SDO Feature Finding Team Alisdair Davey (ard@head.cfa.harvard)

1. SDO Feature Finding Team Alisdair Davey (ard@head.cfa.harvard.edu)

2. Feature and Event Driven Approach to Data Discovery • Scientists will often browse through data looking for events, analyze numbers of these events and sometimes turn them into science catalogs. • How about starting with features or events, filtered for your scientific objective and then extracting data cubes for analysis. Heliophysics Event Knowledgebase (HEK) • “The HEK is designed to catalogue interesting solar events and features and to present them to members of the solar physics community in such a way that guides them to the most relevant data for their purposes.”

3. Team Composition Harvard-Smithsonian: Kasper (PM), Davey* (pipeline, interfaces, hardware), Korreck (documentation, outreach), Wills* (Dimmings, EIT Waves), Attrill (dimmings), Grigis, Testa (flares), Saar, Farid (XRBP’s), Engell* (PILs) MSU: Martens (PI), Angryk, Banda, Atreides (all trainable module) Lockheed-Martin: Timmons (pipeline, interfaces, HEK), Hurlburt Johns Hopkins-APL: Bernasconi (filaments), Raouafi (sigmoids) NASA-Marshall: Cirtain* (pipeline, interfaces, metadata) Boston University: Savcheva (jets) SwRI: DeForest, Lamb* (magnetic feature tracking, sunspots, CMEs) Royal Observatory of Belgium: Hochedez, Delouille, Mampaey, Verbeek (Spoca, ARs and CHs) New Mexico State, Trinity College Dublin: McAteer (oscillations) Academy of Athens: Georgoulis (sigmoids, filaments) Max Planck Lindau: Wiegelmann (full disk NLFFF extrapolations)

5. FFT Presentations at this Meeting Talk Paolo Grigis: SDO Flare Detective Derek Lamb: Making Sense of the Soup: SWAMIS Magnetic Feature Tracking for SDO Posters Cis Verbeeck: Multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithms Alec Engell: Automated polarity inversion line detector and associated properties: Flare/CME likelihood Alisdair Davey: An update on the SDO Feature Finding Team efforts

6. Modules • Flares • Coronal Dimmings • Magnetic Feature Tracking (from sunspots to micro-pores) • Sunspots • Active Regions • Coronal Holes • Filaments (H-alpha data) • Sigmoids • CMEs (LASCO data) • Jets • X-ray Bright Points • EIT waves • Coronal Oscillations • Mapping Polarity Inversion Lines • Full Disk Non-Linear Force-Free Field Extrapolations • Trainable Feature Recognition Module

7. Module Status • Currently running in Event Detection System (EDS) at LMSAL • Flares • Magnetic Feature Tracking • Active Regions • Currently running at SAO outside of EDS. • Filaments • Sigmoids (but not uploading events - needs more work to be • pipeline ready) • Has run in the EDS but is not currently • Coronal Dimmings

8. Module Status • Done but not EDS ready and what should the “Event” be? • Mapping Polarity Inversion Lines • X-ray Bright Points • Still in science development • Coronal Holes • CMEs • Jets • EIT Waves • Coronal Oscillations • Being held up by vector magnetogram issues • Full Disk Non-Linear Force-Free Field Extrapolations

9. Module Status • Still in science development, and resulted in a Ph.D thesis • Trainable Feature Recognition Module • See poster 114 for more details on individual module status

10. Verification • Reproducibility • Even if it’s wrong it’s reproducibly wrong! • By hand / eye • By publication of results - peer review • Previous instrument data sets • Using HEK / iSolSearch • Using to AIA/HMI - throws up interesting problems • Modules are not frozen • Expect modules to be updated with improvements to code. • Code is versioned • If results updated, old events not thrown away • Codes available to the community

11. Challenges What’s in an Event? Polarity inversion lines for the full sun and in an active regions

12. Challenges to putting modules in EDS • Even really good science code is not ready to run in a pipeline! • Failure is not an option! Must recover from all errors. • When data flow stops … • Image rejection - some done by EDS, but models need to be able to deal with bad images, or in fact good images with exposure changes due to AEC. How about eclipse images? • EIT -> AIA or even TRACE -> AIA is a massive change! AIA data is really good!(46 dimming regions). • Defining events - how do you make them fit your model but allow others to apply their own different filtering criteria? • What’s in an event? • Different modes of operation. Trigger mode events useful for space weather. Standard mode and full science mode (Outside of generating HEK events)

13. Challenges to putting modules in EDS HEK welcomes anyone who wants to create a module to run in the EDS at LMSAL Could also run code at SAO if not suitable for EDS or not time critical or want to look at older time periods. • Talk to Alisdair Davey (SAO) and Ryan Timmons (LMSAL) - it will save you a lot of time and anguish. • If you want to contribute a module for a feature or event already running in the pipeline, look at the code for that event already running in the EDS. Lot of code you will need to have that is independent of event detection. • EDS is a java pipeline - uses JAVA2IDL bridge to run IDL modules. • IDL tools in Solarsoft for event creation already there. • Most modules written in IDL but not all. SWAMIS written in PDL. Called from IDL wrapper. • Read: Event Detection System Interface and API by Ryan Timmons. Describes the API to the EDS and what you need to do in order to create a module suitable for running in the EDS. http://www.lmsal.com/sdodocs/doc?cmd=dcur&proj_num=SDOD0042&file_type=pdf