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HESSI Image Reconstruction with Forward-Fitting Algorithm

Hessi Data Analysis Workshop, HDAW Febr 28-Mar 2 University of Berkeley. HESSI Image Reconstruction with Forward-Fitting. HESSI Image Reconstruction with Forward-Fitting Algorithm. Markus J. Aschwanden LMSAL.

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HESSI Image Reconstruction with Forward-Fitting Algorithm

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  1. Hessi Data Analysis Workshop, HDAW Febr 28-Mar 2 University of Berkeley HESSI Image Reconstruction with Forward-Fitting HESSI Image Reconstruction with Forward-Fitting Algorithm Markus J. Aschwanden LMSAL http://www.lmsal.com/~aschwand/hessi_imaging/

  2. Tutorial I : Standard Example of Forward-Fitting o=his_image() o->set,image_algorithm=‘forwardfit’ o->set,image_dim=[64,64] o->set,pixel_size=[2,2] o->set,energy_band=[6,300] o->set,time_range=[0,4] o->set,det_index_mask=byte([0,1,1,1,1,1,1,1,1]) o->set,n_gaussians=2 o->set,n_par=4 (spherical gaussians) o->set,n_par=6 (elliptical gaussians) o->set,n_par=7 (curved gaussians) im=o->getdata() o->plot http://www.lmsal.com/~aschwand/hessi_imaging/hessi_fwdfit_tutorial1.html

  3. Output parameters of forward-fitting procedure p=o->get() print,,p.n_gaussians … e.g. 2 (number of gaussian components) print,p.n_par … e.g. 4 (numbe of free parameters per component) print,p.chi_ff ... C-statistic of 9 detectors, e.g. [0.00,0.78,1.01,1.07,1.12,1.13,0.91,1.15,1.12] print,p.chiav_ff … average of C-statistics from non-zero detectors, e.g. C=1.04 print,p.phot_sec … incidnet photon rate, e.g. R=959 photons/sec print,p.peak_flux … gaussian amplitude, e.g. A=4.77 photons/sec/cm^2 print,p.coeff_ff(*,0) … coefficients of first source, e.g. [1.00,1.19”,-11.34”,-11.69”] print,p.coeff_ff(*,1) … coefficients of second source, e.g. [0.99,1.34”,0.69”,0.73”]

  4. Tutorial II : Initial Guess Optimization o->set,pixel_size=[4,4] o->set,image_dim=[64,64] o->set,pixel_size=[1,1] o->set,image_dim=[64,64] Unresolved double source Resolved double source http://www.lmsal.com/~aschwand/hessi_imaging/hessi_fwdfit_tutorial2.html

  5. Tutorial III: Number of Source Components Strategy: - Start with single component - If chi-square (C-statistic) C>1.05 increase number of components incrementally until chi-square in range C~0.95…1.05 http://www.lmsal.com/~aschwand/hessi_imaging/hessi_fwdfit_tutorial3.html

  6. o->set,n_gaussians=1 o->set,n_gaussians=2

  7. o->set,n_gaussians=3

  8. Comparison of Image Reconstruction Algorithms - Backprojection - Forward-Fitting - Clean - (Pixon ) - (MEM Sato ) Test images: - single, double, triple, quadruple gaussians - single, double ellipticals - curved ellipticals http://www.lmsal.com/~aschwand/hessi_imaging/hessi_fwd_comp.html

  9. The Slinky Challenge !

  10. Future Developments - Iterative Forward-fitting with option to input initial guess from previous run - Realistic simulations using Yohkoh/HXT and TRACE/UV images (double flare ribbons) - “Empirical forward-fitting” : user varies number of gaussians (n_gaussians) and free parameters (n_par) by trial and error - “Blind forward-fitting” : Algorithm optimizes automatically number of gaussian components and free parameters - “spectral-spatial forward-fitting”: reconstruct images at different energies with constraint of smooth spectrum - computational speed (accelerated convergence, dynamic pixel size)

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