WG 1 Summary Techniques and Applications. WG1 sessions New algorithms: UV smooth, electron maps Source locations Source sizes Source fluxes (imaging spectroscopy)

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Presentation transcript:

WG 1 Summary Techniques and Applications

WG1 sessions New algorithms: UV smooth, electron maps Source locations Source sizes Source fluxes (imaging spectroscopy) Comparing different imaging algorithms Talks will be available online. Talks were limited to less than 10 slides, preferably 5.

Feb 20: keV CLEAN nat g3-9 CLEAN uni g3-9 CLEAN nat g1,3-9 UV smooth MEM PIXON fwd fit VIS fwd fit

Summary of comparison of algorithm Source locations: good agreement (for simple source geometry,  ~0.2” for Feb 20 footpoint) Source size: agreement ok, but choice of input parameters is important Source flux: within 20% (selection of box around the source introduces uncertainty) Statistical study should be done!

How to improve our understanding of RHESSI imaging Simulations (similar as has been done for spectroscopy): 1.Select different HXR source geometries (Brown, Emslie) 2.Create simulated data (Schwartz) 3.Use different imaging algorithms to reconstruct images 4.Compare results at the next workshop

How to improve our understanding of RHESSI imaging Simulations (similar as has been done for spectroscopy): 1.Select different HXR source geometries (Brown, Emslie) 2.Create simulated data (Schwartz) 3.Use different imaging algorithms to reconstruct images 4.Compare results at the next workshop Estimate errors in images by simulation 1.add noise to observations (calibrated event list) 2.Make images for different representation of noise 3.Calculate standard deviations

How to improve our understanding of RHESSI imaging Simulations (similar as has been done for spectroscopy): 1.Select different HXR source geometries (Brown, Emslie) 2.Create simulated data (Schwartz) 3.Use different imaging algorithms to reconstruct images 4.Compare results at the next workshop Estimate errors in images by simulation 1.add noise to observations (calibrated event list) 2.Make images for different representation of noise 3.Calculate standard deviations Compare with imaging at other wavelengths e.g. EUV or white light footpoints at high resolution e.g. SXR loops taken with XRT thick filters