Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPan-STARRS Pixel Analysis : Source Detection and Characterization
77
0
0.0
(
0
)
Authors
Eugene A. Magnier
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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).
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.
Thick back-illuminated deep-depletion CCDs have superior quantum efficiency over previous generations of thinned and traditional thick CCDs. As a result, they are being used for wide-field imaging cameras in several major projects. We use observations from the Pan-STARRS $3pi$ survey to characterize the behavior of the deep-depletion devices used in the Pan-STARRS1 Gigapixel Camera. We have identified systematic spatial variations in the photometric measurements and stellar profiles which are similar in pattern to the so-called tree rings identified in devices used by other wide-field cameras (e.g., DECam and Hypersuprime Camera). The tree-ring features identified in these other cameras result from lateral electric fields which displace the electrons as they are transported in the silicon to the pixel location. In contrast, we show that the photometric and morphological modifications observed in the GPC1 detectors are caused by variations in the vertical charge transportation rate and resulting charge diffusion variations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
