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Register a new userPan-STARRS PSF-Matching for Subtraction and Stacking
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We present the implementation and use of algorithms for matching point-spread functions (PSFs) within the Pan-STARRS Image Processing Pipeline (IPP). PSF-matching is an essential part of the IPP for the detection of supernovae and asteroids, but it is also used to homogenize the PSF of inputs to stacks, resulting in improved photometric precision compared to regular coaddition, especially in data with a high masked fraction. We report our experience in constructing and operating the image subtraction pipeline, and make recommendations about particular basis functions for constructing the PSF-matching convolution kernel, determining a suitable kernel, parallelisation and quality metrics. We introduce a method for reliably tracking the noise in an image throughout the pipeline, using the combination of a variance map and a `covariance pseudo-matrix. We demonstrate these algorithms with examples from both simulations and actual data from the Pan-STARRS1 telescope.
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The Pan-STARRS1 Science Consortium have carried out a set of imaging surveys using the 1.4 giga-pixel GPC1 camera on the PS1 telescope. As this camera is composed of many individual electronic readouts, and covers a very large field of view, great care was taken to ensure that the many instrumental effects were corrected to produce the most uniform detector response possible. We present the image detrending steps used as part of the processing of the data contained within the public release of the Pan-STARRS1 Data Release 1 (DR1). In addition to the single image processing, the methods used to transform the 375,573 individual exposures into a common sky-oriented grid are discussed, as well as those used to produce both the image stack and difference combination products.
We present the details of the photometric and astrometric calibration of the Pan-STARRS1 $3pi$ Survey. The photometric goals were to reduce the systematic effects introduced by the camera and detectors, and to place all of the observations onto a photometric system with consistent zero points over the entire area surveyed, the ~30,000 square degrees north of $delta$ = -30 degrees. The astrometric calibration compensates for similar systematic effects so that positions, proper motions, and parallaxes are reliable as well. The Pan-STARRS Data Release 2 (DR2) astrometry is tied to the Gaia DR1 release.
The Pan-STARRS Data Processing System is responsible for the steps needed to downloaded, archive, and process all images obtained by the Pan-STARRS telescopes, including real-time detection of transient sources such as supernovae and moving objects including potentially hazardous asteroids. With a nightly data volume of up to 4 terabytes and an archive of over 4 petabytes of raw imagery, Pan-STARRS is solidly in the realm of Big Data astronomy. The full data processing system consists of several subsystems covering the wide range of necessary capabilities. This article describes the Image Processing Pipeline and its connections to both the summit data systems and the outward-facing systems downstream. The latter include the Moving Object Processing System (MOPS) & the public database: the Published Science Products Subsystem (PSPS).
Over 3 billion astronomical objects have been detected in the more than 22 million orthogonal transfer CCD images obtained as part of the Pan-STARRS1 $3pi$ survey. Over 85 billion instances of those objects have been automatically detected and characterized by the Pan-STARRS Image Processing Pipeline photometry software, psphot. This fast, automatic, and reliable software was developed for the Pan-STARRS project, but is easily adaptable to images from other telescopes. We describe the analysis of the astronomical objects by psphot in general as well as for the specific case of the 3rd processing version used for the first two public releases of the Pan-STARRS $3pi$ survey data, DR1 & DR2.
We describe development and application of a Global Astrometric Solution (GAS) to the problem of Pan-STARRS1 (PS1) astrometry. Current PS1 astrometry is based on differential astrometric measurements using 2MASS reference stars, thus PS1 astrometry inherits the errors of the 2MASS catalog. The GAS, based on a single, least squares adjustment to approximately 750k grid stars using over 3000 extragalactic objects as reference objects, avoids this catalog-to-catalog propagation of errors to a great extent. The GAS uses a relatively small number of Quasi-Stellar Objects (QSOs, or distant AGN) with very accurate (<1 mas) radio positions, referenced to the ICRF2. These QSOs provide a hard constraint in the global least squares adjustment. Solving such a system provides absolute astrometry for all the stars simultaneously. The concept is much cleaner than conventional astrometry but is not easy to perform for large catalogs. In this paper we describe our method and its application to Pan-STARRS1 data. We show that large-scale systematic errors are easily corrected but our solution residuals for position (~60 mas) are still larger than expected based on simulations (~10 mas). We provide a likely explanation for the reason the small-scale residual errors are not corrected in our solution as would be expected.
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