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24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality 1 Pi of the Sky Algorithm for automatic estimation of quality of.

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Presentation on theme: "24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality 1 Pi of the Sky Algorithm for automatic estimation of quality of."— Presentation transcript:

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2 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality 1 Pi of the Sky Algorithm for automatic estimation of quality of measurements Joanna Falzmann UKSW

3 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality2 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…

4 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality3 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

5 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality4 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

6 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality5 Clouds and High Moon Clouds Sun sets Good data Sun raises

7 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality6 Example of good data Good data Sun sets Good data Sun raises

8 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality7 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

9 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality8 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

10 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality9 Histograms   Some histograms are regular – we can fit one gauss

11 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality10 Histograms – one gauss

12 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality11 Information to the database   About 50 fields in 500 of all has this regularity   Info about mean value and sigma to the database

13 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality12 Histograms – two gausses   Some histograms show two gausses

14 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality13 Histograms – two gausses

15 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality14 Strange events

16 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality15 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

17 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality16 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

18 24 maja 2007Joanna Falzmann Algorithm for automatic estimation of measurements quality17 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


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