005, American Astronomical Society Meeting Abstracts, 207, [2] Stubbs, C. W., & Tonry, J. L., Toward 1% Photometry: End-to-end Calibration of Astronomical Tele- scopes and Detectors, Astrophysical Journal, 646, 1436, 2006. [3] Granett, B. R., Chambers, K. C., & Magnier, E. 2005, American Astronomical Society Meeting Abstracts, 207, [4] Granett, B. R., DeRose, K., Chambers, K. C. in prep.

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Table 2: Sky probe components

CCD Model Apogee U42 Interface USB 2.0 Shutter integrated iris Arraysize 2048x2048 Pixelsize 13.5µm Gain 1.0 e/ADU Readnoise 10e Darkcurrent 2e/sec Depth 16 bits/pixel Read out time 10 sec Cooler Thermoelectric w/ glycol Lens Nikon 500 mm F/4 Aperture 12.5cm Fieldofview 9sqrdeg Pixelscale 5arcsec/pixel Computer LittlePC 1.5GHz Operating system Redhat Filter wheel 5 filters grizy Filter size 140 mm diameter Encoder drive Animatics Smart Motor Counts / rev 19200 Positioning error 1 µm Focus drive Stepper motor

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Figure 2: Linearity and gain performance tests of the Apogee CCD detector. The top plot shows the response of the detector to a constant flat field lamp over a range of exposure times. The line shows the best fit linear relation. The dark current is unimportant at just 2 electrons/sec. The detector saturates at 55000 counts, but the response remains linear to this limit. The lower plot, demonstrates the signal to noise properties of the detector. The slope of the relation gives the gain of 1.0 electrons/ADU.

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Figure 3: Two views of the insides of the Imaging Sky Probe. Visible is the CCD and camera lens, as well as the five-petal filter wheel.

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Figure 4: The Imaging Sky Probe mates to a mounting frame on the PS1 mirror cell. Before being locked down, the platform floats on a ball joint, at the top, to facilitate alignment.

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Ultraviolet Imaging Probe (UIP) for the Pan-STARRS-1 Telescope

Klaus W. Hodapp, Kenneth Chambers, and the Pan-STARRS Team

University of Hawaii, Institute for Astronomy, 640 N. A’ohoku Place, Hilo, HI 96720

1. Introduction

The Pan-STARRS-1 telescope has been designed to operate with a filter set that is closely matched to the Sloan Digital Sky Survey (SDSS) g, r, i, and z filters. In addition, an even longer wavelength filter near the cutoff of Si CCDs, the y filter, will be used. However, a deliberate decision has been made not to include a filter covering ultraviolet wavelengths. The reasons for this are both scientific and technical.

For the main scientific mission of the Pan-STARRS project, the search for potentially hazardous asteroids, observations in the near ultraviolet offer only disadvantages over work at longer wavelengths, since both the reflectance of typical asteroid surfaces as well as the reflected solar spectrum peak at wavelengths much longer than ultraviolet. Many other core scientific projects planned with the Pan-STARRS data are focused on high-redshift objects, many of which are characterized by being undetectable at ultraviolet wavelengths. On the technical side, near-ultraviolet wavelengths pose many problems: The seeing is generally inferior to that at longer wavelengths, atmospheric dispersion is a much more limiting factor, and the difficulty of designing corrector optics increases rapidly. By ignoring the near-ultraviolet wavelengths, a system design with significantly better performance at longer wavelengths is possible: The Pan-STARRS CCD detectors (paper by J. Tonry, these proceedings) are optimized for longer optical wavelengths, relatively simple corrector optics can be built (J. Morgan, these proceedings), and the coatings both for the mirrors and the corrector lenses can be optimized for longer wavelengths.

While the decision not to include a near-ultraviolet bandpass in the Pan-STARRS filter set is both scientifically justified and technically sound, some projects could still benefit substantially from observations at these wavelengths. Therefore, we are working on a small additional sky probe dedicated to obtaining relatively shallow, but well calibrated, photometric data in the SDSS u-band.

2. Scientific Justification

Most photometric systems contain a filter bandpass in the near ultraviolet, between the terrestrial atmospheric cutoff defined by ozone absorption and the Balmer break in the spectra of hot stars. The reasons for this choice are that hot stars, spectral types O and B, emit most of their flux at these wavelengths. Fig. 1 shows a comparison of the Sloan Digital Sky Survey (SDSS) bandpasses and typical stellar spectra. For ground-based observations, the near-ultraviolet band (the u-band in case of the SDSS) is therefore the best tracer of these hot, and necessarily young stars.

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Fig. 1: The Sloan Digital Sky Survey (SDSS) bandpasses, compared to a range of stellar spectra [1].

Another important benefit of including a near-ultraviolet band is related to the problem of determining the effective temperature of a star and interstellar extinction towards it. The effective temperature can be used to determine the absolute magnitude of a main-sequence star. In combination with the interstellar absorption, this can then be used to determine the distance to the star from a comparison of absolute and apparent magnitude of that star. Interstellar extinction is wavelength dependent and therefore leads to a reddening of a star’s colors, an effect that is difficult to distinguish from a lower temperature of a star if only two wavelengths and therefore one color are used. The method usually employed to break the degeneracy of temperature and interstellar extinction is the use of color-color diagrams, where two colors (magnitude differences) are plotted against each other. The main sequence of ordinary, hydrogen burning stars usually forms a well-defined locus in these diagrams. Fig. 2, from [2], shows three examples of such color-color diagrams based on Sloan Digital Sky Survey data obtained in bandpasses similar to those of Pan-STARRS-1. Once a unique combination of interstellar absorption and temperature has been established, the star can be placed in a color/absolute-magnitude diagram.

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Fig. 2

Three color-color diagrams based on the SDSS filter set. The grey near the left end of each diagram represent RR Lyrae stars. The dots represent the locus of main sequence stars. The arrow is the reddening vector for an interstellar absorption AV=2 mag. The comparison of these diagrams shows that multiple color-color diagrams are required to obtain a unique solution for star color (basically effective temperature) and interstellar absorption.

Included in these diagrams is an arrow representing the reddening vector corresponding to an interstellar

extinction of Av=2 magnitudes based on the Aλ/AV values for the SDSS filters [1]. The absorption in the Johnson V filter is commonly used as a standardized measure of interstellar extinction, even if the actual photometry is done in a different photometric system.

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As can be seen from this figure, the interstellar reddening vector often lies parallel to the distribution of stellar colors. In these cases, the degeneracy of stellar color (or temperature) and interstellar reddening (and therefore interstellar absorption) cannot be resolved.

The center panel is the (g-r)/(r-i) color-color diagram, combining the shortest bandpasses that Pan- STARRS will provide. The red colored data points represent RR-Lyrae stars that are not of concern in the context discussed here. To the left side of all color-color diagrams, early spectral type stars (B and A) are plotted, the right end of each diagram represents stars of spectral type M. The central panel of Fig. 2 illustrates that both for early spectral types, as well as for intermediate spectral types (around K type), the reddening vector lies parallel or tangential to the locus of the main sequence. This means that with these data points alone, it is impossible to compute a unique solution of intrinsic stellar color and interstellar reddening.

The longer wavelength (r-i)/(r-z) color-color diagram shows a reddening vector that is not parallel to the main sequence locus for all but the earliest spectral types. It therefore resolves the intrinsic color and interstellar reddening degeneracy for most stars.

The top panel shows the (u-g) and (g-r) color-color diagram, combining the three shortest bandpasses used by SDSS. Of the three diagrams shown inthis has the largest angle between the locus of the main sequence and the reddening vector, and therefore offers the best opportunity to determine intrinsic color and interstellar reddening separately for early-type stars.

In combination, these diagrams illustrate that, in order to arrive at a unique solution of intrinsic stellar color and interstellar reddening, all three color-color diagrams are required, and that the inclusion of the u-band enables the determination of these parameters for hot stars.

Beyond this particular scientific example, the benefits of obtaining even a shallow ¾ -of the sky survey in the u-band is the establishment of a sufficiently dense grid of stars of known u-band flux so that any project that requires u-band data can simply calibrate their deeper images using these known secondary calibration stars. The Pan-STARRS astrometric and photometric survey will cover a larger area of sky than the SDSS. A u-band imaging probe that works in parallel to PS1 and gets it data reduced in the same data reduction pipeline as the Pan-STARRS ISP will fully benefit from the careful characterization of the photometric quality of each night that PS1 will perform for the AP survey. It is therefore reasonable to expect that the photometric calibration of all PS1 AP bands, including the ISP and the UIP data, will be superior to that of the SDSS.

The latest work on design reference mission for the AP survey suggests that each area of the ¾ of the sky will be visited 72 times for 30 seconds over the course of the 3 year duration of the survey. Therefore, the u-band survey can be expected to be both quite deep and photometrically precise. Assuming a reasonably good throughput, which will be discussed in more detail below, we expect to achieve a similar depth in the accumulated data as the SDSS. With this large number of images, some image quality can be recovered by sub-sampling the 5 arcsec pixels of each image. In the medium deep fields, there will be about 800,000 s of total integration time over the 3 years and the limiting magnitude should substantially exceed the SDSS.

3. Survey Concept

As desirable as a 3/4 –sky u-band survey may be, conducting a sky survey, reducing the data, and making them available in a data base is a formidable task. As a stand-alone project, such a survey has never been undertaken. However, as an add-on to the Pan-STARRS-1 AP survey, such a survey is easily feasible, since the Pan-STARRS ISP will conduct shallow sky surveys in the Pan-STARRS g, r, i, z, and y bands. Therefore, the integration of such a sky probe into the PS1 system, software control of the sky probe, readout of the CCD, pipeline reduction of the data, and preparation of the accumulated, distortion-corrected

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sky images will all be largely in place and can be used for UIP. It is only in this environment of PS1 that the UIP survey can be conducted at little marginal cost.

4. Hardware Concept

The hardware of UIP is relatively simple and cheap. The UIP is expected to use an Apogee CCD camera, the same camera originally used for the ISP, if and when ISP will be upgraded to a deep depletion orthogonal transfer array. The apogee CCD camera is a 2k×2k thinned CCD with 13.5μm pixels. It is the largest thermoelectrically cooled high quantum efficiency CCD camera that is commercially available. To match the field of view of the PS1 telescope, the UIP needs to have a focal length of 500mm, the same as the ISP.

We have evaluated several options for the optical system used for UIP. The starting point was the Nikkon lens used by the ISP (B. Granett, these proceedings). The problem with this commercial, all refractive system was the large total thickness of glass used and the marginal optical quality. In combination, the likelihood of this system working well in the near-ultraviolet were low. We have evaluated a simple, corrector-less Schmidt telescope design. The advantage of such a custom- design would have been that all optical parameters would have been completely controllable. However, the f-ratio achievable with good optical quality was low, and the cost even for such a simple custom system was high. A search of the market for amateur astronomy astrographs resulted in the selection of the new Takahashi Epsilon 180 astrograph as the optical system for UIP. This astrograph has the required focal length of 500mm, has a clear aperture of 180mm and offers excellent image quality at optical wavelengths. The design uses a hyperboloid primary mirror, a flat Newtonian secondary, and a doublet corrector lens made from extremely low dispersion glasses. Unfortunately, we could not obtain throughput and image quality data in the u-band from the manufacturer. The image quality in the blue, however, is much better than we require, and the collecting area of the astrograph is large, so that we can achieve acceptable performance even in the face of reduced image quality and throughput in the u-band. Of all commercial optical systems available on the market, the Takahashi E 180 offers the best mix of characteristics for use as the UIP optical system. Multiple examples of high-quality astronomical images taken with the E 180 at optical wavelengths by amateur astronomers exist and demonstrate the system is optically and mechanically of good quality.

We will adapt the E 180 for use in the UIP by motorizing the focusing mechanism, using much of the same hardware and software solutions that were used for the ISP.

The UIP will be equipped with a single fixed u-band filter. We intend to purchase a filter that nominally reproduces the bandpass of the SDSS, but avoids the red leak problem that SDSS has encountered with their filters. Also, as SDSS has found out, even nominally identical filters will have slightly different bandpasses when used in ambient conditions (as in UIP) compared to use in vacuum as in the SDSS camera.

Fig. 3: The Takahashi E 180 optical tube assembly

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Fig. 4: The E 180 focusing mechanism

Fig. 5: The micro-focusing gear unit planned for UIP

Fig. 6: Drawing of the central box of the PS1 telescope. Indicated in green is the ISP. The spectroscopic sky probe will be of identical dimensions and will be mounted symmetrically on the left side. Between those two instruments, the UIP will be mounted.

References:

[1] Girardi, L, Grebel, E. K., Odenkirchen, M., and Chiosi, C, Theoretical Isochrones in several Photometric systems, 2004, A&A, 422, 205

[2] Ivezic, Z. et al., Candidate RR Lyrae stars found in Sloan Digital Sky Survey commissioning data, 2000, AJ, 120, 963

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The Pan‐STARRS Gigapixel Camera J. L. Tonry, P. Onaka, G. Luppino, S. Isani and the Pan‐STARRS Project Team

University of Hawaii, Institute for Astronomy The Pan‐STARRS project will undertake repeated surveys of the sky to find “Killer Asteroids”, everything else which moves or blinks, and to build an unprecedented deep and accurate “static sky”. The key enabling technology is a new generation of large format cameras that offer an order of magnitude improvement in size, speed, and cost compared to existing instruments. In this talk, we provide an overview of the camera research and development effort being undertaken by the Institute for Astronomy Camera Group in partnership with MIT Lincoln Laboratories. The main components of the camera subsystem will be identified and briefly described as an introduction to the more specialized talks presented elsewhere at this conference. We will focus on the development process followed at the IfA utilizing the orthogonal transfer CCD in building cameras of various sizes from a single OTA “μcam”, to a 16‐OTA “Test Camera”, to the final 64‐OTA 1.4 billion pixel camera (Gigapixel Camera #1 or GPC1) to be used for PS1 survey operations. We also show the design of a deployable Shack‐Hartmann device residing in the camera and other auxiliary instrumentation used to support camera operations.

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The Pan‐STARRS PS1 Calibration System J. L. Tonry, C. W. Stubbs, J. Masiero and the Pan‐STARRS Project Team

University of Hawaii, Institute for Astronomy The Pan‐STARRS PS1 prototype system has a goal of achieving 1% photometry, and to reach this goal, several challenging system and subsystem requirements must be met. A calibration system for the PS1 camera is a critical component in the current system design that should allow PS1 to meet the 1% photometry goal. In this talk, we briefly review the main challenges in breaking through the 1% precision barrier, we identify and discuss approximations and limitations inherent in the present methodology, and we propose an alternative scheme for the relative calibration of astronomical instruments. The proposed conceptual scheme exploits the availability of tunable lasers to map out the relative wavelength response of an imaging system using a flatfield screen and a calibrated reference photodiode. We show how the conceptual scheme is being implemented for the calibration system to be used with the Pan‐STARRS PS1 prototype telescope at the Haleakala summit.

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Pan-STARRS PS1 STARGRASP Controller P. Onaka, J.L. Tonry, S. Isani, A. Lee, R. Uyeshiro, C. Rae, L. Robertson, G. Ching and the Pan-STARRS Project Team Institute for Astronomy 2680 Woodlawn Drive Honolulu HI 96822

ABSTRACT

The requirements for rapid readout and low noise combined with the unprecedented number of pixels in the Pan- STARRS camera (~1.4 Gigapixels) poses a challenge for the design of the readout and signal chain electronics. This paper presents the hardware and software design for the Pan-STARRS Scalar Topology Architecture of Redundant Gigabit Readout Array Signal Processors (STARGRASP) controller. Compared to other controllers, the current design also yields a significant reduction in physical size and number of required PCBs with a corresponding reduction in power consumption. Over the past two years, Pan-STARRS controller development has been rapid with the current STARGRASP performance meeting requirements.

INTRODUCTION

The requirements imposed on the array controller for the PanSTARRS Orthogonal Transfer Array (OTA)1 in an 8x8 mosaic presents a formidable challenge to any existing design. Despite the presence of an ongoing array controller development program at the Institute for Astronomy, a completely new effort was undertaken to produce a next generation design that could scale up to the needs of a gigapixel focal plane instrument. The team that was assembled to produce this design consisted of 2 scientists, 3 hardware/firmware engineers and 2 software engineers each with specific expertise and experience in critical areas.

DEVELOPMENT AND INFRASTRUCTURE PLAN

A multiphase plan has been developed to produce the STARGRASP array controller. A distinct infrastructure and prototype phase was identified as critical for success and the Institute for Astronomy has acquired new CAD development tools, Ball Grid Array (BGA) integrated circuit package rework and inspection capability, clean room, and a wire bonder. The BGA rework and inspection tools were especially important because the small form factor requirements for the controller required the use of BGA packages. Optical and X-ray BGA inspection microscopes have already proved valuable in troubleshooting. The prototype phase has been successfully completed, resulting in a electronics board set that is capable of running an individual OTA.

REQUIREMENTS

Modes of Operation The OTA will be operated in a sequence of different modes of clocking and readout to accomplish the overall goal of a corrected science image.2 The OTA design incorporates specific circuitry that will need to be serviced by the controller electronics to accomplish these modes.

Mode Name Description OTA Controller Function Required operation Idle No integration, no guide Resets? Reset OTAs? Shutter Control Mode No integration, no guide

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Guide Star Acquisition mode ~1 sec Integration Integrate Reset, Integrate timing, ~0.5 sec Readout Address cells Readout, for ~ 5 guide objects in Clock out Transport data to Pixelserver 5 cells per OTA computer, Pixelserver computer determines centroids and corresponding clocking patterns for image correction. Combined Guide and Fast clocking and readout of Integrate Integration Mode Guide cells Address cells Clock out Low Noise Readout Mode (Closed shutter) readout of Integrate science image Address cells Clock out

The typical sequenced readout of an OTA would be:

Sequenced readout : Open Shutter Acquire ~1 sec integration, readout ~0.5sec Pixelserver determines ~5 guide objects in 5 cells per OTA Guide + Integration ~5 guide cell subarray readout at 10-30Hz (<100Hz) Pixelserver centroids and determines guide subarray position Pixelserver also computes OT parallel shift patterns for remaining cell Expected Itimes Nominal 10s of seconds Max ~1000sec Min ~shutter speed/setup Apply OT parallel shift clocks (~10usec each) Delay ~50msec Close shutter and Readout ~2sec

Full Camera operation • Synchronized clocking to <30nsec. • Heat dissipation from controller system should not impact seeing. • System will need active cooling. • System goal of < 400Watts (On Telescope, per Gigapixel Camera). • Noise floor 1 LSB, less than 1e-. • OTA crosstalk must be less than noise floor

OTA Input and Control • Sequenced readout • guide rates 10-30Hz • Nominal guide patch o @ 0.3 arcsec/pixel 10 arcsec patch = ~32 X 32 pixels, 10msec/1024 pixels = 9.77 usec/pixel max speed clock+signal condition+ADC+buffer • Centroid and shift calculation time + Change clock patterns, latency goal = 2msec goal • Clocking pattern time resolution ~10nsec

OTA Output

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• 100K e- well and expected 4e- noise at 1 MHz • 2e- noise goal at 100 kHz (guiding) • ADC bit resolution – approx two bits on the noise o 100k / 4 = 25K X 2 = 50K resolution wanted ƒ 16 bits = 64K ( > 14bits) • Image memory o OTA 4096 x 4096 pixels = 16M pixels = 32MBytes (16 bit resolution) o 8 x 8 focal plane = 1Gpixel = 2Gbytes (16bit) • Data buffering and storage o Unless data transport is real time, minimum storage is 2Gbytes, 4Gbytes double buffered. • Memory access speed minimum ~8 to 16Gbits/sec (1 to 2 Gbytes/sec). • Full 1Gpixel focal plane readout in ~2sec = ~8Gbits/sec • Signal processing chain o 1Gpixel / 64 OTA = 16Mpixels / OTA o 16Mpixels / 8 outputs = 2Mpixels /output o 2 sec / 2Mpixels = 954nsec/pixel o ADC plus settle time ~1usec per output o 1Mhz ADC borderline too slow (unless CDS built-in) • Controller data transport o Maximum speed matches Full 1Gpixel focal plane readout in ~2sec = ~8Gbits/sec o Minimum speed equal to shortest expected science integration ~10s = 214 Mbits/sec • Internal (controller) data path >= 8Gbits/sec • Path to Pixelserver(s) >= 8Gbits/sec

Internal Data Throughput Requirements

Stage Array/ADC 1G ethernet Pixel Data Storage Readout Processor

Requirement Goal 128Mbps Min 64Mbps Min > 256Mbps Min 64Mbps Goal >256Mbps

Capability Max 260Mbps >800Mbps Processor/ Burst ~60Mbps (5MSps/ADC x theoretical memory Only ~20MBps sustained 24) access (read + write)

Requirement 33.55 Mpixel/sec 133/400/533M Duty cycle? Notes (~2sec) hz FSB

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PHYSICAL FORM FACTOR AND SCALABILTY

The size and shape of the controller electronics is as much of a driver of the design as the electrical requirements. The short cassegrain depth available on the telescope and the size of the focal plane resulted in a horizontal arrangement with the controllers place outboard of the mosaic.

8 x 8 OTAGigapixel Focal Plane

1 x 4 Rigid-Flex Assembly

The 8 by 8 mosaic focal plane was further broken down into 1 by 4 OTArigid-flex assemblies (see figure). The rigid flex assembly will penetrate the dewar wall and be connectorized for direct attachment to the controller. The ideal size for the chassis would be the width of an individual OTAwhich is ~50mm. The 3U CompactPCI form factor was chosen due to the availability of off the shelf products including standardized connectors that insure electrical, mechanical, thermal, and EMI performance.

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SYSTEM AND NETWORK ARCHITECTURE

8 STARGRASP chassis will be required for the first Gigapixel camera. Each chassis is an individually removable 4 card slot, fan cooled assembly that controls OTAs. Each card slot is connected via a standard 1 gigabit ethernet fiber link to a commercial network switch which is also connected to a bank of Pixelservers (1U computers). The advantages of this architecture are that there is no custom communications electronics (or associated driver software) required in the Pixelservers, the network is robust, inexpensive and can be reconfigured on the fly.

Chassis V2 Chassis V2 Chassis V2 Chassis

V2 LAN Switch 1Gb ethernet

OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA

Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA 1Gb ethernet

Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA

OTA OTA 1G ethernet

Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA OTA Fiber isolated Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA Pixel Server

OTA OTA OTA OTA OTA OTA OTA OTA Pixel Server

Pixel Server Chassis V2 Chassis V2 Chassis V2 Chassis V2

Pixel Server

Pixel Server

Pixel Server

Pixel Server

Pixel Server Power Supply Pixel Server

Pixel Server

PIXELSERVERS AND SOFTWARE PROCESSES

Commercial off the shelf, 1U rackmount server computers have been tested and chosen as the standard Pixelserver platform (although any commercially available equivalent computer type can suffice). The required data storage capacity, communications speed and processing rates meet our requirements (except for the orthogonal guide mode which hasn’t been tested yet). The software running under a LINUX operating system on the Pixelservers consists of several processes. • Conductor, a combination of scripts, shells, and services providing the glue between gpccom and multiple daqcom processes. Conductor also includes a "status server" which may collect current temperatures, statistics, and user settings from/for gpccom and daqcom.

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• Gpccom, Camera client operational software for the camera-level. • Daqcom , Data acquisition client. Daqcom runs on the pixel server and handles communication with daqserv. It translates an input set of higher level commands into the lower level commands for the embedded system. It also receives UDP pixel data which it combines with header data to form multi-extension FITS format files. Daqcom is built on libraries "libsockio" "libcatp" and "libfh" and "libcli" which are also provided with the package.

On each embedded STARGRASP FPGA, the Daqserv process is running. • Daqserv, low-level data acquisition server. The software portion of the flash images for the controller FPGA boards. Implements a network service that accepts commands to load parameter sets, select binning and raster sizes, manipulate digital outputs, and trigger readout (TCP/IP socket). Once readout is triggered, pixel data is transmitted by daqserv using UDP/IP to the endpoint specified on the command interface.

Software Processes

CRYOGENIC WIRING AND CHASSIS

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A full Giga pixel camera focal plane has on the order of 4500 OTA signals present in the cryostat. To support this, rigid-flex (combination flex cable and printed circuit board) assemblies were developed. Each assembly connects 4 cryogenically cooled OTAs through the vacuum cryostat wall directly to a backplane with 2mm CompactPCI form factor connectors. The STARGRASP preamplifiers connect directly into these connectors eliminating all hand wiring.

Rigid-flex Assembly

Chassis mounted to cryostat

Dual Chassis mount , top view

STARGRASP EMBEDDED BOARDSET

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A STARGRASP board set consists of a 16 channel (2 OTA) preamplifier, DAQ3U data acquisition motherboard and an FPGA daughterboard.

Power Supply Pixelserver 1Ge network BKPLN PREAMP_V2 DAQ3U_V2

16 preamp 16 ADCs channels

Bipolar DAC Clocks 90chnl HV amps Rigidflex Unipolar Clocks

HDL [+clocking engine]

OCM RS232 dcache

icache PPC405 1G Ethernet BRAM

DDR SDRAM

FPGA (daughterboard)

Block diagram

• The Preamplifier board contains 16 channels of OTA amplification, 2 channels of RTD or diode temperature drive and sense, and high voltage buffers. Each amplifier channel has an AC coupled input with a DC clamp function and a 2 pole second stage Bessel filter. • The DAQ3U board contains 16 individual channels of 10MHz, 3 channel 16bit Analog to Digital Conversion (Analog Devices AD9826). Programmable levels and timing for clocks, biases, and operation of fixed voltage level select signals. The programmable levels are generated by three 32 channel 14bit DACs. • The FPGA board has a Xilinx Virtex2Pro FPGA which has a hardcore PowerPC CPU running at 300Mhz , a 1 gigbit ethernet fiber interface, and 256MB of DDR ECC SODIMM SDRAM.

PREAMPLIFIER, MULTIPLE SAMPLING AND DIGITAL CDS

The high ADC conversion speed allows us to apply a multiple sampling scheme on the OTA video signal. As depicted in the oscilloscope trace (video signal in blue on top, sampling time in green and summing well signal on the bottom), high speed multiple samples are taken on both the pedestal and signals levels. The multiple samples can be averaged in the FPGA or farther downstream in the Pixelservers. The Correlated Double Sample calculation is handled digitally in this system and allows the preamplifier to have a simple 2 stage design.

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Multiple sampling example

Preamplifier board

DAQ3U AND ADC COMPONENTS

The proper choice of Analog to Digital Converter (ADC) is critical to achieve the noise, speed and size requirements for the system. After an industry search for an acceptable 16 bit ADC, a series of tests were performed to evaluate the

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Analog Devices ADC9826, a 16 bit, ~15 to 12.5MSps, 3 channel ‘Imaging Front End’. The AD9826 has several non- ideal specifications including a read noise of 3 LSB and integral non-linearity of 16 LSB3. However, the multiple sample averaging technique has been successfully prove to reduce the ADC read noise to 1.69 ADU (Analog to Digital Units)

Table 1 Bench Noise Results Test Conditions 300nsec Sample 100nsec Sample 100nsec CDS 180nsec CDS with 4 sample Single Channel and Hold and Hold digital average Bench test Noise for DC 3.667 ADU 3.391 ADU 3.546 1.690 input ADU ADU

DAQ3U board

FPGA BOARD AND EMBEDDED CODE

FPGA board

The Virtex2Pro FPGA operates at 2 levels. The PowerPC405 is running a kernel that allows for high speed control and data communication over gigabit ethernet. This level of software also interfaces and controls a lower level of embedded

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functionality that provides high speed programmable clocking of the OTAs. This hardware description language based “Clocking Engine” design is a command FIFO structure that executes the following command set:

• Delay for precise amount of time • Wait for external trigger (sync) • Set voltages (biases, clock levels) • Do 1 or more parallel clocking sequence • Do 1 or more serial+video clocking sequence • Select cells (by manipulating. digital output lines) • Shift cells during integration (OT correction) • Provides repeatable, precise timing

Clocking Engine conceptual diagram

A software GUI has been developed to aid in the optimization and test of the different types of Lot#1 and #2 OTAs. Named Cestlavie, it allows an operator to graphically load, edit, save and real time download a CCD clocking pattern to the STARGRASP controller.

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Cestlavie GUI

Data communication throughput across the 1 gigabit ethernet link was been excellent. 400 to 600Mbits per second speeds are achieved with a UDP protocol through commercial network switches. The large SDRAM memory on board makes this possible because it is able to buffer entire device images.

CURRENT STATUS

Since the Lot#1 OTA devices had higher read noise than hoped for, testing was performed on a low noise CCID20 device. Using a 4 multiple sample readout, we were able to achieve 3 to 4 electron read noise at 1/5 to 3usec per pixel readout rates.

Read Noise at pixel rate CCID20

4.2

4.1

4

3.9

3.8

3.7

3.6

3.5

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CCID20 Read Noise vs Readout rate

Lot#2 MIT/LL CCID58 OTA testing has just been started and we have verified at least an X2 output gain increase (~10uV/e-) and X2 decrease in read noise (~8e- un-optimized operation). Having been thinned to 75micron thickness to increase their red response the Lot#2 devices require a negative bias (5 to -40 volts) on their substrate to achieve good charge diffusion. The improved charge diffusion with substrate bias has been demonstrated and an optimal working voltage is being investigated because a too negative voltage produces bright pixel defects.

The Test Camera #3 which has a 16 OTA focal plane, has been assembled and is undergoing integration and cold tests. It will be the first camera on the PS1 telescope.

A single OTA test camera named Microcam has been assembled on a tripod and on the PS1 telescope. It is being used to evaluate the RFI environment at the telescope site (PS1 is in the near field of several television and radio transmitting towers ). The star image was taken outside the dome at the PS1 site..A second Microcam is being planned to help develop the orthogonal transfer correction code for the controller.

Tripod mounted Microcam

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Microcam image (outside the PS1 dome)

ACKNOWLEDGEMENTS This material is based on research sponsored by Air Force Research Laboratory under agreement number F29601-02-1- 0268. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the ofcial policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government.

REFERENCES

1 Barry Burke et al., elsewhere in these Proceedings. 2 J. Tonry, G. Luppino, N. Kaiser, B. Burke, G. H. Jacoby, “Gig-Pixels and Sky Surveys”, in Scientific Detectors for Astronomy, P. Amico, J. W. Beletic, J. A. Beletic, ed., Vol 300, pp. 395-402. 3 Analog Devices AD9826 data sheet, www.analog.com.

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The Pan-STARRS PS1 Image Processing Pipeline Eugene Magnier, Nick Kaiser, Ken Chambers Institute for Astronomy, University of Hawaii, Woodlawn Dr., Honolulu, HI 96820 ABSTRACT The Pan-STARRS PS1 Image Processing Pipeline (IPP) performs the image processing and data analysis tasks needed to enable the scientific use of the images obtained by the Pan-STARRS PS1 prototype telescope. The primary goals of the IPP are to process the science images from the Pan-STARRS telescopes and make the results available to other systems within Pan-STARRS. It is also responsible for combining all of the science images in a given filter into a single representation of the non-variable component of the night sky, defined as the “Static Sky”. To achieve these goals, the IPP also performs other analysis functions to generate the calibrations needed in the science image processing, and to occasionally use the derived data to generate improved astrometric and photometric reference catalogs. It also provides the infrastructure needed to store the incoming data and the resulting data products. 1. BACKGROUND The Pan-STARRS Telescope #1 (PS1) on Haleakala is nearing completion, and is expected to start survey operations in early 2007. It is designed to act as a test-bed for the commissioning, testing, and calibration of the Pan- STARRS hardware and software in anticipation of the full Pan-STARRS array. PS1 uses a 1.8m primary mirror to image a roughly 7 square degree field on a 1.4 Gigapixel CCD camera. With 0.26 arcsecond pixels, the images from PS1 will be well-matched to the sub-arcsecond seeing of Haleakala. The large entendue of PS1 will make it the most efficient survey telescope for the near future. The PS1 Survey Mission is expected to last three years, and will produce in the vicinity of 1.8PB of raw image data. The PS1 Survey Mission will consist of multiple survey components, including a very large area survey covering three quarters of the full sky (the 3π survey) and targeted fields observed frequently and repeatedly for both extensive temporal coverage and depth. A major driver for the observing strategies is to efficiently detect solar system objects, including potentially hazardous asteroids. The telescope will use 6 filters, including a wide filter (w) for surveys specific to the solar-system, as well as Sloan-like griz filters and a y filter at the reddest end of the camera sensitivity. This last filter will exploit the high quantum efficiency of the detectors at 1 micron. The Pan-STARRS PS1 Image Processing Pipeline (IPP) is designed to perform essentially all of the image analysis tasks required to extract the science results from the survey data. Like other large-scale surveys (e.g., the Sloan Digital Sky Survey or the 2 Micron All-Sky Survey), end users will have access to derived data products, not the raw image data stream. The IPP will perform the individual image calibrations, image combinations, and object measurements needed to characterize the astronomical sources detected in the images. During clear, dark nights, the PS1 telescope will produce images at sustained rate of one 3GB image every 45 seconds, for periods as long as 10 hours. The IPP needs to perform the data processing with a high enough throughput to keep up with the raw image data. The resulting data products, and the extensive supporting metadata stream, will be made available to the other components of the project, including the Published Science Products System (PSPS), which will make the data products available to users via a database and sophisticated query mechanisms. Other science clients which will perform additional interpretation of the science data products will also receive subsets of the full IPP output data stream. 2. IPP OVERVIEW 2.1. Scope The IPP receives data from two Pan-STARRS subsystems: the Camera, from which it receives the large volume of image data, and OTIS (Observatory, Telescope and Infrastructure Subsystem), from which it receives metadata describing the images and the environmental conditions. The location of the IPP computing hardware will evolve over the course of the first year of the PS1 project. Initially, during the start of the commissioning phase, a subset of hardware will be located at the summit facility on Haleakala. The Maui High-Performance Computing Center (MHPCC) is in the process of relocating its computer room. When the high-bandwidth network connection is established from the PS1 site to MHPCC and the new facility is available, the IPP hardware will be relocated to the MHPCC computer room in Kihei.

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The users of the IPP output are all systems internal to the Pan-STARRS project. They consist of: 1) The Preferred Science Clients, which receive specified data products on short timescales. 2) The Moving Object Processing System (MOPS), which is one of the Preferred Science Clients, but has the distinction of being a component funded by Pan-STARRS. It will receive the detections of all transient objects. 3) The Published Science Products Subsystem (PSPS), which will receive all data products of interest to the community external to the Pan-STARRS data processing systems, and will act as the long-term archive and publishing clearinghouse. 2.2. Analysis Tasks The IPP performs several types of data analysis in a regular fashion. The most obvious of these is the science image analysis, from which the measurements of individual astronomical objects are actually derived. In preparation for this critical function, the IPP must also analyze the calibration images needed by the science image analysis. Downstream from the science image analysis, the IPP must perform data calibration on the collection of object detections, yielding improved calibrations and improved reference catalogs for astrometry and photometry. The IPP science image analysis is separated into two major stages: the analysis of individual images (historically called “Phase 2”) and the analysis of groups of images taken of the same portion of the sky (“Phase 4”). This division is illustrated in Fig. 1. The individual images are analyzed independently, as they arrive from the telescope. At this stage, the standard image detrending steps (bias, flat, etc) are performed, as discussed below. Objects are detected in these images, and used to perform astrometric and photometric calibrations. Stars and non-stellar objects are distinguished, but only limited effort is spent at this stage on characterizing the extended sources. Phase 2 ends with calibrated individual images and tables of objects from those images. In the second major stage, the images which correspond to the same portions of the sky are combined. Several image combinations may be performed. Sets of individual science images may be combined into a single, high-quality image which has been cleaned of cosmetic defects. Comparison between this image, or the individual images, and an archival reference image of the same location (the “Static Sky” image) may be performed. Image difference techniques are used to detect the variable, transient, and moving sources. Finally, the new images may be combined with the Static Sky image in order to improve the signal-to-noise. All of the images described above will have object detection and classification performed on them. The objects detected in the summed image stacks are called “Phase 4 Σ”, while the image difference detections are called “Phase 4 Δ” detections.

PS-1 : N sequentially PS-4 : 4 simultaneous

raw stacked image The Static Sky - Image Combine static & sky Image Difference image + difference image

Fig. 1. IPP Image Analysis Stages

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Additional science image analysis is performed on the Static Sky images. It is in this stage that detailed analysis of the shapes of extended objects is performed. Since the Static Sky is the most sensitive to the faint galaxies, and since the galaxies do not change with time, this is the logical place to perform this type of analysis. In the Static Sky analysis, the data from all five filters will be analyzed at the same time to improve the signal-to-noise for fainter sources. Common parameters such as the location of the galaxy may be fitted to a single value in all filter images. An important outstanding issue is how to account for the variations in seeing between the multiple images which contribute to the Static Sky images. The measured galaxy shapes require a good estimate of the effective PSF for each object. The baseline IPP code will initially estimate the impact of the effective PSF on the parameters of the observed galaxy shapes. However, a possible extension of the software will allow for the PSF to be convolved with the model before fitting to the observed flux distribution. If this analysis may be made sufficiently efficient, or enough computing resources are available, it may be possible to perform this type of PSF-convolved fitting across all input images simultaneously without sacrificing information in the image warping and stacking process. The IPP is also responsible for generating a high-quality astrometric and photometric reference catalog from the collection of measurements of the astronomical sources. As the images are analyzed, the information about each object is supplied to the IPP object database software called DVO (the Desktop Virtual Observatory). Several programs interact with this database to iteratively improve the calibration of the individual images and to update the astrometric and photometric reference catalog. This analysis can be viewed as a very large least-squares problem, in which the astrometric or photometric parameters of the images are solved for to minimize the residuals for individual objects, while the positions and magnitudes of individual objects are adjusted to minimize the residuals for individual images. This analysis will likely be limited to the objects which have been observed with sufficient signal-to-noise ratios to have a strong detection, but the resulting image parameters can then be used to characterize all objects in the images. As a natural consequence of this analysis, the objects which have significant residuals even after the iterations have run their course can be identified as photometric variables or objects which detectable proper-motion and/or parallax. Images which were obtained until less-than-ideal conditions will also be detected, and potentially excluded from this analysis.

Analysis Programs Support Tools

psphot camera configurations psastro ippTools ppImage ippMonitor ppMerge pantasks scripts stac ppStats pois ppNorm

psModules

system configuration camera representation image combination detrend construction image subtraction detrend application astrometry calibration object analysis photometry calibration

psLib

memory statistics error handling fits / minimization data containers FITS & XML I/O images DB I/F vectors time FFTs earth orientation

Fig. 2. IPP Architecture

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2.4. IPP Software and Hardware Organization The programs used to perform the image and object analysis stages are designed as stand-alone UNIX programs. These programs may be run manually, if desired. For the bulk of IPP operations, the programs must be automatically launched for specific images when needed. All automatic analysis tasks are tracked within the IPP Metadata Database, which also records the Q/A statistics produced by the analysis steps. The operation of the IPP is managed by a program called “PanTasks” which schedules specific analysis tasks based on the current state of the Metadata Database. The IPP image analysis tasks are well suited to parallel processing. Not only are individual images processed in Phase 2 independently, but most of the computational effort for each of the chips of the mosaic camera may be processed without reference to the other chips. The analysis of different patches of the sky ('sky cells') in Phase 4 may be performed independently as well. PanTasks uses the associated program, “pcontrol” to distribute analysis tasks across a computer cluster. The IPP can take advantage of the natural parallelization units (chips and sky cells) to make interesting optimizations for the parallel processing. For example, each chip may be assigned to a specific computer, allowing all of the Phase 2 operations on that chip, as well as all calibration image analysis for that chip, to be performed on the same computer used to store the data. This type of optimization minimizes the load on the network switch since most data access is to a local disk array. In order to facilitate testing and development, and to encourage flexibility, the IPP is built in a layered fashion. The lowest level functions define basic data types, statistical analysis, and I/O operations. These are written in C and collected together into a library called “psLib”. The next layer consists of functional elements which are more specifically related to astronomical data analysis, such as detecting sources in an image or performing a bias subtraction. These are built from the psLib elements, and are also collected into a library called “psModules”. Making use of these two libraries are the top-level analysis programs which are run on the cluster computers. There are also a collection of support functions which interface with the Metadata Database and provide isolation of the database design from the analysis programs. Fig. 2. illustrates the relationship between these three layers of the IPP Software Architecture. The major analysis programs defined by the IPP are:

• ppImage: responsible for all basic single-image analysis, including detrending and invoking the photometry and astrometry analysis.

• ppMerge: responsible for all detrend image stacking operations, including combining raw fringe images

• psPhot: PSF modeling, object detection and classification. The functionality of this program is also available as a library module for use by ppImage.

• psAstro: Astrometric calibration, including retrieval of reference sources from available databases, and simultaneous mosaic solutions. The functionality of this program is also available as a library module for use by ppImage.

• Stac: image warping and combinations. This program is a key element of the Phase 4 analysis.

• POISub: image difference analysis, including PSF-kernel matching using the Alard-Lupton method as an option. 2.5. IPP Milestones Over the next 6 months, the IPP will pass several major software milestones. At these times, major portions of the IPP functionality will be completed. We list below the planned milestones and the corresponding IPP functionality. IPP Release 1 (Sep 22, 2006) : Phase 1-3, Detrend Creation, Infrastructure, ISP Support Release Contents: Analysis programs for Phases 1-3 and the Detrend Creation analysis, PanTasks (the controller/scheduler), PanTasks scripts for Phase 1-3 and Detrend Creation, the Metadata Database tools (ippTools) needed to track the process flow, Nebulous, and DVO. Release Capabilities: Detrend image creation (bias, dark, flat, fringe), image detrending (excluding convolved guide kernels), single-image positive object detection and classification, astrometric calibration, object detection databasing, basic zero-point analysis. Real-time ISP transparency measurements.

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IPP Stage 2 (Oct 30, 2006) : Phase 4, static sky pixel definitions Release Contents: Phase 4 analysis programs, Phase 4 PanTasks scripts, Static Sky pixel layout, interface to Magic, psphot and ppImage upgrades. Release Capabilities: Image warping, Image differencing, Image stacking, object modeling for difference images (loading PSF from an external source, fitting positive and negative sources), analysis of the OTA guide kernel, detrend images convolved with the guiding kernel. IPP Stage 3 (Dec 30, 2006) : Static Sky, AP Catalog tools, output interfaces Release Contents: Updates to psphot; DVO crawler tools (uniphot, relphot, relastro); external interfaces. Release Capabilities: Static sky version of the photometry analysis, with more-complete characterization of galaxy parameters and the simultaneous multi-filter photometry; tools to perform the astrometric and photometric calibration of the all-sky survey data; the interfaces to provide data to the MOPS, PSPS, and other science clients as defined. IPP Stage 4 (Mar 30, 2007) : Code Freeze for Operational Readiness Review and the Survey Start Release Contents: Updates to programs identified in the commissioning. Release Capabilities: Improvements determined on the basis of the commissioning with GPC-1 (possibly new PSPhot PSF models, temperature dependent selects of input detrend data as needed, etc). 3. SOFTWARE DEMONSTRATIONS 3.1. PSPhot In the following section, we illustrate the current status of one of the major IPP components, PSPhot. PSPhot models the image PSF using any of a number of possible analytical models. The parameters of the models are allowed to vary by position in the image to account for variations due to the optics and seeing. PSPhot uses the PSF model to distinguish stellar and non-stellar objects, assigning a probability for each object that the shape is consistent with a PSF. PSPhot measures photometry of objects using the PSF model for stellar sources, and any of several

Fig. 3. PSPhot and SDSS Photo Comparison. Left: M13 photometry with Photo. Right: M13 photometry with PSPhot

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possible alternate models for extended sources. It also measures an aperture correction for each image, and uses the curve of growth of the PSF model to scale all photometry to a common-sized aperture. PSPhot is designed to perform well in crowded fields. It has the ability to detect potentially blended sources and perform a simultaneous fit to a collection of nearby sources. It also uses a two stage detection process, in which a quick, linear subtraction of the PSF model, with position fixed by the object centroids, is used to make the detailed fitting process more robust. Recent tests by the team led by R.H. Lupton have show the efficacy of these approaches. These researchers have used SDSS images of the Galactic plane region in the vicinity of M13 to compare the behavior of PSPhot with the Photo routine used by SDSS (Lupton REF) and the well-used program DAOphot (Stetson REF). Photo was designed to perform well at high-galactic latitude and to measure galaxies well, since SDSS is primarily an extragalactic survey concentrated on the northern Galactic polar cap. However, some observations called the “Transition Strips” have been obtained across the Galactic plane, in order to connect the northern and southern Galactic cap regions. The SDSS group led by R. Lupton intend to use PSPhot for these high-density regions. We illustrate the ability of PSPhot to perform crowded field photometry in regions where Photo performs poorly using preliminary results kindly provided by R.H. Lupton. Fig. 3. shows a pair of color-magnitude diagrams of the region including the cluster M13. The CMD on the left was measured with the SDSS routine Photo, while the one on the right used PSPhot. The giant branch is clearly more ragged and ill-defined in the Photo version of the analysis. Comparisons with DAOphot measurements of the same images show an excellent correspondence, with a slight improvement in the DAOphot photometry over the PSPhot photometry. Lupton and his team are exploring the PSPhot parameters and code details to improve even further the analysis of such crowded fields. 3.3. ISP Image Analysis An important component of the Pan-STARRS PS1 Telescope is the atmospheric transparency monitor called the Imaging Sky Probe. This device uses a 120 mm lens to image a roughly 3 degree field of view, comparable to that

Fig. 4. PSPhot PSF modelling of a g-band ISP image. Left column: input images. Right column: PSF- subtracted images. Top row: data from the upper-right hand corner of the camera. Bottom row: data from the lower-left hand corner of the camera. Note the substantial image PSF variations, which PSPhot models well, yielding only minimal residuals. The remaining objects were below the subtraction theshold for this analysis.

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of the Gigapixel camera of PS1. The field is imaged onto a small (2k square) commercial detector. The goal of the system is to use frequent observations of relatively bright stars to measure the transparency of the atmosphere. The photometry is initially tied to the Tycho and 2MASS photometry of the stars, but is later improved by determining an internal magnitude system for the stars in the appropriate magnitude range. PSPhot will be used to perform the photometric measurement of objects in the images. One interesting issue with the ISP images is that there is substantial image shape variations within individual images in the g-band. PHPhot must successfully model the PSF variations to perform an accurate measurement. Fig. 4. illustrates the ability of PSPhot to perform such an analysis. In this figure, we present an example of two regions of a single ISP g-band image, showing the clear variations in the PSF from one corner of the chip to the other. We also show the image after the PSF model has been measured and subtracted by PSPhot. The small residuals show that the PSF variations are being captured in the PSF model. Remaining objects in the subtraction are mostly below the subtraction threshold; there are also some objects for which the PSF model was a poor fit and which were skipped. 4. SUMMARY The Pan-STARRS PS1 Telescope is nearing operational readiness. The Pan-STARRS PS1 Image Processing Pipeline (IPP) is also nearing operational readiness, and will be completed before the survey operations start. The IPP is designed to be flexible, robust, able to achieve the stringing scientific accuracy requirements for the system, and capable of keeping up with the very high data rate. We have discussed elements of the software infrastructure and the hardware architecture. More complete documentation is available on the Pan-STARRS web site at http://pan-starrs.ifa.hawaii.edu. Testing of portions of the software are underway, and we have illustrated specific examples of the behavior of one of the components, PSPhot. 5. ACKNOWLEDGEMENTS The design and construction of the Panoramic Survey Telescope and Rapid Response System by the University of Hawaii Institute for Astronomy is funded by the United States Air Force Research Laboratory (AFRL, Albuquerque, NM) through grant number F29601-02-1-0268. 6. REFERENCES 1. Lupton Photo REF 2. Stetson DAOphot REF

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Pan-STARRS PS1 Published Science Products Subsystem

J. N. Heasley Pan-STARRS Project, Institute for Astronomy, University of Hawaii

Robert E. Eek, Wayne L. Smith, Julie A. Rosen Science Applications International Corporation

The Pan-STARRS team Pan-STARRS Project, Institute for Astronomy, University of Hawaii

Abstract

This paper describes the requirements and design of the Pan-STARRS PS1Published Science Products Subsystem (PSPS) that constitutes the primary distribution tool for the very large amount of science data products produced by the Pan-STARRS PS1 prototype telescope. The data management challenges are identified in terms of stressing characteristics: dynamic, fast, spatial, and large; these are countered by mitigating characteristics: simple and lenient. This combination of characteristics is not only distinctly more demanding than traditional survey astronomy data managers, but lies at the boundaries of current commercially available data management technology. The requirements imposed on the PSPS result in devising a design strategy at the boundaries of currently available data management technology. In particular, we describe the capabilities and characteristics of the four main PS1 PSPS components: the Web-Based Interface (WBI), the Data Retrieval Layer (DRL), the Object Data Manager (ODM), and the Solar System Data Manager (SSDM). Potential architectural strategies are examined in the context of the stressing and mitigating characteristics with the conclusion that the ODM should follow an architectural concept that emphasizes the pooling of application, processing, and storage resources. The PS1 PSPS is specifically designed to support the PS1 science mission (see Chambers et al., these proceedings) while at the same time providing substantial design direction for a future PSPS component of the final PS4 Pan-STARRS. Finally, the limitations and possible scalability of the PS1 design relative to PS4 are discussed.

1 Introduction The Published Science Products Subsystem (PSPS) within Pan-STARRS serves as the archive and access point for all data products published by the Pan-STARRS system; see Figure 1. The PSPS will receive publications from other Pan-STARRS components, including the Image Processing Pipeline (IPP), the Moving Object Processing System (MOPS), and potential future science processing clients. The PSPS is a multi-tiered system, consisting of the following major components: • Web-Based Interface (WBI), which provides end-user access to the published products; • Data Retrieval Layer (DRL), which integrates all data management components and provides Web Service (i.e., software) access to the published products; and • Data Management Components (DM Components), which manage and provide access to specific collections of published products. The term “published” is important in this context, and needs explicit mention. PSPS will not provide access to all of the data produced or managed within Pan-STARRS, rather only those data products that have been determined to be suitable for publication. It is expected that publication suitability will be determined by a defined data-processing stage, rather than an individual assessment of any particular data product. In other words, data from intermediate steps in a processing pipeline are not likely to be published, and therefore will not be archived within the PSPS.

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Published Science Products Subsystem ( PSPS ) Web- Based Interface (WBI) End Users

T e le s co p e Data Retrieval Layer (DRL) s n o t o h P Object Data Solar System Manager Data Manager ( ODM) (SSDM ) s s d d r r o o c c e e R R

Image Moving Object Gigapixel Processing Processing Im ages Detection Records Camera Pipeline System (IPP ) (MOPS )

Figure 1. Components of the Pan-STARRS System

Due to the technical risks in the development and deployment of the full four-telescope Pan-STARRS, the project is pursuing a reduced-scale prototype to investigate the higher risk components of the full system. This operational prototype is called PS1, and its PSPS development focuses on two data management components:

• The Object Data Manager (ODM), which will manage catalogs of detections and derived celestial objects from the IPP, and • The Solar System Data Manager (SSDM), which will manage catalogs of high-quality orbit determinations and their attributed detections. Other data management components, required for full Pan-STARRS development, were intentionally excluded from the PS1 PSPS in an effort to focus on the high-risk technical issues and reduce financial cost.

The ODM is viewed as the high-risk data management component due to the data entity counts, ingest rates and access patterns (both end-user access and internal processing access). The SSDM is not considered a high-risk component, since it is relatively small and simple. However, implementation of the SSDM component is expected to provide high value for future science with minimal cost. Additionally, implementation of this second data manager component ensures appropriate attention to DRL integration issues.

This paper is intended to provide a concise description of the ODM issues, possible ODM architectures, and the prototype design we have chosen to implement.

2 ODM Published Products

The ODM will archive and provide access to data products that are published from the Image Processing Pipeline (IPP). To understand these products and how they interrelate, we provide a brief introduction to the IPP’s published products.

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2.1 IPP Products

The PS1 ODM will administer published products that are directly produced by the IPP. These products and their attributes are the input data for the ODM.1 As this paper is written, the authors understand that the IPP will publish the following products to PSPS:

• Simplified Frame Metadata: A record for each P2 frame, each P4Δ frame, and possibly one for each Cumulative Sky slice. Important attributes are the sky position, filter and time of the observation (frame). • P2 Detection Records: A record for each celestial object found in each P2 frame. These detection records will arrive from the IPP already associated with their respective frame record. Important attributes of these detection records include the sky position, magnitude (brightness), and associated errors. Additionally, the IPP may provide an identifier for the celestial object itself. • Cumulative Detection Records: A record for each celestial object found in each Cumulative Sky slice. Important attributes of these cumulative detection records are the sky position, magnitude, associated errors, and shape information, as appropriate. • 5σ Delta Detection Records: A record for each difference seen at the 5σ level in each P4Δ frame. These 5σ delta detection records will arrive associated with their respective frame record. Many of these records will not correspond to any known celestial object. Important attributes of these 5σ delta detection records are the sky position, magnitude, and associated errors. • 3σ Delta Detection “Sets”: The collection of all 3σ delta detection records for each P4Δ frame. The limited use of these records, when compared with their large numbers, makes it acceptable to publish them in a single collection; that is, 3σ delta detection records will not be published as individual entities.

Except for the 3σ Delta Detection “sets,” all of these products are simple, relatively small records. This fact is noted here to preface the subsequent discussion of the ODM’s challenges in which the authors focus on the number of objects and their required processing throughput, but not the length (or size) of the individual records.

2.2 Derived Products

The IPP is responsible for most of the computational analysis of the gathered data. This said, the ODM will derive at least one published product based on the information provided by the IPP:

• Celestial Object Records: A record for each celestial object in the sky. The properties of each object will be derived from the set of all detection records associated with the object. Important attributes of these celestial object records include sky position, average magnitude in each filter, proper motion (if appropriate), photometric redshift (if appropriate), and errors or statistics related to the derived properties. This derived product will be generated from the set of all detections of the same celestial object. Every celestial object record will be associated with at least one detection record (of the various forms produced by the IPP), and each detection record will be associated with one (and only one) celestial object record.

The derived attributes of the celestial object record fall into three general categories: 1. Statistical attributes that apply to all celestial objects and require access to the full time-history of detections for that object (e.g., average magnitudes or variances); 2. Attributes that apply to only some of the possible celestial objects, and require access to the full time- history of detections for those objects (e.g., proper motion values); and 3. Attributes that can be calculated from the statistical properties of the objects themselves, without access to the time-history of detections (e.g., colors, photometric redshifts). It is possible that these various attributes can be generated on different schedules, as appropriate.

1 The detailed attributes of these records are not discussed in this paper, but the attributes that indicate important relationships to other entities managed by the ODM are highlighted.

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Table 1. PS1 Published Entities Estimate Record Type 1-Year Total Publish Action Average Rate Frame Metadata 400,000 Add new record 1000 / day P2 Detections 40 billion Add new record 100 million / day Cumulative Detections 40 billion Add new record 100 million / day 5σ Delta Detections 3 billion Add new record 10 million / day 3σ Delta Detection Sets 200,000 Add new record 500 / day Celestial Objects* 7 billion Update existing record 100 million / day * See the discussion on celestial object maintenance in Section 3.5 for an explanation of why this number is not simply the sum of the detection rates.

As more detections of a celestial object occur, the calculated sky position of the object changes to incorporate the evolving information. This “movement” occurs through either the refinement of the average position, or the application of proper motion to the time-history of detection positions. Additionally, it is possible for the time- histories to show that a single object should be split into two objects (or even that two objects should be merged as one).

Although the IPP will be producing a data record each time a celestial object is detected, it does not provide a record for the object itself. Thus, the celestial object record within the ODM must be updated based on the complete set of detections, which are not expected to be maintained by, nor consulted during, the IPP processing.

2.3 Expected PS1 Publication Counts and Rates

The small record size and simple structure of the ODM’s published products are countered by large record counts and high processing rates. For this discussion we reference only order-of-magnitude numbers and rates, employing a single digit of precision. Additional precision on the numbers will not change the underlying nature of the ODM data management problem.

Table 1 enumerates the number of published entities expected to be produced in a single year of operation for PS1. Note that these rates are per day, not per observational night. PS1 is expected to process data every day of the year (within the required availability), even if Pan-STARRS is unable to capture images every night of the year.

A bit of context is required to clarify the organization of these records. For the cumulative detections, there will be a single detection record for each color filter on each celestial object (within a single year). On the other hand, the P2 detections will occur against a smaller set of brighter celestial objects (on the order of one billion objects). These P2 detections will occur several times in each color filter. The 5σ delta detections will usually be associated with a known celestial object, but not always. The 3σ delta detections will not be directly associated with celestial objects within the ODM, since they will not be accessible individually.

The “Publish Action” in Table 1 indicates how the information is required to be integrated with the existing published products. In the case of all IPP-produced entities, the ODM need only add them to the existing information. On the other hand, the ODM-derived celestial object records must be updated when associated detections are published. Naturally, it is necessary to add a new celestial object record the first time an object is detected. After one year, most of the possible celestial objects will have been seen, and a corresponding record will have been created within the ODM. Therefore, the count of celestial objects will remain essentially constant after the first year. However, the counts of the other record types will continue to grow at the same rates.2

2 It is possible that new cumulative detections will replace existing cumulative detections in successive years. This decision has the potential to trade-off initial development complexity for reduced long-term storage requirements (in a long-running PS1 system).

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2.4 ODM Access Patterns

The published ODM products will be accessed in two distinct modes: externally by end-user astronomers3 and internally by the ODM itself. A complete set of end-user access patterns is inherently unforeseeable, since the nature of research is to ask questions that have not been asked before. The internal access patterns are more predictable, since these arise from the anticipated processing required to publish and organize the products themselves, and will be discussed in that context in the next section.

An important feature of the Pan-STARRS survey products is the time-history made possible by repeated observation of the sky. Although the end-user access patterns are difficult to predict, it can be reasonably assumed that access to this time-history will be important. Further, it is likely that the time-history will be accessed on a per-object basis, rather than by location on the sky. That is, end-users are likely to be interested in the time-history of detections on a specific celestial object rather than in the time-history of a general area of the sky.

All of the ODM products have an explicit spatial nature; that is, they are all at some location on the sky. This implies a spatial access pattern, in addition to the temporal access patterns for detections.4 These two access patterns can be combined either directly or indirectly to construct most—possibly all—significant end-user access needs. We provide the following list as a summary of potential access patterns:

1. Time-History Access: Find all detections for a specified celestial object; 2. Simple Temporal Access: Find frames or detections that occurred within a timeframe; 3. Simple Spatial Access: Find frames, detections, or objects that lie within a region of the sky; 4. Simple Spatial-Temporal Access: Find detections that occurred within a timeframe for all objects that lie within a region of the sky; 5. Complex Spatial-Temporal Access: Find frame metadata records or detections that occurred within a timeframe and lie within a region of the sky; and 6. Standard Relational Access: Find frames, detections, or objects that match other attributes, e.g., magnitudes, shapes, color, etc.

We note that it may be possible to rearrange some of the category 5 patterns to fall into category 4 by use of spatial access to the frames that contain the detections. Naturally, the standard relational patterns are expected to be applied in combination with all other patterns.

It is interesting to note that the published IPP data products are provided to the ODM previously organized for certain temporal access. That is, the ODM will receive detection records associated with frames, which have location and time attributes, and the frames generally arrive in chronological order. These explicit spatial-temporal relationships will be available for access, regardless of how the published data are reorganized within the ODM. However, the time-ordered data coming from the IPP will not arrive organized in a manner that readily supports time-history access for individual celestial objects and their associated detections. Reorganizing the products to support both time-history and spatial access patterns is potentially complex, and is the subject of the following section.

3 ODM Ingest Processing Summary

Readers familiar with traditional data management design and development are well aware of the challenges imposed by large record volume. However, the Pan-STARRS ODM also poses a significant challenge in the

3 End-users will access the ODM through the Web-Based Interface (WBI) and Data Retrieval Layer (DRL) components of the PSPS. There will not be any direct end-user access to the ODM component itself. 4 Note that use of the term “spatial” does not imply a specific solution (e.g., a vendor product), but rather that the search is based on multi-dimensional distance calculations instead of the matching of simple keys. There are several promising solutions to this problem available within the commercial and academic arenas.

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processing required to ingest new data records for insertion into appropriate organizations within the data stores. Such insertion processing must keep pace with the data collection and the IPP generation. Of course, these data cannot be write-only records, as the goal of Pan-STARRS is to provide access to scientists for their continuing research. The paragraphs that follow present a discussion of the ingestion needs to be met by the ODM.

Published products will arrive from the IPP organized according to the observational process. That is, the IPP will deliver detections associated with their frames in the chronological order that the frames were taken. The ODM must maintain and update the derived products (i.e., the celestial object records) as new data are published. Additionally, the ODM should organize this information to enable as many access patterns as possible, and to facilitate the most likely patterns. Since keeping pace with the IPP publication rates requires that these internal processes be as efficient as possible, the intent is to optimize the access patterns that are used internally by the ODM to perform its own maintenance and organization. This approach is reinforced by the difficulty in predicting the most likely end-user access patterns, as discussed in the previous section.

For purposes of describing the ODM challenges, we discuss both a physical organization and a logical organization. The physical organization refers to the actual location of data products within the hardware and software of the ODM. The logical organization refers to the techniques and procedures that allow the ODM to know where a product is within the physical organization. The important distinction is that a physical organization implies the existence of limits or boundaries. Additionally, there can be only one physical organization scheme for a product, but there can be multiple logical organizations. For example, the physical organization might involve a number of servers and disks, each with a product capacity limit. This paper does not propose or reject any particular physical organization, but does identify where the existence of boundaries or limits should be considered during architectural design.

What follows is a description of the general data processing required within the ODM to maintain the derived celestial object properties and to organize the IPP data to support this maintenance. The organization process involves the following activities:

• Frame metadata loading, • Detection-to-object correlation, • Detection loading, • Celestial object creation, and • Celestial object maintenance. After describing these organization activities, we discuss how the resulting internal access patterns relate to possible end-user access patterns.

3.1 Frame Metadata Loading

The record data for new frames are added to the existing published records within the ODM. The expected rate is easily handled through traditional techniques, which will allow ready access via both spatial and temporal attributes. This is not likely to be affected by the physical organization, since the total size should fit well within any likely physical boundary or limit. Once a frame record has been loaded, it will not be updated or changed.

3.2 Detection-to-Object Correlation

Upon arrival at the ODM, each new detection record is associated with a (unique) known celestial object, if possible. In the likely case, this correlation is done through a spatial search of existing celestial object records.

If the IPP is able to provide a unique identifier to a known celestial object, then this correlation may be done through a more direct key-based search. In this case, though, the ODM would need to manage the issues that arise when the

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IPP’s internal object records are out of synch with the current ODM object records.5 A frequent feedback from the ODM to the IPP is not permitted, so the need for some amount of spatial detection-to-object correlation will always remain necessary within the ODM.

3.3 Detection Loading

The various detection records for each frame are added to the existing published detection records. The anticipated ingestion rate and total record counts are very high by current technology standards. The records arrive from the IPP physically organized by frame, which provides either a spatial or a temporal organization. To support both types of access—expected for end-user access, and required for internal ODM processing—additional logical organization must be created.

For example, if the detections are physically organized with a spatial arrangement (i.e., put all detections for a region in the same physical boundary), then an additional logical organization must be created which identifies how to access the detections using temporal attributes. For example, the additional logical organization might imply a process to look at every physical storage location for detections that occurred in a given timeframe.

This said, for purposes of maintaining the celestial object properties, it is important to have an organization that provides efficient access to all of the detection records associated with a given celestial object. This celestial object maintenance process is detailed in the following sections.

The good news about the management of these vast numbers of entities is that once detections are loaded (after the correlation process), those records are not likely to be updated or changed. A rare exception would be to change the association with a celestial object, perhaps because additional observations have resulted in a split or merge of celestial objects. Another exception would be the removal of a set of detection records if, for any reason, it was determined that the IPP needed to reprocess those detections and replace them.

3.4 Celestial Object Creation

A new celestial object record must be created for any incoming P2 or cumulative detection that cannot be reliably correlated with an existing object. The frequency of creation of new objects is expected to fall off as the sky is repeatedly observed, countered by the addition of new observable sky as the Earth moves around the Sun. After the first year, the creation of new celestial object records is anticipated to drop to a very low rate. Celestial objects created previously will need to be updated, as described in the following paragraph.

3.5 Celestial Object Maintenance

Ideally, the celestial object properties should be revised every time the object is observed (i.e., a new detection record is associated with the object). For most objects, the first few detection records will provide the most significant changes to the object properties, and most objects will reach a steady state after several successive observations in all filters. Rarely, it may be necessary to revise object properties when detections are removed for reprocessing.

As discussed in Section 2, the various categories of derived attributes may be generated on different schedules, as appropriate to the input data required to calculate the attribute, and the universal nature of the attribute itself. In conjunction with the scheduling of attributes, celestial object maintenance occurs in two distinct modes:

5 Note that any amount of detection-to-object correlation within the IPP may additionally complicate the ODM publication process. The intensive, but simple, spatial correlation process becomes a challenging data cleansing problem, since the IPP may publish correlations based on sky positions that do not match the current state of the ODM’s published celestial objects, whose sky positions are based on the evolving time-history.

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• Visible Sky Maintenance: Roughly half of the sky will be visible on any particular night, and P2 detections could be generated for any object in the visible sky. It is anticipated that only (roughly) 10% of the possible objects will be visible at the magnitudes expected for P2 detections. • Cumulative Sky Maintenance: Each day, a 1° wide slice of sky directly opposite the visible sky will be processed for cumulative detections. The objects in this slice will receive a new detection record for each filter. Regardless of physical organization, maintaining the object properties will be a relatively intensive process. To maintain a celestial object, the full time-history of detections must be gathered and then the latest aggregate properties must be calculated. The existing celestial object record must then be updated, which is always a more expensive action than simply adding a new record.

To mitigate some of the expense, it is possible to defer this processing and do it in batches. By waiting for the arrival of several detections of the object before revising the object properties, we can perform the maintenance in a single step. However, deferred maintenance can only occur when P2 frames overlap within an acceptable deferral period. The nominal daily update rate identified in Table 1 presumes that there is minimal overlap of P2 frames, but that the cumulative sky maintenance is deferred until all filters are loaded.6

The physical organization of the celestial object records must be carefully considered. Since the calculated sky position of a celestial object will be refined by further detections, any physical organization that is spatially-based must be able to handle the relocation of object records. None of the celestial object properties lend themselves well for use in physical organization schemes, since none are immutable (except for a unique identifier).

3.6 ODM Internal Access Patterns

The above paragraphs identified the processing required for the ODM to organize and publish the IPP products. To support this internal processing and meet the anticipated ingestion rates, the following access patterns must be well- supported and efficient:

• Simple spatial access to the celestial objects records, i.e., find objects near a sky position; and • Time-history access to the detections associated with a celestial object; i.e., find all detections associated with a specific object. All end-user access patterns identified in Section 2 are enabled indirectly through the addition of standard relational access support (e.g., find all detections associated with a specific frame) and spatial access to the frames themselves. Although the complex spatial-temporal end-user access patterns may not be as efficient as those patterns that are directly optimized for ODM ingest processing, they certainly are achievable.

4 Unique Characteristics of the PSPS ODM

The above paragraphs present an introduction to the ODM issues that must be addressed in the PS1 operational prototype. In this section, we highlight the characteristics that make the ODM different from extant systems— especially extant astronomy survey systems—that might initially appear comparable. Some of these characteristics increase the challenges posed by the ODM, and some simplify the challenges. All of these characteristics are important factors when discriminating possible solutions for the ODM design. These unique characteristics are:

Fast: Near real-time ingest rates,

Simple: Simple entities and relationships,

6 That is, the object update rate is the sum of the P2 detection rate, the cumulative detection rate divided by the number of filters, and the non-noise 5σ Delta detection rate. The values in Table 1 are rounded to a single digit of precision.

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Spatial: Multi-dimensional distance calculations,

Large: Massive number of managed entities, and

Lenient: Academic and prototype features.

It should be noted that there are existing commercial, government, and academic data management systems that possess several of these characteristics. However, the PS1 ODM appears to be the first system planned to “enjoy” all of them, especially to the expected degree. Each of these characteristics will be detailed below.

4.1 Fast

The ODM needs to operate at near real-time ingest rates. In other words, the ODM cannot fall too far behind the IPP’s generation of published products. This means, on average, that the ODM must add 200 million various detection records and update 100 million celestial object records per day.

These rates are comparable to those seen in telecommunications call-detail data stores. However, the ODM must perform some form of a correlation search to associate each detection record with the appropriate celestial object, while the telecommunications systems simply append new records.7 Each update of a celestial object requires a full time-history search of all detections associated with the object. The ODM’s ingestion rates are unique among published astronomy archives, which have not been required to concurrently perform collection, data processing, and data management for publication purposes.

4.2 Simple

The published entities themselves are relatively simple, with few attributes and uncomplicated relationships to other entities (once the correlations have been performed). The possible exception to this is the 3σ Delta Detection sets, which may ultimately require some form of “unpacking” to access their internal detection records. The number of these sets is relatively small compared with object and detection counts. Additionally, the relationships to the sets themselves are very simple: a one-to-one relationship exists between a P4Δ frame record and a 3σ Delta Detection set.

Another aspect of this characteristic is the fact that detection records can generally be considered immutable once they have been correlated to their appropriate celestial object. This can allow for significant simplifications in the physical and logical organization of these records. Although the ingestion rate of these records is unusual for astronomy projects, ODM data records are a bit simpler—and hence smaller—than those stored in many extant astronomy archives.

4.3 Spatial

All of the ODM published products include an explicit position on the sky. This requires robust support for multi- dimensional distance and area evaluations on the surface of a sphere. Such evaluations cannot be supported by traditional relational tools, which are generally key-based and perform their search through equality or range comparisons on a single attribute. However, several solutions (commercial and custom-developed) exist that address this need. The key factor is that all of these solutions require significantly more processing capability than a simple relational access.

4.4 Large

The ODM will manage a massive number of individual entities. The record counts for a single year of PS1 operation approach 100 billion. Regardless of the physical and logical organization, this record count is certainly at the cutting edge of data management technology and far exceeds extant astronomy survey projects.

7 This correlation search will usually include a spatial search of the known celestial objects within the ODM.

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4.5 Lenient

Mitigating the impact of some of the ODM’s stressing characteristics is the fact that the PS1 ODM is an operational prototype of an academic research tool. It will be neither a high-availability commercial business-enabling system nor a high-volume/high-performance intelligence analysis system.

The operational prototype nature of PS1 allows for certain simplifications. For example, the PS1 IPP is expected to maintain all images and intermediate products for the duration of the PS1 operational life. This prototype flexibility allows the ODM to avoid expensive reliability and backup solutions by simply asking the IPP to re-publish lost data.

The academic/research nature of the full Pan-STARRS system, as well as the PS1 operational prototype itself, allows for significant cost savings in the area of availability. The availability requirements for the ODM are significantly lower than would be expected in a commercial or public-sector application. In fact, as long as the ingest processing is able to continue, time-consuming repair actions on large portions of the physical organization can be tolerated during operation.

5 Distribution of Resources

In an ideal world, a computing problem could be solved using a single set of storage, processing, and application resources. Unfortunately, many problems exceed the physical or logical boundaries of such components. When that occurs, the problem must be partitioned in some manner to fit within those boundaries. When data volume exceeds the size of a single storage device, records must be split among files on different devices, or multiple devices must be aggregated to appear as a single, larger storage device. When computational requirements exceed the capacity of a single processor, processing must be split among multiple processors to “divide and conquer” the problem. Applications, in turn, are partitioned to allow their use of processing and storage to fit within the physical and logical boundaries of the other resources.

The decision to distribute a resource is often a simple choice based on the problem being solved and the technologies available and/or affordable. Any data management system of significant size will require distribution of data among storage resources. It is also likely to require distribution of data processing among processors, and may require distribution among applications. The complexity of managing and using the distributed resources will vary with the degree of distribution and the level of support for resource distribution that is provided by the underlying technologies.

Scalability issues are often behind decisions to distribute resources. In addition to exceeding storage boundaries, large data management problems are often constrained by other boundaries, such as the address limits of an application or the memory within a processing node. Partitioning this type of problem across boundaries takes two forms:

• Scaling Up: Scaling up increases the resources available within a single component. Examples include adding CPUs or memory to a processing node, or adding disks to a storage array. • Scaling Out: Scaling out adds extra components to provide additional resources. Examples include adding processing nodes to a compute cluster, or standing up additional web servers in an e-commerce site. Each scaling method is best suited to certain types of problems, and neither is without limitations. At some point, a node runs out of room for memory or processors. Similarly, a cluster will run out of interconnect bandwidth at some number of nodes.

When system developers choose to distribute a problem across resources, then they must decide how the information being processed will be partitioned across the boundaries of those resources. This decision requires choosing a physical organization for that information. A good organization will optimize the use of available resources; a bad organization results in consumption of additional resources. In determining the organization, developers desire to minimize four factors:

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• Management Communication: Reduce the control information that must be exchanged among resources to synchronize their activities, • Data Movement: Avoid moving data from one resource to another, • Interference: Avoid resource contention where processes have to wait for others to finish an activity before they can continue, and • Load Imbalance: Avoid hot spots or idle resources. Minimizing these organizational factors in the context of a real problem, including factors such as allowable complexity and budget, requires the development of an architectural strategy. Architectural strategies are enumerated in the following section.

6 Architectural Strategies

Architectural strategies for modern data management systems can be described in terms of resource distribution and pooling choices. The level of distribution of a resource indicates a decision to partition a problem into pieces, either to address a resource limitation or to make the problem more tractable. The level of pooling, or virtualization, of a resource indicates a decision to combine results from, to balance the usage of, or to streamline the management of that distributed resource. Resource pooling often entails increases in technological complexity, so the decisions should be made consciously, and in proportional response to the problem being solved.

As discussed in the previous section, the decision to distribute resources will often follow directly from the nature of the problem and the technologies available to address that problem. The decision to pool those distributed resources, however, is less straightforward. In the subsequent paragraphs, we introduce three orthogonal classes of resource pooling, describing the benefits and disadvantages afforded by various pooling decisions in each class. We then use these three classes to describe the set of possible architectural strategies.

6.1 Resource Distribution and Pooling

Data management systems require distribution and pooling decisions for three resource classes:

• Application Pooling: Software subsystems range from independent applications that require end-user integration of input and results, to clustered operating system and distributed application options, including federated, independent databases. • Processor Pooling: Processor subsystems range from independent (share-nothing) processing nodes, through clustered computer options, to large symmetric multiprocessor (SMP) options. • Storage Pooling: Storage subsystems range from internal direct-attached disks, through external disk subsystems, to various networked storage options, such as network attached storage (NAS) or storage area networks (SAN). These three resource pooling classes are discussed independently in the subsequent sections. The benefits and consequences of pooling decisions within each class follow.

6.1.1 Application Pooling

Application distribution divides a problem among multiple instances of the same application.8 Application distribu- tion can be used to avoid scalability issues with a single instance, or to improve performance for geographically dispersed users. Application pooling combines multiple instances of the same application to present unified results to the end-user or software clients. Pooling may be used to simplify users’ requests for data, or for load-balancing between instances when user or system workloads vary. Pooling application instances across multiple sets of

8 This is different from application pipelining where a problem is partitioned into different (sequential) stages that are handled by separate applications.

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Designated Utility Agent Computing g n i l o o P

Collaborating r Matrixed o

Agents s Resourcing s e c o r P

Storage Pooling Independent Storage Bricks Broker g n li o o P n o ti a c li p p Federated A Drone Bricks Agents

Figure 2. Decision Space for Architectural Strategies

hardware may increase availability through fail-over, or allow multiple small boxes to solve a large problem at less cost than a single large box.

On the down side, pooling adds application complexity. If the application instances are completely independent, then users must know which one contains data of interest. When pooled, a process must orchestrate the workload through the separate instances so they appear as a concerted system. If data are visible only within an instance, then load balancing across instances depends upon data access patterns matching the data distribution scheme. Data can be made visible across instances to improve load balancing, but some process must then coordinate reads and writes to ensure data consistency.

At one pooling extreme, a data management system might be comprised of a number of independent data managers, each responding directly to requests for the data they contain. The other extreme might be a large corporate data warehouse or e-commerce system with many concurrent users connecting to a single application.

6.1.2 Processor Distribution and Pooling

Processor distribution divides a problem among multiple CPUs and memory. This provides computational throughput by splitting the work among multiple inexpensive processors rather than using a single high-performance (and high priced) one. Distribution can avoid scalability issues with a single processor system (e.g., a mainframe) and enable load balancing between processors. Processor pooling coordinates the work of multiple CPUs and memory so that their work stays synchronized. Pooling processors across multiple sets of hardware may increase availability, through fail-over, or support additional load balancing. Recently introduced technologies, such as virtual machines and blade servers, allow dynamic allocation of resources to processing pools.

The primary concerns with processor pooling are: (i) the degree of parallelism achievable, given the problem being solved, and (ii) the ability of the processing scheduler to allocate work efficiently. In systems with independent processors and no scheduler, it is likely that some processors would be idle while others were overloaded. The problem is compounded if the data under management were visible only to the local processors. On the other end, a single multi-processor computer can have internal complexity with memory allocation or inter-process communications that limit the number of processors that can be installed effectively.

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As example, one pooling extreme might be a set of PCs independently running a Monte Carlo simulation with different parameters. The other extreme might be a high-CPU count system used for computer generated animation.

6.1.3 Storage Distribution and Pooling

Storage distribution divides data among storage devices. This allows management of large volumes of data. Storage pooling aggregates storage devices to make them accessible to the applications and processors. Storage pooling improves throughput and load balancing with multiple I/O controllers working in parallel, and by striping data across multiple storage devices. Storage manager software supports the creation of logical drives larger than a single device, which simplifies management as well as supporting growth (i.e., provisioning) over time. These software managers also help protect against device failures by using redundancy techniques. Allowing access to pooled storage by multiple processors and applications through some form of network connection facilitates load balancing of data requests and permits device consolidation.

With independent storage devices, applications access each physical device directly. However, independent storage devices limit file sizes. A storage manager is desirable to mitigate this limitation and can protect against device failure. Using local storage, even with load balancing, constrains data organization to match access patterns. Mismatched growth patterns may require extra storage in some local stores, while others have unused capacity.

A management process and some form of network connectivity are required to make storage visible to multiple processing subsystems, and to manage their read/write requests. This connectivity, in turn, requires more complex storage management software.

A workgroup network, with no file server, is an example of an architecture with no storage pooling. Files are stored locally on each computer, requiring users to access data files from separate computer file systems. At the other extreme one might find a large corporate data center with multiple servers sharing a large, centrally managed pool of storage.

6.2 Possible Strategies

Any complete architectural solution must consider the interactions between the three resource pools in the context of the problem being solved. To discuss the range of architectural strategies, though, we can consider these pooling decisions to be independent.

If we consider each pooling decision as an independent axis, the exhaustive set of possible architectural strategies can be described using the cube in Figure 2. The vertices of the cube indicate the extreme combinations of all-or- nothing resource pooling. Each vertex is a viable architectural strategy, and problems exist that can best be solved by each of those strategies. The strategies are enumerated below:

• Independent Bricks: “Fat bricks” without a federated data manager. The user knows which machine to access and then integrates results from multiple machines, if necessary. • Federated Bricks: “Fat bricks” with a federated data manager. The user submits a single query and the results will be integrated before returning. Example: Sloan Digital Sky Survey. • Designated Agent: A compute cluster with direct attached disks and individual applications, or a large multi-user Symmetric Multi-Processor (SMP) system. Example: Hosted J2EE web applications • Storage Broker: Independent processors without a federated data manager connected to a SAN or networked file system. Example: Storage Resource Broker (www.sdsc.edu/srb). • Collaborating Agents: A compute cluster with direct attached disks, but using a single entry-point application. Example: SETI@Home. • Drone Agents: Independent processors connected to a SAN (or analog), all working on the same problem. Example: Human Genome Project.

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• Utility Computing: A “grid” data center: Virtualized storage and processors, but multiple applications serving various needs. Example: Corporate Management Information Systems (MIS); Smallpox Research Grid Project. • Matrixed Resourcing: A compute cluster with federated data management and virtualized storage, working on a single problem. Example: AT&T Call Detail Warehouse. Each of these strategies is a viable approach for certain problems. The names are notional, and are intended to evoke the underlying concept of each strategy. The next section examines candidate technologies available to implement these strategies.

7 Available Technologies

Architectural strategies are implemented by assembling components into a data management system. The principal components of a data management system can be thought of as layers. The layers defined in this section are generic, and no specific technology or implementation is assumed.9 However, decisions of budget allocation and vendor selection depend on consideration of candidate components/layers. As a result, we provide a brief introduction to current technologies available to implement an architectural strategy in each of the following layers:

• Application Layer • Processing Layer • Storage Layer Although all layers must be represented in a data management system, individual components might be implemented in a number of ways, depending on the architectural strategy selected. The following paragraphs provide a definition of each layer and list the range of technology products that exist for that layer.

7.1 Application Layer

The application layer is responsible for efficiently storing and organizing records, retrieving them and managing their updates. This layer runs on the computing resources in the processing layer. As multiple processes are likely to be accessing data simultaneously, the data manager must ensure the integrity of concurrent read, insert, update, and delete operations. While one implementation of this layer might be a Database Management System (DBMS), this layer could also be implemented as a file system with scripts to search its files, or a mix of the two (or something else entirely). Nothing presumes this layer is a single process or instance. It could be a group of separate data managers working independently, or coordinated in some manner. The application interconnect is used to move control information and data among instances in the application layer. Federated or clustered databases use their interconnect to de-conflict reads and writes and to move results sets across instances. A single instance database would not need an interconnect, since that function is handled by its internal scheduler. Relevant technologies in the application layer are file systems and Database Management Systems. File systems handle shared access to files. A DBMS handles shared access to records stored within files or on “raw” devices assigned to the DBMS. Types of file system include single computer systems, network file systems supporting shared access across networks, clustered file systems supporting applications doing concurrent read and write, and hierarchical file systems that extend functionality to non-disk media, such as tape or optical, to reduce the cost of retaining infrequently accessed data. Both files systems and DBMSs support shared access to data. With a file system, files are generally shared for reading, but locked when a user or process is writing or updating the file. With a DBMS, locking occurs at the record level.

9 We note here that our Pan-STARRS efforts are in the preliminary design phase, and as such this paper does not address the technology “solution space,” rather only alternate architectural candidates currently under investigation.

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7.2 Processing Layer

The processing layer of a data management system contains the computing power (CPU and memory) and operating system that the data management processing requires. This layer is responsible for supporting the data manager’s processing, and for ensuring the stability of active processes. In this context, there is no presumption regarding the number of processors, how they are connected to memory, nor how these processors interact (i.e., communicate) with the storage layer.

The processor interconnect is used to move control information among nodes in the processing layer. For a single node, its interconnect might be the node’s internal cards and cabling. For example, an SMP computer might use its backplane to pass control information among its processors and memory to implement the processor interconnect.

The processing layer’s components are CPUs and memory within some number of processing nodes. These processing nodes could range from single CPU machines through small Symmetric Multi-Processor (SMP) computers to enterprise-class SMP systems or mainframes. Virtual machines, a software technology that allows multiple operating system images to run on a single node, are also candidate implementations for this layer. A recently introduced technology called Blade computing allows hardware clustering to dynamically allocate processors and memory to virtual machines. Processor interconnect products include cabling, switches, hubs, and routers. A number of protocols exist for networking processors. These include Ethernet, Gigabit Ethernet, and Infiniband, among others.

7.3 Storage Layer

The storage layer of a data management system contains the storage devices and their controllers. It consists of a combination of direct-access and sequential-access storage devices. This layer might include either internal or external storage devices.

The storage interconnect is used to move data between the processing layer and the storage layer. The SMP computer, identified in a previous example, could use the same backplane as the processor interconnect to access locally installed disk drives, or it might use network interface cards and cabling to external storage.

Technologies in the storage layer include storage devices, controllers, and interface devices. Storage devices of interest include magnetic disk and tape. Disk and tape drives come in a variety of sizes and support various serial and parallel interfaces. For both technologies, access performance declines as the capacity goes up. Controller and interface technologies include direct disk or RAID for internal storage and network interface cards or host bus adapters for external storage. The storage devices themselves can be installed internal to processing nodes, or in external storage arrays and tape libraries. Storage interconnect products include the same components and protocols that are available for the processor interconnect. This interconnect can also use block mode protocols, such as iSCSI and Fibre Channel, which may provide improved throughput.

8 ODM Characteristic

Support of ODM functions will require efficient spatial and time-history access to published products, both of which are expected to be important end-user access patterns as well.

Several characteristics are highlighted that distinguish the ODM from existing systems. All of these characteristics are important factors when evaluating architectures for the ODM. Taken individually, the stressing characteristics identified above (dynamic, fast, spatial and large) are not uncommon in the data management domain. The combination of all of these characteristics, however, poses a particularly challenging problem. The interaction of certain characteristics leads to obvious tension (e.g., fast and large, dynamic and fast). Fortunately, the mitigating characteristics (simple and lenient) can reduce the problem, and potentially lower the cost of possible solutions.

The combination of the spatial and large characteristics likely implies a physical organization that is spatial in nature, but care must be taken to manage the complexity of the logical organization schemes that will be required to

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Table 2. Optimization Goals for the ODM Activity Optimization Goal Ingest Sustain constant ingest rate as total record count increases. Ingest Scale storage linearly with the total record count. Ingest Support increased ingest rate through linear addition of processing hardware. Query Support increased user query load through linear addition of processing hardware. Ingest/Query Support the addition of storage hardware without disruption. Ingest/Query Support the addition of processing hardware without significant disruption. Ingest/Query Support variable ingest and query load balances without introducing delays. Query Constant query performance on object records. Query Linear query performance on detection records proportional to the number of records retrieved. Query Support query patterns for objects and detections as they are observed during operations. Query Allow tuning of storage access based upon observed end-user access patterns.

support any physical organization. For example, the difficulty of managing overlapping areas or duplicated records must be properly considered during design.

Taken individually, the stressing characteristics (fast, spatial and large) are not uncommon in the data management domain. The combination of all of these characteristics, however, poses a particularly challenging problem for the ODM. The interaction of certain characteristics leads to obvious tension, e.g., fast and large, dynamic and fast. Fortunately, the mitigating characteristics (simple and lenient) can reduce the problem, and potentially lower the cost of possible solutions.

8.1 ODM Desired Behavior

Before evaluating architectural alternatives, it is important to consider the desired behavior of the system. The following paragraphs address this in the context of: (i) the ideal features that would enable the ODM to perform its job effectively, (ii) the technical risks that we foresee (and which must be mitigated), and (iii) the ODM characteristics to be optimized. These three qualities can be described in terms of goals:

• Feature Goals: Data access is the principal function of the ODM. It must be able to support access efficiently, which means being able to organize data for efficient ingest and query. The ODM should achieve optimum use of its resources, especially processing and storage. The configuration must be manageable without extraordinary efforts by Pan-STARRS administrators. In order to reduce initial costs, ODM storage must be able to grow incrementally as data are collected over time. The ODM implementation should minimize the demands it makes on infrastructure, such as space, power, and cooling. Finally, as with all system development efforts, the authors are especially cognizant of the project’s budgetary constraints, and desire to develop and deploy the ODM at a reasonable price consistent with getting its job done. • Risk Mitigation Goals: Some aspects of the ODM pose technical risks to be mitigated. First, the number of records to be managed is extremely large. Second, the resulting volume of storage may strain the data management and processing layers’ capabilities to access that storage. Last, given the size and potential complexity of this system the authors desire to assure a level of availability consistent with implementing the ODM at a reasonable cost. Without such assurance, a significant amount of time and money might be expended during the ODM’s operational period. • Optimization Goals: Given alternatives that have the desirable features, as well as mitigate the risks, then the qualities to be optimized must be considered. These qualities are enumerated in Table 2, both for ingest processing and end-user query activities. It is noted that these are target goals for ODM behavior, not system requirements. These are the qualities desired to be achieved when considering architectural alternatives, and they must be traded with cost and complexity factors.

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9 ODM Architectural Implications

The characteristics and desired behavior of the ODM inform architectural decisions. The first of these decisions relates to the distribution of resources. The volume of storage required will certainly exceed the physical limits of any available storage devices, implying a distributed storage solution. At the same time, the required high processing rates indicate a distributed processing solution. An architectural strategy must be chosen to handle these distributed resources, and component features must be considered.

Architectural components of a system are selected to support the chosen pooling strategy, optimize the desired behavior, and minimize cost and complexity. The stressing and mitigating characteristics of the ODM are reiterated in Table 3, along with their implications for the component layers. The following paragraphs present these implications in the context of pooling strategies for the ODM.

9.1 ODM Resource Pooling Strategies

Pooling decisions can be made independently for each of the data management resources. The pooling implications for each resource are discussed in the context of the ODM characteristics and desired behavior.

9.1.1 Application Pooling Implications

The data management application pool should appear to be a single data manager. Query splitting and results aggregation should be invisible to the end-user. Similarly, the detection load and object maintenance processes should require neither access to, nor information about, the internal organization of data handled by the data manager. This isolation allows the data management application to be restructured or reorganized without outside impact.7

Operations should be highly parallelized within the data management application. Ingest processing and object maintenance each require a significant amount of storage interaction. To increase their performance, the data manager must be able to partition, and isolate, ingest and update operations for data representing different parts of the sky. This allows processes to run in parallel without contending for the same resources. As a result, the data management application should organize data spatially to minimize the I/O required to support object updates.

An optimal configuration includes data that are globally visible to the management application instances. In this case, each instance could process data in any region to support load balancing. This configuration would also reduce complexity by reducing the required number of data management instances, thus minimizing coordination among instances. However, this configuration may conflict with the partitioning and isolation needed to support parallelization. If data are to be partitioned among independent data management instances, then an optimal design must avoid duplicating or replicating data across instances. Replication adds significant complexity and workload in order to keep copies consistent, creates opportunity to introduce errors, and increases the volume of data that must be managed.

An attractive application pooling approach would include a single data manager instance, or a small number of instances clustered together. Data would be globally visible to the application or cluster, so that any instance can service any request. The data manager would have internal capabilities to support partitioning, parallelization, and isolation with respect to processor and storage utilization. The data manager would be able to organize data to minimize I/O operations.

9.1.2 Processor Pooling Implications

A goal in pooling processors is to spread the work-load across all processors as evenly as possible, in order to reduce the number of processors in the system. With respect to ingest and update—the ODM’s most challenging functions —multiple, parallel processing threads are essential to achieve the necessary throughput.

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The allocation of work to processors should be invisible within the processing pool. Neither the application pool, nor the storage pool, is required to be aware of processor assignments. This transparency facilitates dynamic allocation of processors and keeps the layers isolated from each other.

It is reasonable to assume that some number of processors will be aggregated into nodes, which in turn are aggregated to achieve the processing power required. This approach optimizes inter-processor communications through faster internal communications paths without placing too many processors in a node and thereby exceeding the node’s effective capacity.

For the configuration to be efficient, the processing nodes must be sized to minimize workload caused by cross-node communications and minimize data movement among nodes. Ideally, each processor would be able to see data globally. Alternatively, if processors in the pool could only see data for a portion of the sky, then those processors would be idle when data in that region was not being processed. In this sub-optimal case, data must be moved between nodes to put those idle processors to work. Such manipulation increases the bandwidth requirement on the processing node interconnect, and could slow down the overall system.

A desirable processor pooling approach includes a number of processors in multiple nodes that are clustered together. Data would be globally accessible for all processors, so any processor would have access data without going through another processing node.

Table 3. Implications on ODM Architectural Pools

Characteristic Application Pool Processor Pool Storage Pool

Fast • Ingest at required rates • Allocate workload across all • Optimize organization to • Parallel threading and processing resources to meet minimize I/O operations isolation processing complexity needs used to correlate detections with minimum components to objects and to retrieve • Prevent management time-history of detections for overhead from slowing an object ingest Simple • Data structures amenable to • Efficiently support relational DBMS storage/retrieval of small • Take advantage of read- records only status to reduce • Small records size suggests system overhead that high bandwidth • Store small records interconnect may not be efficiently needed throughout

Spatial • Support spatial correlation • Parallel threading required and query Large • Manage number of records • Enough sophistication to • High number of devices in • Capable of partitioning address the volume of the system suggests some records in some manner to storage under management reliability features may be support scale out rather • Storage interconnect able to needed to keep up long than scale up scale out to the full volume enough to sustain ingest of data without bottlenecks Lenient • Can accept process failures, • Highly available, never-fail • Highly redundant storage is node outages, and thread components are not needed not needed failures as long as recovery • Neither are highly redundant • Slower, less expensive brings data to a consistent components devices may be appropriate state for detection record storage • Can reduce end-user query • Interconnect failover through to support ingest redundant paths not needed processing

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9.1.3 Storage Pooling Implications

The storage pool must grow over the life of the system. The vast majority of celestial object records will be created early during the operational period, and their numbers will remain fairly constant thereafter. However, detections arrive to the ODM in more-or-less a linear fashion over the life of the system. Rather than pre-configuring the ODM with the full quantity of storage that is likely to be filled only after two years of operation, storage should be added only as the volume of detection records increases. To minimize disruption to system operations, these storage increments must be added without reconfiguring processing or applications.

Detection records should be organized spatially, rather than by frame, to speed up object record maintenance. When updating an object record’s aggregate properties, its time history of detections must be retrieved from the ODM. Fewer I/O operations are needed if those detections are co-located in storage. However, this need implies that the growth in storage, assuming the data are partitioned by regions on the sky, will not be evenly distributed over time. That ramification further implies a storage pooling approach that isolates storage from applications and processors.

The storage pool should be visible to multiple processors/applications for load balancing. At the same time, I/O activities to limit storage contention should be isolated. The storage pool must support storage partitioning to isolate the I/O activities of the other pools. It should also support striping across devices and controllers to improve throughput and reduce device contention.

The storage pool should be able to recover from errors or media failures with minimal disruption to operations. While the overall availability requirement on the ODM is relaxed for prototype deployment, frequent outages to replace and rebuild failed storage devices will tax the end-users’ patience and introduce some risk of permanent data loss. Additionally, excessive outages may require reprocessing of published products, which could prevent the ODM from meeting the necessary ingest rates. Accordingly, some level of data protection is appropriate for the ODM.

Pooling of different types of storage is also desirable. Object records are accessed frequently, detection records less often, and 3 difference sets rarely. Use of lower cost, higher density storage for less-accessed items will reduce the number of storage devices and the overall cost of the ODM.

A desirable storage pooling approach is to use external storage to allow incremental addition of capacity without disruption to applications or processors. This configuration would be able to aggregate and virtualize physical devices, further reducing disruption to other components of the ODM, while storage is reconfigured.

10 PS1 Prototype Design

Resource pooling provides design advantages since it is a strategy that relegates responsibilities to sets of resources capable of dividing and conquering a data management problem. Pools are not bound by the same scaling limitations as individual components. Application pooling, for example, can be extended by adding more processes to existing hosts, while widening the load balancing queue. Additionally, pools do not require homogeneous specification. Larger boxes might be added to a processing pool to concurrently service different sets of problems— such as the ODM’s ingest correlation—while lesser hosts pool together to service query processing.

Resource pools can also be upgraded with technology refreshes without losing legacy investment. For example, storage pools can be upgraded by disk sizes, types, and speeds within the existing environment. The pooling strategy empowers design opportunities throughout the application architecture and data lifecycle.

The analysis of the Pan-STARRS PS1 ODM challenges clearly indicates an architectural strategy that strives to pool all three data management resources. Pooling of storage resources supports the large, fast, and dynamic nature of the system, allowing the storage to be provisioned incrementally. Pooling of processing resources can significantly reduce the resource requirements by allowing all processors to support any activity. Finally, pooling of application resources will greatly simplify query processing, can reduce storage requirements by reducing boundary data replication, and can significantly reduce I/O requirements.

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Architectural pools provide design strategies that support a balanced architecture with a flexible, scalable, and optimized approach for Pan-STARRS’s PS1 Object Data Manager. The schematic design we have adopted for the PS1 ODM is shown in Figure 3. In this design, we have split the primary functions of the ODM—data ingest and query response—into autonomous cooperating subsystems. The data ingest for the ODM takes place in the “left brain” subsystem over the course of the monthly cycle during which the IPP publishes its products to the PSPS. While the ingest subsystem is performing those operations necessary to incorporate the new detections into the database and update celestial object records, all the data that has already been published and ingested into the ODM is available for astronomical queries via the “right brain” side of the system. When the ingest side has finished preparing the detections for input into the ODM and updating object records, the updated objects are copied to the right brain and the new detections are made accessible for queries. While the ingest processing is taking place on the left brain, we plan to run Published Data Clients (PDCs) on the right brain in addition to serving user queries. One use of a PDC will be to examine, and if necessary, refine detection-object associations made during the ingest processing. Any corrections the PDC might generate to these associations would be fed back to the left brain for updating in the next monthly update cycle. Other PDCs might perform astrometric analyses of the detections, compute photometric redshifts, etc. The PDC will also be a mechanism available to the database users; however only those PDCs that are part of the ODM will be able to update the database itself.

The ingest processing server will be handled by a single multi-processor Enterprise class server machine. For PS1 our baseline configuration for this subsystem is a server with 16 sockets populated with Itanium dual core processors. To allow for potential growth, the system will be expandable to a maximum of 32 sockets as a single server. The anticipated hardware will support 128 GB of RAM and 8 host bus adapter (HBA) ports connected to the data storage area network (SAN).

The initial deployment of query servers for PS1 will consist of two quad-socket servers using dual core Xeon processors. Each processor will have 16 GB of RAM and 2 HBAs.

Fig 3. Preliminary Design Layout for the PS-1 ODM

The disk storage for the ODM will employ a pooled strategy, with both high performance storage (10-15K drives) for storing the celestial objects, and lower performance high-capacity disks for storing the published detections. At deployment, we plan to have 10 TB of storage allocated for the celestial objects and 100 TB of storage available to hold the detections. As indicated by the blank slots on the right side of Figure 3, the detection storage will not be fully populated when the system becomes operational, but can grow as the demand requires. Our current planning for the PS1 mission is to provide up to 420 TB of storage for the detections.

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One final note about our design deserves comment with regard to the storage, namely that we will have a copy of the celestial objects available on both sides of the ODM “brain.” This is done so that the ingest preparation does not impact the query performance seen by the ODM users, and to provide a snapshot backup of the objects should one or the other side experience storage failures for the objects.

11 Scaling the PS1 Design to PS4

The PS1 system was originally proposed at a risk assessment and reduction activity. The ODM was definitely considered to be one of the major risks involved in Pan-STARRS by those recommending that PS1 be undertaken prior to building the full PS4 system. While PS1 is a quarter-scale system in comparison to PS4, its database will be a major leap over those currently representing the “state of the art” in astronomical databases, e.g., the 2MASS database or the Sloan Digital Sky Survey (SDSS). These systems have ~1-2 billion entries stored in roughly that same number of rows. We estimate PS1 will need to keep track of ~10 times more objects, and will ingest per month as many rows as currently contained in either the 2MASS or SDSS databases, resulting, at the end of the PS1 operations, in a database with over 40 times more rows that the current systems.

We are confident the design we are planning for PS1 will meet the requirements for the PS1 mission, both as a test bed for the database development effort and as a scientific tool for members of the PS1 science team as well as those of the PS1 Science Consortium being formed to support the PS1 science programs. Given that PS4 will need to load ~4 times more detections per second and maintain object information on perhaps ~2-4 times more celestial objects than PS1, if the PS1 design can’t scale up, it can certainly scale out by simply replicating the PS1 configuration 4 or more times. And, while it may seem straightforward, the primary challenge will be to do so in a way that constrains the total system costs as much as possible.

A major thrust of our design effort has been directed to the data ingest problem. Pan-STARRS will be the first major survey program to open up the study of the time-domain in astronomy on large scales. Other projects, such as the Large Synoptic Survey Telescope, will follow on this path. If we are unable to make these data available to users in a “reasonable” period of time without falling behind the data production at the telescope and the IPP, we have essentially failed to attain one of our primary goals. A fact of life, however, is that the users only see system performance from their perspective, i.e., how fast can it give me the information I asked for?

While the total user base of the PS1 Science Consortium is currently unknown, it should easily be sufficient to develop a profile of the usage patterns in the ODM. Our architectural design should allow for straightforward expansion on the query side of the ODM brain at very moderate cost should the initial hardware deployment be inadequate to meet the needs of the user community. Establishing how astronomers want to search the data is essential to taking the PS1 design to the next stage in that we can start to understand tradeoffs in use and design that drive the system cost. Acknowledgments The authors wish to thank contributors and reviewers of this paper, and our colleagues on the Pan-STARRS project. In particular, we appreciate the insights and suggestions offered Messrs. Christopher Argauer and Bernard C. Cotton.

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