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Scanamorphos port to

Scanamorphos port to. Michael Wetzstein. Why port Scanamorphos to Java?. get a preview of the Scanamorphos results into the Herschel archive test our modules by direct comparison with Scanamorphos results (well – test Scanamorphos by direct comparison with our results too)

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Scanamorphos port to

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  1. Scanamorphos port to Michael Wetzstein

  2. Why port Scanamorphos to Java? • get a preview of the Scanamorphos results into the Herschel archive • test our modules by direct comparison with Scanamorphos results • (well – test Scanamorphos by direct comparison with our results too) • test the processing idea of Scanamorphos with different code • understand in detail, how Scanamorphos works Contents • remove Turnarounds • baseline subtraction • destriping • remove average drifts (of all detector pixels) • remove individual drifts (of every individual detector pixel) • map

  3. Level 1 of obsid 1342204362 + -63

  4. Baseline removal Repetition by Repetition: baseline fit of average signal, complete timeline. 3-sigma masking subtract the median of all unmasked values per scanleg repeat 1 and 2 repeat 1 galactic option: repeat 2 no galactic: fit scanlegs per detector pixel, 3-sigma masking

  5. Baseline removal - result

  6. Destriping scan • Takes a Map and backprojects it into a Signal cube • this should average out drifts • subract this simulated cube from the real one • fit the difference • subtract fit from the signal • Map • iterate

  7. Destriping Results

  8. Destriping - result Destriped Map Difference to Baseline-Subtracted Map

  9. Average Drifts Goal: remove the drift that is common to all detector pixels Approach: Collect crossings of Detectors over a Map pixel scan For every Map Pixel: Fluxi,si, timei Build drift matrix: Put Fluxi,si at timei Assign Flux(timek) = 0 Find difference: driftki = Fluxk – Fluxi Assign Fluxi Iterate over the matrix

  10. Average Drifts 2 cross-scan scan s-map s-map merge weighted map backproject to timeline subtract from signal

  11. Average - result Map after average Drift removal Destriped Map

  12. Individual Drifts • Calculate the crossings again (like for the average drift) • Calculate the average flux of all crossings for one Mappixel • take the difference as drift and assign it to the timeline, this time for every detector pixel • subtract the drift cube from the signal cube • iterate

  13. Compare Madmap Highpass Filter

  14. More Examples

  15. Sgr A Alpha Boo M 51

  16. Next Steps • Extended Documentation • Process all scan-cross-scan pairs • this is my current work • results are posted in Spr Pacs-4323 together with a list • of successfully processed observations • first prototype of an iPipe script is available • scientific validation is due (for the successfully processed observations) • how do we proceed? • where do we want to go? • (open the discussion)

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