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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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Small events detection

Small Events Detection

Fludra1, D. Haigh1,2, D. Bewsher1, V. Graffagnino1, P.R. Young1

1 Rutherford Appleton Laboratory, UK

2 Birmingham University, UK

Small events detection

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.

Small events detection

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.

Small events detection

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

Small events detection

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.

Small events detection

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.

Small events detection

CDS Quiet Sun Brightenings

CDS NIS in O V 630 A line

Sit & stare time series

 5 arc min 

Movie duration: 2 hours


  • 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

Small events detection

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.

Small events detection

Peak detection

Small events detection

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.

Small events detection

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.

Small events detection

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



Small events detection

Example 2:


Threshold: 3x lower, 255 events


Small events detection

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

Small events detection

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

Small events detection

Example of detected events in CDS rastered images

60 rasters, 10 min cadence. Time coincidence requirement: Δt=0

 240’’ 

 240’’ 

Small events detection

Example of a detected event in the CDS rastered image

Small events detection


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

Small events detection


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?

Small events detection


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?

Small events detection


What constitutes an ‘event’ – every act of energy release even if occurring within a bigger event?


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