1 / 27

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.

boone
Download Presentation

HESSI Image Reconstruction with Forward-Fitting Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related