IV.E. Noise Performance and Sensitivity

ISSA Explanatory Supplement
E. Noise Performance and Sensitivity

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  1. Cross-Scan vs. In-Scan Noise
  2. Noise Equivalent Surface Brightness in ISSA
  3. Residual Zodiacal Emission
  4. Quality Estimates From Scan-to-Scan Statistics

Six major sources contribute to the noise in the ISSA data. Detector noise plus photon noise constitute random noise; natural variations in the celestial background contribute confusion noise; drifts in the calibration of the data produce stripe noise; residual zodiacal emission contains gradients and steps; and nonconfirming sources and radiation spikes introduce spurious point sources. The effects of nonconfirming sources and methods for eliminating them are discussed in §I.D.4. Confusion noise in the IRAS data is discussed in Gautier et al., (1992). The remaining noise sources were measured and analyzed as described below to give the user of ISSA an idea of the sensitivity limits of the ISSA data and of the kinds of errors to expect in the data.

The remaining calibration or stripe noise falls into two spatial domains. Variations over several degrees in the scan direction are discussed in §IV.D.2 in terms of large- and medium-scale baseline distortions. Calibration imperfections produce scan-to-scan and detector-to-detector variations in the images. The RMS stripe noise is measured by examining the variations perpendicular to the scan direction. The performance of the ISSA destripers in reducing this noise is detailed in the section below on cross-scan vs. in-scan noise. Random variations due to electronic noise and photon noise set the noise floor and determine the ultimate sensitivity of ISSA as described in the discussion of noise-equivalent surface brightness density (NESB) and dimmest detectable sources. Finally the magnitude and character of the residual zodiacal emission is discussed.

E.1 Cross-Scan vs. In-Scan Noise

One of the performance goals of the ISSA destriping procedure was to reduce the cross-scan noise to the same level as the in-scan noise. This goal was substantially achieved. Table IV.E.1 shows typical values for a coadded image of the RMS variation along a ~1° cut taken in the cross-scan and in-scan directions. These cuts were confined to flat, low signal regions within each image. Values for the individual HCONs are about 1.6 times higher. There remains a difference in the spatial power spectrum of the noise in the two directions. The in-scan noise spectrum is characteristic of the noise spectrum of an individual IRAS detector. This spectrum is characterized by a power law with a spectral index near -0.75 and contains little power at frequencies near the resolution limit of ISSA. In contrast, the cross-scan spectrum contains substantial power at frequencies up to the free spectral range of the ISSA data at (3')-1, because the cross-scan noise is caused by variations between adjacent detectors whose noises are uncorrelated. This difference in spectral distribution leaves stripe-like features in the residual noise, because the period of the noise variation is much longer in the in-scan direction than in the cross-scan direction. The RMS variation over a few degrees is nearly the same in the two directions, however.

Table IV.E.1 In-Scan vs. Cross-Scan Noise in ISSA Field 398
(MJy sr-1)
(MJy sr-1)
12 µm 0.045 0.033
25 µm 0.048 0.044
60 µm 0.042 0.036
100 µm 0.080 0.063

E.2 Noise Equivalent Surface Brightness in ISSA

Noise equivalent surface brightness (NESB), actually brightness density here, is conveniently expressed in units of Jy sr-1sr-0.5. Then, for instance, the expected minimum detectable surface brightness for an object of size Omega sr can be calculated as NESB× sqrt(Omega). NESBs for the ISSA images can be estimated from Table IV.E.1 assuming that the appropriate solid angle is that of the 90% encircled energy contour of the ISSA point spread functions (about 2.4× 10^{-6}sr). This calculation yields 51, 68, 56 and 97 Jy sr-1sr-0.5 for the 12, 25, 60 and 100 µm bands, respectively. In areas similar to those where the data for Table IV.E.1 was taken, the dimmest discernible features with size about 0.5° have surface brightness above the background of about 0.02 MJy sr-1 at 12, 25 and 60 µm and about 0.07 MJy sr-1 at 100 µm. Assuming that "dimmest discernible' means about a 3 sigma detection these dimmest surface brightnesses are consistent with the estimates above except at 100 µm where the higher general cirrus brightness makes selection of features as dim as 3 sigma more difficult. The estimates of NESB based on Table IV.E.1 are in agreement with estimates based on the average IRAS detector NEFDs shown in Figure IV.A.1 of the Main Supplement.

E.3 Residual Zodiacal Emission

Residual zodiacal emission causes gradients and sharp discontinuities in the ISSA images. Discontinuities can occur when adjacent regions of sky were observed at very different zodiacal brightnesses. These discontinuities are small, as seen in Table IV.E.2, but are easily identified because their boundaries are very sharp and align in the scan direction. Residual zodiacal gradients are more subtle and can be harder to detect. The residual gradients in the high-ecliptic-latitude ISSA data are most apparent near the ecliptic poles in the 12 and 25 µm bands. The magnitude of the residual emission is largest compared to other celestial emission at the shorter wavelengths. The spatial scale of variation is small near the poles due to the combination of scanning geometry and modeling errors in the variation of polar brightness with the motion of the Earth in its orbit. Measurements of some prominent residual zodiacal gradients are given in Table IV.E.3.

Table IV.E.2 Residual Zodiacal Emission Discontinuities
Field12 µm
(MJy sr-1)
25 µm
(MJy sr-1)
376 0.3 0.5
397 0.2 0.7
352 0.3 0.4
404 0.5 0.8
341 1.2 2.4
175 0.4 0.5
012 0.6 1.0
133 0.6 1.1
024 0.3 0.7

Table IV.E.3 Residual Zodiacal Emission Gradients
Field12 µm
(MJy sr-1 rad-1)
25 µm
(MJy sr-1 rad-1)
352 2.6 2.4
381 3.0 2.3
382 3.2 5.2

E.4 Quality Estimates from Scan-to-Scan Statistics

Noise and variability statistics were kept for each pixel during the ISSA map generation process. These noise maps were originally intended for use with a confirmation algorithm that failed because the variation of data within a pixel was extremely non-Gaussian. This was presumably caused by zero-point variation from scan to scan due to systematic errors in the zodiacal emission model. As a result, the pixel statistics did not reveal much about the actual noise levels in the ISSA data.

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