Calibration types

	Detector calibration
	Dead-time calibration
	Grid calibration
	Aspect system calibration
	Attenuator calibration

Imager Calibration Status

	Grids, aspect and attenuators were fully calibrated at the subsystem level
	Grid alignment was independently verified by ‘gridlet test’
	Calibration data will be installed in SSW data bases
	Goal is to understand instrument response at the ~1% level

Inflight Calibration

	Assumes grid, aspect and attenuator parameters are independent of time, energy and flare.
	Redundancies in data to be used to refine aspect, grid and attenuator parameters.

	Aspect component positions
	Roll calibration using Crab pulsar
	Grid parameters using flare data redundancies
	Attenuator parameters using discontinuities at attenuator changes.

	Improved calibration data base can be applied retroactively
	Imager performance will improve with time.

NORMALIZATION AND PHOTOMETRY

High spectral resolution and imaging spectroscopy are drivers for photometric accuracy.

Goal is ~1% photometry in optimum circumstances.

Map units will correspond to photons / pixel / cm^2 (or equivalent).

è Integrate over a source component to extract photons/cm^2

Back Projection

	For a point source, peak in map indicates summed incident fluence
	Underestimates flux by ~10% (to be fixed)
	For multiple or extended sources, this is not a good choice since ‘hand corrections’ are required.
	Used at present to support testing.

 

 

OTHER IMAGING ISSUES

Snapshot imaging

	Largely untested up to now
	Backprojection works at subsecond intervals.

Visibilities

	Calculated internally
	Not yet made conveniently accessible
	Format will be table of Time, U, V, Amplitude, Phase, (Errors, Flags, tbd)
	Conversion to AIPS-compatible format is under consideration

Spectral calibration

Imaging uses only the diagonal elements of the spectral response matrix.

Imaging Spectroscopy

	Based on multi-image comparison
	Best option will be feature based rather than pixel-based
	Will be a significant reduction is spectral quality
	Needs good photometry
	Manual capability now
	Goal by launch is semi-automated, feature based.

 

DATA RATE AND VOLUME ISSUES

Basic Capabilities

	On board solid-state recorder has 4 Gbyte capacity (1 photon = 4 bytes)
	Downlink capability is ~1 Gbyte/day/station (6 x 10 minute passes)
	Data volume dominated by background, except for very large flares
	Maximum data rate ~25000 counts/s/detector segment
	Large flare could easily fill memory and have substantial dead time.

Use of a second ground station (Wallops) during periods of activity increases downlink capability by ~75%

Attenuators to control rates

	Two aluminum attenuators (thin and thick).
	Simultaneous insertion for all detectors.
	Controlled by on-board software, based on data rates and adjustable parameters.
	One-time override mechanism to reduce effects of malfunction.
	Disabled in default position for first few weeks/months

Decimation to control data volume in front segment.

	Discards every fixed fraction of photons below preset energy.
	Four steps of decimation
	Effects compensated in analysis (except for statistics)

Fast rate mode to provide some imaging capability at high rates.

	At high input rates, transmitted photon rates decrease due to deadtime.
	Fast rate mode is triggered automatically to transmit binned rates in 4 energy channels.
	Detector-dependent time resolution is sufficient to permit imaging in broad spectral bands.
	Detector rear segment is largely unaffected by high rates.

 

 

EFFECT OF GRIDS ON SPECTROSCOPY

Detectors view Sun through rotating grids.

Transmission of grids depends on

	Grid
	Time
	Energy
	Source location relative to pointing axis.

Distorts spectra

	Spectral corrections depend on approximate location of source
	Spectral corrections are straightforward, for spectroscopy with integration times that are multiples of half-rotation time.

Important check

When correctly calibrated, each detector should yield identical, independent spectra.

 

EFFECT OF GRIDS ON LIGHT CURVES

Grids introduce significant artifacts into light curves on three time scales.

1.  Modulation time scales (~1 to ~500 milliseconds)

Timescale depends on grid pitch and source radial offset from pointing axis

2.  Periodic at twice rotation rate

	Due to internal shadowing in grids
	Time of maximum depends on grid orientation direction from source to pointing axis
	Amplitude of slow modulation depends on energy, source offset, and grid pitch:thickness ratio

3.  Periodic at rotation rate

Due to shadowing in grids, combined with grid tilt

Correction of slow variations is straightforward, but requires knowledge of approximate position of source

Correction for modulation is possible only for the summed response over several grids.

	‘Demodulation’ will be an analysis option.
	Timescales for modulation for various grids do not overlap
	Result will be a single lightcurve, (as a function of energy) valid on all timescales.

 

FACTORS TO CONSIDER FOR JUDGING WHICH FEATURES TO TRUST ?

Counting statistics

	Statistical s/n in the peak of a BPmap is s ~ 2 * SQRT(total counts)
	Complexity of image (simpler is better)

Fraction of total counts in feature of interest (a smaller fraction is more susceptible to systematic calibration errors) Special circumstances

	Importance of dead time
	Fast-rate mode
	Attenuator changes
	Special source locations (relative to pointing axis or imaging field of view)
	Suspicious symmetries (arcs relative to pointing axis or strong image sources)

	Characteristics of aspect solution
	Known hardware anomalies
	Known software anomalies
	Unusual symmetries
	Unusual source variability
	Believability of feature

 

TESTS FOR JUDGING WHICH FEATURES TO TRUST ?

Reimage with:

	different algorithms
	different FOV
	pixel size
	image center
	subcollimator combinations
	energy range
	energy bin sizes
	time ranges
	calibration parameters
	odd/even half-rotations

 

Apply redundancy tests

	Compare spectra/lightcurves from different detectors
	Check rotational symmetry of light curve

 

Trace back the feature to:

	Fourier components
	Observed modulation curves

 

Simulate and analyze similar sources

 

HESSI AS AN IMAGER

Strengths

	Nominal performance requirements (angular- and energy-resolution, imaging spectroscopy…)
	Image location
	Colocation at different energies
	Energy calibration
	Photometry (usually)
	Dynamically adaptable to a wide range of image scales

Dynamically adaptable to large range of source strengths

Limitations

	Limited image complexity (Will NOT provide TRACE-like images!)
	Limited dynamic range within an individual image (goal ~100:1 in favorable cases)
	Extended sources can be invisible
	All sources contribute to noise of each source feature.

Limiting Factors

Statistics

May dominate for weak flares, short integrations, narrow energy windows

Image complexity

May be important for spatially complex cases

Systematic errors – In other cases, limitation may be set by

	Knowledge of instrument response
	Algorithm limitations

 

A PERSPECTIVE ON HESSI IMAGING

Consider HESSI as an imager which you can configure.

Variables you control:

	Integration time
	Energy range
	Choice/weighting of subcollimators
	Software algorithms and parameters

Factors to consider:

	Science objective
	Imaging, spectra or light curves
	Spatial scales
	Energy
	Characteristics of flare

Performance of imager will be sensitive to your choices.

We will all be on a steep learning curve.

We should plan on doing easy science first.

Responsible NASA Official:  Gordon D. Holman Web Design:  Merrick Berg, Brian Dennis, Gordon Holman, & Gilbert Prevost Heliophysics Science Division NASA/Goddard Space Flight Center Laboratory for Solar Physics/ Code 671 Greenbelt, MD, 20771, USA Gordon.D.Holman@nasa.gov + NASA Privacy Statement, Disclaimer, and Accessibility Certification This site last updated November 10, 2008.