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Pi of the Sky

Pi of the Sky. Joanna Falzmann UKSW. Algorithm for automatic estimation of quality of measurements. Pi of the Sky. Searching for optical flashes of astronomical origin Every night thousands of pictures (frames) are taken All data goes to the huge database

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Pi of the Sky

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  1. Pi of the Sky Joanna Falzmann UKSW Algorithm for automatic estimation of quality of measurements Joanna Falzmann Algorithm for automatic estimation of measurements quality

  2. Pi of the Sky • Searching for optical flashes of astronomical origin • Every night thousands of pictures (frames) are taken • All data goes to the huge database • Part of this data has low quality. It’s caused by clouds, full moon… Joanna Falzmann Algorithm for automatic estimation of measurements quality

  3. Division into fields • Sky has been divided into fields • Astronomical coordinates gives name for each field (Right Ascention and Declination) • Example: 0800+20 Ra= 08h 00m Dec=20 • 600 fields in database • About 500 was observed at least once Joanna Falzmann Algorithm for automatic estimation of measurements quality

  4. Number of visible stars • Each field has characteristic number of stars on it • If we know this number we can use it for estimation of quality of fames • Plot of the number of stars on each field is automatically created every night Joanna Falzmann Algorithm for automatic estimation of measurements quality

  5. Clouds and High Moon Sun raises Clouds Good data Sun sets Joanna Falzmann Algorithm for automatic estimation of measurements quality

  6. Example of good data Good data Good data Sun sets Sun raises Joanna Falzmann Algorithm for automatic estimation of measurements quality

  7. Clouds and High Moon • System sees less stars that it is there • We need to build an automatic tool to select bad and good frames • Associate a „quality measure” to each frame Joanna Falzmann Algorithm for automatic estimation of measurements quality

  8. Building an algorithm • Finding all interesting data for each field • Number of measurements greater than 10 • Creating histograms for all fields fo all nights • Analizing the histograms • Inserting characteristic number of stars to database Joanna Falzmann Algorithm for automatic estimation of measurements quality

  9. Histograms • Some histograms are regular – we can fit one gauss Joanna Falzmann Algorithm for automatic estimation of measurements quality

  10. Histograms – one gauss Joanna Falzmann Algorithm for automatic estimation of measurements quality

  11. Information to the database • About 50 fields in 500 of all has this regularity • Info about mean value and sigma to the database Joanna Falzmann Algorithm for automatic estimation of measurements quality

  12. Histograms – two gausses • Some histograms show two gausses Joanna Falzmann Algorithm for automatic estimation of measurements quality

  13. Histograms – two gausses Joanna Falzmann Algorithm for automatic estimation of measurements quality

  14. Strange events Joanna Falzmann Algorithm for automatic estimation of measurements quality

  15. Investigation • Histograms of about 150 fields in 500 of all are useless • We need to collect more data • For the rest we have to investigate why they have two (or more) gausses structure Joanna Falzmann Algorithm for automatic estimation of measurements quality

  16. Criteria • Changing criteria • More than 30 measurements of the field during one night • Cut observations from begin and end of the night • Cut measurements with full moon Joanna Falzmann Algorithm for automatic estimation of measurements quality

  17. Implementation • Sipmle algorithm wich will give number for each frame: • Frames with number of stars equal mean value - sigma – very good • Frames with mean value – 1 to 2.5 sigma – acceptable, but suspicious • Discard the rest Joanna Falzmann Algorithm for automatic estimation of measurements quality

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