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Explore Pi of the Sky project, an algorithm for automated estimation of data quality collected from capturing optical flashes in astronomy. The algorithm categorizes measurements based on various factors including number of visible stars, cloud cover, and moon phases. By dividing the sky into fields and analyzing histograms, the system assigns a quality measure to each frame, aiding in separating good data from bad. This innovative approach streamlines the process of identifying and retaining high-quality astronomical data for further analysis.
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Pi of the Sky Joanna Falzmann UKSW Algorithm for automatic estimation of quality of measurements Joanna Falzmann Algorithm for automatic estimation of measurements quality
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
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
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
Clouds and High Moon Sun raises Clouds Good data Sun sets Joanna Falzmann Algorithm for automatic estimation of measurements quality
Example of good data Good data Good data Sun sets Sun raises Joanna Falzmann Algorithm for automatic estimation of measurements quality
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
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
Histograms • Some histograms are regular – we can fit one gauss Joanna Falzmann Algorithm for automatic estimation of measurements quality
Histograms – one gauss Joanna Falzmann Algorithm for automatic estimation of measurements quality
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
Histograms – two gausses • Some histograms show two gausses Joanna Falzmann Algorithm for automatic estimation of measurements quality
Histograms – two gausses Joanna Falzmann Algorithm for automatic estimation of measurements quality
Strange events Joanna Falzmann Algorithm for automatic estimation of measurements quality
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
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
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