Imaging Topics Imaging Assumptions Photometry Source Sizes Background Control Parameters Gordon Hurford 19-Oct-2004

Imaging Assumptions (1) Grid, attenuator, & detector response is uniform over energy interval. Can affect quality &/or photometry for wide energy bands Grid response is uniform over FOV Can affect results at edge of FOV, especially for full disk imaging Detected energy = photon energy. May get ghost from higher energy photons

Imaging Assumptions (2) Time bins are short compared to modulation period. Can reduce apparent source strengths (increase xxxxxxxxxxx) Harmonic response of grids is ignored. Chi-squared may not match expectations. (MEM, Pixons, FF) Secondary effects are decoupled. Can lead to imaging artifacts (eg rings around spin axis)

Imaging Assumptions (3) Spectral calibration and imaging assumes an instrument response model. (grids, detectors, attenuators, etc) In practice, after making all known corrections, typical range of single subcollimator flux estimates is ~10%.  There are errors, not yet understood, in some/all of these models at the ~10% level.  Systematic errors in image reconstruction and spectra.

Photometric Issues (1) Map pixel <> surface brightness at center of pixel, not average over pixel.  Use small pixels to integrate flux over a map feature.  Interpolate to get accurate source locations Units of Cleaned maps: photons/cm2/s/arcsec2 Assumes detected energy = photon energy !!

Photometric Issues (2) Feature-integrated flux estimates are much more reliable than peak flux (See Aschwanden et al 2003) Reason: Imaging algorithms respond more directly to source flux than source size. Peak flux estimates then incorporate additional errors which scale as diameter^2. Line of sight flux estimates are VERY unreliable. Require a knowledge of source flux, source size AND source shape. Insufficient data to accurately determine source shape in most cases.  USE FEATURE-BASED FLUX ESTIMATES !

Photometric Issues (3) RMS statistical error (ph/cm2/s/arcsec2) in Clean map flux estimates: = SQRT(2 N) / (40cm2 * modamp * gridtrans * det.eff. * time) where N = total number of counts BUT systematic errors almost always dominate RMS statistical error in isolated source locations: = 2 * Ang.Res * SQRT(N) / (  * modamp * S ) where S = number of source counts (= N - background) USUALLY ok for dominant source except within ~2 grid periods of rotation axis. EXCLUDES possible bias for weaker source components. EXCLUDES small (<1 arcsec) contribution from aspect errors

Source Sizes (1) Ability to accurately measure source sizes is inherent in relative response of different subcollimators Relative visibility > 0.8  FWHM < 0.5 * resolution (unresolved) Relative visibility < 0.2  FWHM > 1.3 * resolution (over-resolved) 0.5 < Rel vis < 0.8  sensitive to source FWHM

Source Sizes (2) Current Options MEM-Sato tends to overestimate sizes. MEM-VIS tends to underestimate sizes. CLEAN Fundamental output is component list which is sensitive to weighting Displayed maps are convolutions with an arbitrary beam Might use 2nd moments of component distibution. Forward Fit gives direct result, but may incorrectly suppress fine RMCs. PIXON algorithm is inherently multiscalar hsi_bproj2size on ssw. documented at http://hesperia.gsfc.nasa.gov/~schmahl/bproj2size

Source Sizes (3) Complications Future Options Albedo Multicomponent sources Future Options Clean approach based on comparing single subcollimator images Visibility-based algorithms should be accurate.

Background (1) Types of “background” From particles, s/c activation, etc. Component that is independent of flare Component that is proportional to flare 2 temporal terms: Constant and periodic (4s).  Independent of grid transmission From unmodulated source photons Transparent grids Sources are partially over-resolved  Depend on grid transmission CANNOT EASILY DISTINGUISH !

Background (2) Effect of background In principle: Image is independent of background Secondary effects: Increases statistical noise Affects convergence of algorithms (eg FF, MEM) that need to model observed count rates. Background correlation with pointing introduces “ring artifacts” ( Correlation is due to ‘coning’ of s/c pointing and background component that correlates with rotation.)

New Back Projection Option Background (3) New Back Projection Option Subtracts a smoothed version of count rate BEFORE beginning imaging process. Reduces/eliminates ring artifacts. Parameterization is not yet optimized In future can be applied to MEM, PIXONS and Forward Fit.\ Activate with: use_local_average = 1 Other related parameters: (local_average_frequency & auto_frequency )

Control Parameters (1) Time Bins Individual counts are grouped into time bins at start of imaging process. Inappropriate choice of time bins can compromise photometry Time bins must be short compared to modulation period. (eg time bin = period/4, mod. amplitude is reduced by 10%) Modulation periods depend on RMC resolution, distance from rotation axis and grid orientation. Manual parameters: time_bin_min time_bin_def (array) ( set time_bin_def to large values (1024) to speed processing) Automatic: use_auto_timebin = 1 (default) To improve photometry, cbe_digital_quality = 0.99 (default=0.9)

Control Parameters (2) Weighting Uniform_weighting = 1 (default) Assigns weight ~ 1/resolution Closest to uniform coverage in uv plane Heavily weights finest subcollimators Natural_weighting = 1 Equal weights to chosen subcollimators Best choice for photometry and sensitivity May reduce sidelobes BUT beam has a large pedestal Grids 3 - 8

Control Parameters (3) Default assumes flux is constant over integration time. Image quality can be compromised if not true. use_flux_var = 1 To improve compensation for data gaps and livetime Use_rate = 1 To speed processing by ~x2 with a small sacrifice in image quality Modpat_skip = 4

Imaging exercise (2) For the 30 second interval beginning at 095800 on 20 Feb 2002 compare the flux, diameter and position of the sources at 6-10 and 30-50 keV.