IRAC Instrument Handbook - IRSA - California Institute of Technology

IRAC Instrument Handbook
This is mostly noticeable in very low background conditions. This is corrected by adding to the individual
readout channels a common mean offset. For any image, the flux in a pixel is assumed to be
A i,
j Si
, j + B + DC
+ D
i , j Oi
, j
= (5.13)
where A is the detected intensity in DN, S is the incident “science flux" (celestial background + objects),
B is a constant offset in the frame, DC is the standard calibration dark, and DO is the dark offset. The first
dark varies on a pixel by pixel basis, whereas the offsets are assumed to vary on a readout channel basis.
It can be assumed that the mean Si,j is the same for all readout channels i, and therefore there is a mean
estimator function M for each readout channel
The corrected image (post dark-subtraction) is then
Mi = MeanEstimator(Si,0…Si,n) (5.14)
4
'
1
A i j = Ai,
j − ( M i − M i )
(5.15)
4
, ∑
i=
1
5.1.13 FOWLINEARIZE (detector linearization)
Like most detectors, the IRAC arrays are non-linear near full-well capacity. The number of read-out DN
is not proportional to the total number of incident photons, rather it becomes increasingly small as the
number of photons increases. In IRAC, if fluxes are at levels above half full-well (typically 20,000–
30,000 DN in the raw data), they can be non-linear by several percent. During processing the raw data are
linearized on a pixel-by-pixel basis using a model derived from ground-based test data and re-verified in
flight. The software module that does this is called FOWLINEARIZE. FOWLINEARIZE works by
applying a correction to each pixel based on the number of DN, the frame time, and the linearity solution.
For channels 1, 2 and 4, we use a quadratic solution, i.e., we model the detector response as
DNobs = kmt – Ak 2 t 2 (5.16)
Pipeline Processing 82 Level 1 (BCD) Pipeline