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2-Dimensional High Spatial Resolution Spectroscopy

2-Dimensional High Spatial Resolution Spectroscopy. R. Kitai 2009/Oct/5 Solar Seminar. Motivation. DST@Hida Spatially 1D Grating Spectrograph Spatial Resolution : limitted by seeing Countermeasure : installing the AO Image Restroration : impossible

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2-Dimensional High Spatial Resolution Spectroscopy

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  1. 2-Dimensional High Spatial Resolution Spectroscopy R. Kitai 2009/Oct/5 Solar Seminar

  2. Motivation • DST@Hida • Spatially 1D Grating Spectrograph • Spatial Resolution : limitted by seeing • Countermeasure : installing the AO • Image Restroration : impossible • Temporal Resolution : limitted by spatial scanning • Imaging 2D Spectroscopy • MSDP at Meudon : • Suematsu at Sac Peak : Micro-lens • Sac Peak DST : IBIS ( with λScanning FP ) • Swedish SST : CRISP ( with λScanning FPs ) • Image Restoration by Phase Diversity or Speckle

  3. Today’s topics • Grating vs. Fabry-Perot • Methods in high-resolution solar spectroscopy (F. Kneer) • Additional discussion • 2D Spectroscopy at SST • Solar image restoration ( M. G. Lofdahl, M. J. van Noort, and C. Denker)

  4. GS vs. Fabry-Perot ( F. Kneer ) Sp Resolution FOV F-ratio Transmission Device Performance Observational Performance

  5. GS vs. Fabry-Perot • Spectral Purity of FP Interferometer Lyot Filter @SMART Fabry-Perot

  6. GS vs. Fabry-Perot • Multi pass FP IBIS : Double Pass FPs The solar chromosphere at high resolution with IBIS I. New insights from the Ca II 854.2 nm line (G. Cauzzi et al. 2008, A&A480)

  7. 2D Spectroscopy at SST • Movies taken by CRISP/SST • Pore region • H-alpha • Now studied by H. Watanabe

  8. Solar image restoration ( M. G. Lofdahl, M. J. van Noort, and C. Denker) • Correction of aberration • Adaptive Optics (AO) • Real time • Low order aberration • Post-observation correction • Speckle Imaging(SI) • Multi Frame Blind Deconvolution (MFBD) • With/without Phase Diversity (PD) • Residual higher order aberration

  9. Image deformation • Deformation by Point Spread Function • Recovery : Simultaneous estimation of Image and PSF with multi frames • ill-defined problem • Constraint by Multi-Frames • Image : invariant / PSF: variant along time • Constraint by PD method

  10. Phase Diversity • Detection of wavefront deformation Beam Focal Plane Offset Plane No Aberration +Astigmatism +Coma

  11. Optical setup

  12. Restored Image • MFBD with PD

  13. Mathematical formulation of the problem • Lofdahl & Scharmer (1994, A&A Suppl 107, p243) • Paxman, Schulz & Fineup (1992, J. Opt. Soc. Am A9,1072) • In Fourier space • Minimization of the difference between the estimated & the observed images • Utilizing the invariance of F along time, L can be expressed as a functional of Ts • Minimization of LM ( non-linear problem ) can be done by successive linear approximation with the help of the orthonormal Zernike expansion of T

  14. Issues of MFBD • Noise • Artificial high-frequency pattern due to noise • Low-pass filtering • Flat fielding • Anisoplanatism • PSFs : Space variant • Mosaic of subfields ( ~1”) • Data volume & Processing time • SI method : 1 night processing / 1 day observation • MFBD method : 1 year processing (100CPU) / 10h observation (This Multi-Object MFBD (MOMFBD) approach was implemented by van Noort et al. (2005). The C++ code is publicly available from www.momfbd.org. )

  15. Variants and new development of MFBD • Multi-object MFBD (MOMFBD)

  16. Conclusion • (MO)MFBD method • Merit • Required frame numbers : several • Simple Optics • No assumption on the seeing characteristics • Drawbacks • Huge processing time • Under estimation of power spectra due to finite number of Zernike expansion terms

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