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Analysis of Image Quality

This analysis focuses on the image quality methods utilized by the Catalina Sky Survey, which seeks to identify near-Earth objects (NEOs) that may pose a threat to Earth. The mission involves capturing multiple images in quick succession to detect objects based on their motion across frames. Techniques such as co-adding images and median averaging help in creating deep star catalogs, while automated analysis metrics like Full-Width Half-Maximum enhance object detection accuracy. We also explore the limitations posed by artifacts and the importance of reliable data selection.

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Analysis of Image Quality

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  1. Analysis of Image Quality Student: Jeffery D. Ahern Mentor: Edward Beshore

  2. Catalina Sky Survey The mission of the Catalina Sky Survey is to find near-earth objects (NEOs) that pose an impact risk to Earth and it's inhabitants.

  3. Catalina Sky Survey This is done by taking four images that are exposed for 30 seconds separated by 10 minutes, and looking for objects that appear in one frame, and have a corresponding object in the other frames plotting in a straight line

  4. Co-added Images • Median average of multiple images • Allow the creation of a deep star catalog • Can compare faint objects to see if a known star is there

  5. Manual Selection • Observer looks over all the images of a field and selects the best • Subjective • Time consuming • Costly • Hard to compare results

  6. Quantitative & Automated Analysis • Meaning to data • Comparable • Objective judgments • Standardization • Decreased error • Repeatable • Reliable • Fast

  7. Metric for a Automated Analysis Full-Width Half-Maximum

  8. Data Selection What should not be counted Artifacts • Bright stars that cause image bleeding Galaxies

  9. Results • The Full-Width Half-Maximum value is a good metric to use to determine if the image is in focus

  10. Questions

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