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The RHESSI Imaging Concept. Rick Pernak Laboratory for Solar and Space Physics Goddard Space Flight Center And The Catholic University of America. RHESSI. NASA SMEX mission Designed to observe solar flares in X-rays and Gamma Rays Unprecedented Resolution Spatial Spectral Temporal - PowerPoint PPT Presentation
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The RHESSI Imaging The RHESSI Imaging ConceptConcept
Rick PernakRick PernakLaboratory for Solar and Space PhysicsLaboratory for Solar and Space Physics
Goddard Space Flight CenterGoddard Space Flight CenterAndAnd
The Catholic University of AmericaThe Catholic University of America
RHESSIRHESSI
NASA SMEX missionNASA SMEX missionDesigned to observe solar flares in X-rays Designed to observe solar flares in X-rays
and Gamma Raysand Gamma RaysUnprecedented ResolutionUnprecedented Resolution
SpatialSpatialSpectralSpectralTemporalTemporal
Launch: February 5, 2002Launch: February 5, 2002
InstrumentInstrument
Rotating Modulation Collimator (RMC)Rotating Modulation Collimator (RMC) 9 Subcollimators, Grids, and Detectors9 Subcollimators, Grids, and Detectors 2 sets of grids2 sets of grids
X-ray ImagingX-ray Imaging
Difficult to focus X-rays (or Gamma Rays) Difficult to focus X-rays (or Gamma Rays) because of such a short wavelengthbecause of such a short wavelength
Focusing Optics not feasibleFocusing Optics not feasibleNeed to block X-rays and cast shadowsNeed to block X-rays and cast shadowsUse collimator-based Fourier-transform Use collimator-based Fourier-transform
imagingimaging
Detector and Incident PhotonsDetector and Incident Photons
Modulation ProfilesModulation Profiles
Imaging SoftwareImaging Software
From data (modulation profiles and/or From data (modulation profiles and/or visibilities), have the desired information to visibilities), have the desired information to construct an imageconstruct an image
Current algorithms: Back Projection, Current algorithms: Back Projection, Clean, Pixon, MEM, Forward FitClean, Pixon, MEM, Forward Fit
Back ProjectionBack Projection
Workhorse of RHESSI imagingWorkhorse of RHESSI imaging ““Simplest” image reconstruction procedureSimplest” image reconstruction procedure Constructs “probability maps” of incident Constructs “probability maps” of incident
photonsphotons
How Back Projection WorksHow Back Projection Works
CleanClean
First used in Radio ImagingFirst used in Radio Imaging An extension of Back-ProjectionAn extension of Back-Projection Most-publishedMost-published
1.1. Iterative algorithmIterative algorithm2.2. Treats extended sources as superposition of Treats extended sources as superposition of
point sourcespoint sources3.3. Picks out brightest pixelsPicks out brightest pixels4.4. Convolves with Clean beamConvolves with Clean beam5.5. Adds residualsAdds residuals
PixonPixon
Most reliable algorithm (theoretically) in Most reliable algorithm (theoretically) in terms of photometryterms of photometry
Arranges pixels in map, creates its own Arranges pixels in map, creates its own modulation profile and tries to match the modulation profile and tries to match the data (data (χχ22 check) check)
The tradeoff is efficiencyThe tradeoff is efficiency
VisibilitiesVisibilities
Radio-based conceptRadio-based concept Essentially an Essentially an
amplitude and a amplitude and a phasephase
Created from Created from modulation profilesmodulation profiles
Analogous to radio Analogous to radio interferometer interferometer visibilitiesvisibilities
Mathematical Approach to Mathematical Approach to VisibilitiesVisibilities
Each detector has a pitch, k, which is used Each detector has a pitch, k, which is used to construct visibilitiesto construct visibilities
UV points based on pitch and phase angleUV points based on pitch and phase angleVisibilities based on UV points and spatial Visibilities based on UV points and spatial
coordinatescoordinates
MEMMEM
Maximum Entropy MethodMaximum Entropy MethodAnother Radio-based imaging methodAnother Radio-based imaging methodRHESSI MEM programs: MEM_VIS, RHESSI MEM programs: MEM_VIS,
MEM_SATO, MEM_NJITMEM_SATO, MEM_NJITEntropy term: log of the map fluxEntropy term: log of the map flux
Bayesian: S = H - (Bayesian: S = H - (λλ**χχ22))Cornwell/Evans: S = H – Cornwell/Evans: S = H – ββ**χχ2 - 2 - αα*F*F
H = H = ΣΣ F Fijij*log(F*log(Fijij))
MEM_NJITMEM_NJIT
MEM_SATO, MEM_VIS not successfulMEM_SATO, MEM_VIS not successfulBoth used counts/modulation profilesBoth used counts/modulation profilesMEM_NJIT is visibility-based, designed by MEM_NJIT is visibility-based, designed by
New Jersey Institute of TechnologyNew Jersey Institute of TechnologyStarting to be used more and more by Starting to be used more and more by
RHESSI researchersRHESSI researchers
Forward FittingForward Fitting
““Guess” at parameters of an eventGuess” at parameters of an eventGenerate map that is consistent with dataGenerate map that is consistent with dataUseable with modulation profiles, but new Useable with modulation profiles, but new
software uses visibilitiessoftware uses visibilities
Qualitative ComparisonQualitative Comparison
Scientific StudiesScientific Studies
Using MEM_NJIT and Forward Fitting:Using MEM_NJIT and Forward Fitting:Asymmetries in Flux and Source SizeAsymmetries in Flux and Source SizeEnergy dependence in the asymmetriesEnergy dependence in the asymmetries