Pan-STARRS Image Processing Pipeline

1. Pan-STARRS Image Processing Pipeline An Overview IFA Pan-STARRS Seminar 735 October 6, 2004

2. Summary of Topics • Image Analysis Steps / Science Data Products • IPP System Architecture & Motivations • Software Organization (PSLib / Modules / Analysis Stages) • PS-1 vs PS-4 implications for IPP

3. IPP within Pan-STARRS Telescopes Cameras OTIS raw images metadata metadata, detections metadata, detections IPP MOPS Science Client IDs IDs Legend orbits identifications PS Subsystem External System metadata, detections static sky images PSPS pixel data photons filtered detections & metadata meta & object data commands static sky images other data World Users Solar System Community

4. IPP Image Processing Responsibilities: • Processing of single images (phase 2) • remove instrumental signature • bright object detection / basic characterization • initial astrometric & photometric calibration • Merging of image groups (phase 4) • warp & resample to reference image grid • stack and CR-reject • subtract static sky image • measure objects on difference & summed images • update static sky • Construct Calibrations • basic calibration images (bias, dark, flat, etc) • derived calibration images (fringe, flat-field corrections, etc) • other calibration data (optical distortion parameters, zero-points, etc • Analysis Trace & Monitor • System Characterization

5. Phase 2 Issues • Flat-fields are corrected based on stellar photometry

6. Phase 2 Issues • Flat-fields are corrected based on stellar photometry • fringe frames may be built with a monochromatic dome source • fringe correction may be based on atm. emission line observation

7. Phase 2 Issues • Flat-fields are corrected based on stellar photometry • fringe frames may be built with a monochromatic dome source • fringe correction may be based on atm. emission line observation • minimal object parameters (x,y, mag, stellar/non-stellar, basic shape) • high detection threshold (~20 sigma?) • we keep postage-stamp images on a (small) subset of objects

8. Phase 4 Issues • overlapping pixels are mapped to common (sky) reference frame • Object detections are perfomed on: • Difference Images (P4D) to low threshold (~3 sigma) • Improved Summed Images (P4S) to modest threshold (~5-10 sigma) • Static Sky is updated • note caveat in CR rejection for sequential vs simultaneous images Phase 2 images: 0.3 arcsec/pix Chip/Cell units Phase 4 images: 0.2 arcsec/pix Static Sky cells = - = - cleaned static sky difference image (P4D) also yields transient-free summed image (P4S) and updated static sky image.

9. Static Sky Analysis • Detailed object analysis including • complex object deblending a la SDSS • simultaneous multi-band analysis • Analysis is NOT performed nightly • Static Sky changes slowly • Goal is deep science, not variability • Analysis is computationally more intense than P2,P4 • Rolling Analysis: • ~1 degree RA strips per day • only RA within 10 degrees of solar RA

10. IPP Data Products • Imaging Data Products: • Static Sky images • Postage Stamp Images • Object Measurements • P2, P4S, P4D, Static Sky • rudimentary object associations • photometric / astrometric calibrations • Metadata • image information from summit • weather & other ancillary summit data • analysis statistics • analysis history • calibration information All external data products are sent to PSPS for external access. MOPS & other science clients also receive data products directly.

11. Additional Image Analysis Issues Data has units of: cell, chip, mosaic, image group (major frame) Analysis is staged by data unit: • Phase 1: analysis preparation (mosaic) • Phase 2: single image analysis (chip) • Phase 3: calibration improvements (mosaic) • Phase 4: image combinations (frame) Precision goals (PS-4): • 30 milliarcsec relative astrometry • 100 milliarcsec absolute astrometry • 5 millimag relative photometry • 10 millimag absolute photometry (internal system) These goals can only be efficiently met after we have produced a Pan-STARRS Astrometric and Photometric reference catalog

12. Hardware Organization: optimized for our parallel processing

13. IPP System Architecture state machine (notevent driven) system infrastructure is independent of analysis architectural components are stand-alone entities

14. IPP Software Architecture processing scheduler parallel processing controller Software Tools: CVS SWIG bugzilla doxygen eups autoconf make gcc processing scripts / analysis stages (phase 0-4, cal 1-3, AstromRef, PhotomRef) modules (debias, convolve, flatten, findobjects, etc) PSLib [includes wrappers to externals] (images, vectors, errors, syscalls, etc) External Systems (Lustre?, GFS?, Mysql, Apache, etc) External Libraries (glibc, glib, gsl, CFITSIO, FFTW, SLALIB, etc)

15. Pan-STARRS 1 primary PS-1 goals” • AP Survey • verification surveys (MVP & IVP) (especially for AP Survey) • no simultaneous images • little or no Phase 4 analysis (first year) • no static sky • limited reference data • keep all raw data (~1 year) • multiple analysis passes

16. An Advertisement... We would like to hire 2 - 3 grad students for short-term IPP demo projects: • M31 variability analysis • SDSS / Megacam proper motion study • CFH12K / 2MASS i-band dropouts • SkyProbe A&E development and test