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Small Events Detection

Small Events Detection. Fludra 1 , D. Haigh 1,2 , D. Bewsher 1 , V. Graffagnino 1 , P.R. Young 1 1 Rutherford Appleton Laboratory, UK 2 Birmingham University, UK. eSDO : Virtual Observatory Access to SDO Data

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Small Events Detection

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  1. Small Events Detection Fludra1, D. Haigh1,2, D. Bewsher1, V. Graffagnino1, P.R. Young1 1 Rutherford Appleton Laboratory, UK 2 Birmingham University, UK

  2. eSDO: Virtual Observatory Access to SDO Data UK’s Particle Physics and Astronomy Research Council (PPARC) has funded the eSDO project to prepare algorithms for the analysis of SDO data and make them available to the solar community using the virtual observatory (poster 26 - Culhane et al. More details in a talk by Elizabeth Auden in session S6 on Friday). ‘Small Events Detection’ will be one of ten eSDO algorithm packages.

  3. What are Small Events? - Size Aschwanden et al. 2000, ApJ, 535, 1047 Length: 4’’ – 30’’ Area: 4 – 500 arcsec2 Any event smaller than 20’’ - 30’’, down to AIA pixel size.

  4. What are Small Events? - Energy Range Benz & Krucker, 2002, ApJ, Aschwanden et al. 2000, ApJ, 535, 1047 Nanoflares: 1023 – 1026 erg Microflares: 1026 – 1029 erg

  5. Why study Small Events? Is the quiet Sun corona heated by nanoflares? If γ>2, small-scale events provide the dominant contribution to the heating of the corona. Some recent analyses give γ<2, suggesting nanoflares cannot heat the quiet sun corona.

  6. Past research on small event detection: Krucker and Benz, 1998, ApJ, 501, L213 Berghmans, Clette and Moses, 1998, A&A, 336, 1039 Benz and Krucker, 1999, A&A, 341, 286 Parnell and Jupp, 2000, ApJ, 529, 554 *Aschwanden et al. (I) 2000, ApJ, 535, 1027 Aschwanden et al. (II) 2000, ApJ, 535, 1047 Harra, Gallagher and Phillips, 2000, A&A, 362, 371 Aschwanden and Parnell, 2002, ApJ, 572, 1048 Benz and Krucker, 2002, ApJ, 568, 413 *Bewsher, Parnell and Harrison, 2002, Sol. Phys., 206, 21 * marks algorithms selected for testing. This talk presents first results from Bewsher et al. method.

  7. CDS Quiet Sun Brightenings CDS NIS in O V 630 A line Sit & stare time series  5 arc min  Movie duration: 2 hours Time • Advantage of using O V observations for testing SED algorithms: • 15 s time cadence and long series • high variability in the transition region ‘Quiet’ Sun areas show thousands of short-lived intensity enhancements

  8. CDS time series in one of 71 pixels, with 2500 exposures and 15 s cadence. Note that previous TRACE & EIT nanoflare studies dealt with 13-25 frames with 80-120 s cadence.

  9. Peak detection

  10. Algorithm from Bewsher et al. 2000, Sol. Phys. I. Peak detection: Stage 1. Identify maximum peak (*) in time series Stage 2. Identify minimum troughs (□) on either side of peak. Intensity jump between peak and troughs is greater than nεε, so peak is kept. Stage 3. Identify largest peaks (*) on either side of original peak, and minimum troughs (□) on either side. If intensity jumps are larger than nεε, so those peaks are kept. Stage 4. Identify next largest peaks (*)on either side of (*) peaks and their minimum troughs (□). If none of the intensity jumps from peaks to troughs are greater than nεε, peaks are neglected and time series is not investigated further. II. Pixel Grouping Adjacent pixels that peak within +/- nΔt (n=0, 1, 2,…) are grouped together to form an event. All 8 neighbour pixels are examined around each pixel already classified as part of the event. The lightcurves of the pixels are integrated over all the pixels in an event to produce an `event' lightcurve.

  11. III. Check! A check is made to see if each ‘event' still meets the same criteria as for individual pixels, i.e. its peaks and troughs are identified and compared in the same way as discussed in Section I. If a peak is identified in the summed `event' intensity, and this peak is still greater than above the troughs, then the event is counted. IV. Picking Troughs The nearest minimum (NM) trough on either side of the peak is identified. Look at all other troughs between the NM trough and the peak and calculate increase in intensity between troughs and small peaks in between. If change in intensity is less than 2ε, then the NM trough is correct. If the change in intensity is greater than 2ε, then the nearer trough is correct. Finally, the intensity difference between the peak and the corrected trough is checked to ensure that they meet the criteria for an ‘event’, i.e. that the intensity increase is still greater than nεε.

  12. Example results from CDS sit & stare time series 4’’x240’’ slit 71 spatial pixels (3.4’’) 15 s cadence 10 hour run 2500 exposures Threshold: high, large peaks only Min event size = 1 pixel time coincidence of +/- 3 steps Result: 52 events Time distance

  13. Example 2: Time Threshold: 3x lower, 255 events distance

  14. Example of individual events in the CDS sit & stare time series

  15. Example of individual events in the CDS sit & stare time series

  16. Example of detected events in CDS rastered images 60 rasters, 10 min cadence. Time coincidence requirement: Δt=0  240’’   240’’ 

  17. Example of a detected event in the CDS rastered image

  18. DISCUSSION (1) How many nanoflares can we expect: Krucker & Benz, 1998, ApJ Let.: 7’x7’ field of view, 42 minutes. Energy range 8x1024 – 2.8x1026 erg >3 sigma = 11,150 events. > 5sigma = 2600 events Aschwanden et al. 2000: 173 A, >3 sigma = 3130 events 195 A: >3 sigma = 900 events Parnell and Jupp, 2000 (TRACE 7’x7’ fov): >3sigma = 11,700 pixels = 4500 events in 80 minutes Our estimate: CDS: ~1,500 events/hour in a 4’x4’ fov. Expect 50,000 – 100,000 events/hour on the entire disk

  19. DISCUSSION (2) Should we catalogue all small events? Down to what energy and size? Should we catalogue events in each AIA channel separately, or attempt to identify the same events in different channels? Data products: What event parameters should be stored for each AIA channel: start/peak/end time, coordinates of all pixels, area, duration, peak intensity, total intensity How can individual event parameters be used? Time evolution of individual events – heating phase, decay phase Are statistical frequency distributions of these parameters sufficient?

  20. DISCUSSION (3) Analyse full disk or a central part of disk? (can events near the limb merge and spoil statistics?) Analyse 24-hours/day or shorter samples (1-hour/day, 1-hour/week etc.)? Frequency distributions of event energies: Conversion of signal (DN/s) to event energy, through Te and emission measure (and Ne). What statistical significance level should we adopt for event detection? 3σ ? Cosmic ray removal, solar rotation correction, other issues?

  21. DISCUSSION (4) What constitutes an ‘event’ – every act of energy release even if occurring within a bigger event? Example:

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