6.9 Charge Transfer Efficiency
The charge transfer efficiency (CTE) of the UVIS detector has inevitably been declining over time as on-orbit radiation damage creates charge traps in the CCDs. Faint sources in particular can suffer large flux losses or even be lost entirely if observations are not planned and analyzed carefully. In this section, we describe the effect of CTE losses on data, observational strategies for minimizing losses, and data analysis techniques which can to some extent correct for CTE losses.
For the latest information about CTE on the UVIS detector, see the WFC3 CTE webpage:
The flux of energetic particles in low-Earth orbit, mostly relativistic protons and electrons encountered during HST’s frequent passages through the South Atlantic Anomaly, continually damages the silicon lattice of the CCD detectors. This damage manifests itself as an increase in the number of hot pixels, an increase in the dark current, and an increase in the charge trap population.
The effect of hot pixels, about 1000 new/day/chip using a threshold of 54e–/hr/pix, is addressed with anneal procedures, dark calibration files, and dithering. The anneal procedures, performed monthly, warm the detectors to +20°C and restore some of the hot pixels to their original levels. Dark calibration files (running averages of daily dark images) can provide reasonable identification of hot pixels as well as a calibration for overall dark current (see Section 5.4.8 for the median level and growth rate). The calibration darks allow hot pixels and dark current to be subtracted from science images in the calibration pipeline. Due to the time-variable behavior, the corrections are imperfect, but the dark current levels are low and dithering can help reduce any residual impact of hot pixels in final image stacks.
The effect of charge traps is more difficult to address, as the traps do not respond to the anneals and the damage appears to be cumulative and irreversible based upon both flight and ground test experience. The traps cause a loss in source flux as well as a systematic shift in the object centroid as the charge is trapped and slowly released during readout. The majority of the trapped charge is released during the readout within ~1/2 dozen pixel shifts, as evidenced by the charge trails which follow hot pixels, cosmic rays, and bright stars. A low percentage of the initial signal can be seen extending out to ~50 pixels in length (see Figure 6.17).
- The number of rows (and columns) between source and amplifier: sources further from the amplifiers require more transfers to read out and thus encounter more traps.
The intrinsic brightness of the source: fainter sources lose proportionally more charge than brighter sources. Very bright sources (>10e4 e–) suffer relatively small amounts of CTE loss (a few percent). (Figure 6.20.)
The image background: a higher background fills some of the charge traps, thereby minimizing flux losses during readout of the source signal. WFC3/UVIS images can have very low intrinsic backgrounds due to the low detector readnoise and dark current as well as the small pixels of the CCDs. Furthermore, the WFC3 UV and narrowband filters have exceptionally low sky backgrounds.
Thus, the CTE loss will depend on the morphology of the source, the distribution of electrons in the field of view (from sources, background, cosmic rays, and hot pixels) and the population of charge traps in the detector column between the source and the transfer register. And, of course, the magnitude of the CTE loss increases continuously with time as new charge traps form. Further details of the current understanding of the state of the WFC3/UVIS charge transfer efficiency (CTE) are presented in Section 5.4.11 and can be found at: http://www.stsci.edu/hst/instrumentation/wfc3/performance/cte
The remainder of this section will discuss the available options for mitigating the impact of CTE losses and their associated costs. Broadly, the options fall into two categories: those applied before data acquisition, i.e., optimizing the observing strategy during the proposal planning stage, possibly including the use of post-flash, and those applied during image analysis after the images have been taken, i.e., formula-based corrections or image reconstruction.
6.9.2 CTE-Loss Mitigation Before Data Acquisition: Observation Planning
1) Consider the placement of the target within the field of view. For example, when possible, place a target close to a readout amplifier - a viable option when the target is small. This reduces the number of transfers along columns during readout of the target, thereby minimizing CTE losses for the target at the expense of objects that will then be located at greater distances from the readout row at the edge of the chip. Where possible, observers should use full frame exposures for the value they can add to the observing program and the archive. A significant change in orbit structure occurs when exposures are lengthened beyond 347 sec, since data dumping of entire chips can then be done in parallel to taking exposures (see Section 10.3.1). For short exposures, if the region of interest is sufficiently small, a subarray aperture (with suffix -SUB) can be used to increase the number of exposures that can be fit into one orbit. (See Figure 6.2.) Subarray apertures read out and store only a portion of the full field of view of the detector. Subarray apertures UVIS2-C1K1C-SUB and UVIS2-C512C-SUB (see Table 6.1) place the target 512 and 256 pixels, respectively, from the edges of the UVIS detector near amplifier C and read out 1025x1024 and 513x512 science pixels, respectively. Starting in cycle 23, observers can use the apertures UVIS2-C1K1C-CTE and UVIS2-C512C-CTE to place the target at the same reference positions as UVIS2-C1K1C-SUB and UVIS2-C512C-SUB, respectively, and read out the full detector instead of only a subarray. POS TARGs can also be used to move the target to the lower part of the C quadrant (e.g., negative POS TARG X and negative POS TARG Y in aperture UVIS-CENTER) to reduce CTE losses. (See Section 6.4.4 for reasons to prefer quadrant C over the other quadrants.)
Another approach, suitable for sparsely populated fields in which the sources of scientific interest are relatively bright, involves obtaining observations at multiple spacecraft roll angles. In this case, the different roll angles (ideally at or near 90 degrees) will result in sources having large variations in the number of pixels over which they must be transferred during readout. This permits a direct assessment of the reliability of the available formulaic photometric CTE calibrations which can be applied during post-processing (discussed in more detail below in Section 6.9.3).
If observations are being taken on a field larger than the instantaneous field of view of the cameras, then stepping in the Y direction (i.e., along the CCD columns) with a small degree of overlap will place some sources at both small and large distances from the transfer register again permitting a direct assessment of the photometric reliability of the CTE corrections applied during data processing (see section on formula-based corrections below in Section 6.9.3).
2) Increase the image background by lengthening exposure times, using a broader filter, and/or applying an internal background (post-flash). Dividing observations into fewer, but longer, exposures has several benefits: it provides more natural sky background (less post-flash will be required), it increases the source signal relative to the read noise and to the post-flash used, and it saves on overheads (each full-frame requires ~90 sec to read out). Ensuring that images with faint sources contain a minimum of 20e-/pix total background (dark+sky+post-flash if needed) is a crucial CTE mitigation strategy for many WFC3/UVIS science proposals in 2020 and beyond (WFC3 ISR 2020-08).
On-orbit testing has shown that CTE losses are a non-linear function of both the source and image background signals: a faint source in a low-background image will lose a significantly larger proportion of signal than a similar source in a high-background image. In some cases, faint sources can even disappear completely during the readout transfers, as illustrated in Figure 6.18 (Anderson et al. 2012; see also WFC3 ISR 2020-08). The left panel is the result of a stack of long exposures minimally impacted by CTE losses, i.e., effectively truth’ image. The middle panel presents the result of a stack of short, very low background exposures; the CTE trails are clearly visible above each source and the charge traps have completely smeared out the signal from the faintest sources (e.g., A and D). The right panel is a stack of short exposures where each image had ~16 e– total background. The CTE trails have been reduced considerably and stars lost in the stack of very low background images are recovered in the stack of higher-background images, a clear qualitative demonstration of how a small amount of background can preserve even small charge packets of signal.
A more qualitative measure of how relatively low levels of background can significantly improve the CTE and increase the S/N of very faint sources is presented in Figure 6.19. Shown are aperture photometry results for faint stars in very low background (top row) and higher-background data (bottom row), as a function of the number of transfers, i.e., distance from the amplifier. The target sources are faint: 100, 50, and 10e- total in a 3 × 3 pixel aperture from left to right. Sources far from the amplifiers (~2000 on the x-axis) in images with little background (top row) effectively disappear. The same faint sources embedded in images with slightly higher background are detectable at the 50-75% level (Anderson et al. 2012). Note that CTE losses have changed significantly since 2012. For more recent results see https://www.stsci.edu/hst/instrumentation/wfc3/performance/cte.
In prior years, with a younger instrument, a relatively harmless background of 12e-/pix provided adequate protection as CTE losses were less than 25%, a perturbation level that the pixel-based CTE model could safely reconstruct. With the accumulating radiation damage to the detector, however, by 2020 marginal losses at background levels of ~12 e-/pix increased to about 50%. That level of loss is too large to correct in an automated way, and moreover, it results in significant S/N loss; even a perfect reconstruction algorithm cannot restore lost S/N. The good news is that increasing the image background can provide increased CTE mitigation although observers with faint sources will want to carefully consider the level of post-flash to use (WFC3 ISR 2020-08).
In order to determine the necessary post-flash level to use for mitigating CTE losses, observers will first need to estimate the expected natural backgrounds. The Exposure Time Calculator provides such estimates, including contributions from sky, dark, zodiacal light, earthshine, airglow, and a selected level of post-flash. In addition, empirical backgrounds as measured on all WFC3/UVIS frames in the archive are summarized in the Appendix of WFC3 ISR 2012-12. If the natural background of the exposure will be higher than 20 e–/pix then there is no need to add post-flash. For images with very low background levels, enough post-flash should be applied to achieve ~20 e–/pix total background (natural+post-flash).
Observers invoke post-flash in APT by choosing the exposure optional parameter ‘FLASH’ and specifying the desired number of electrons per pixel to be added to the image using the LED post-flash lamp. (See Section 12.2 in the Phase II Proposal Instructions.) The flash is performed on WFC3 after the shutter has closed at the end of the exposure: an LED is activated to illuminate the side of the shutter blade facing the CCD detector. The experience so far with these lamps (corroborated by the design analysis) indicates that the illumination pattern is very repeatable (to <<1%); it is similar for the two sides of the shutter blade. The intensity is likewise very repeatable, as expected. The brightness of the flash has shown fluctuations of rms~1.2%; the long-term stability is ~0.1% (WFC3 ISR 2017-03). Calibration reference files have been delivered to CDBS for the calibration of exposures using post-flash (WFC3 ISR 2017-13).
The main disadvantage of post-flash is, of course, the increase in the background noise. In the worst case, a short exposure with low background and dark current would require the addition of about 20 e–/pix of post-flash. Thus the original readout noise of ~3.1 electrons is effectively increased to 5.4 e– in un-binned exposures. (See Section 9.6 for S/N equations.) In most cases, however, the impact will be significantly less severe as exposures will generally contain some natural background already and will not require a full 20 e–/pix post-flash.
Finally, please note that even with a moderate background, larger charge packets from brighter stars, hotter pixels, or cosmic rays will still experience some loss and trailing of their initial number of electrons (Figure 6.20), requiring post-processing correction described in the next section. Thus, even with 20 e– background, all sources will still suffer some CTE losses (Figure 6.20) and it will be necessary to apply an additional correction during data processing.
3) Use charge injection. For completeness, this mode is included as an observing strategy option for CTE-loss mitigation but, in practice, it is not considered as useful as e.g., increasing image backgrounds or applying corrections during image processing. Its use will be permitted only in exceptional cases where the science requires it. Observers who wish to use this mode are advised to consult their Contact Scientist or contact the help desk at http://hsthelp.stsci.edu.
Charge injection is performed by electronically inserting charge as the chip is initialized for the exposure, into either all rows or spaced every 10, 17, or 25 rows. Only the 17 row spacing is supported as of mid-2012. The injected signal is ~15000 electrons (not adjustable) and results in about 18 electrons of additional noise in the injected rows (Baggett et al., 2011). The rows adjacent to the charge-injected rows have between 3 and 7 electrons of effective noise due to CTE effects. The charge injection capability was supported in Cycle 19, but experience has demonstrated that it is useful for very few types of observations. Its primary drawbacks are the uneven degree of protection from charge trapping in the rows between the injected charge rows, an increase in noise in the rows closest to the injected charge, and a very difficult calibration problem posed by the combination of sources in the field and the injected rows, which give rise to different levels of CTE at different places within the image. Furthermore, the strong dependence of CTE losses on image backgrounds makes it challenging to produce a suitable calibration, as typically there will be a mismatch in image backgrounds between the charge injected calibration and science frames (i.e., differing levels of CTE losses).
6.9.3 CTE-Loss Mitigation After Data Acquisition: Post-Observation Image Corrections
1) Apply formula-based corrections for aperture photometry. One way to correct CTE losses after the images have been acquired is to apply an empirical photometric calibration. The current results based on stellar aperture photometry provide corrections for CTE losses as a function of observation date, image background, source flux, and source distance from the amplifiers. Figure 6.20 summarizes the analysis. As expected, larger corrections are required for fainter sources and/or sources embedded in lower image backgrounds. The top left panel represents the worst-case scenario: for sources in long exposures taken with a narrowband filter, i.e. ~2 e/pix image background, flux losses in late 2020 are ~1 magnitude for the faintest sources (few hundred electrons within a 3-pixel radius aperture) farthest from the readout amplifiers. The top right panel shows that a background of ~14 e-/pix (~2e- natural plus 12e- post-flash) produces a noticeable improvement in CTE: the faintest sources experience about 0.4 mag of losses while moderately bright sources (few thousand electrons within a 3-pixel radius aperture) experience less than 0.2 mag of flux loss. The lower panels in Figure 6.20 illustrate the efficacy of applying the pixel-based CTE correction (discussed in topic 2 below) to the data in the upper panels. Note that these results are for small apertures far from the readout amplifiers. Corrections for larger apertures will be smaller as more of the trailed charge is included in the aperture and sources closer to the readout amplifier will experience less CTE loss.
The analysis performed followed that done in prior studies (WFC3 ISR 2015-03, WFC3 ISR 2016-17, WFC3 ISR 2017-09, and a 2021 report in prep). In brief, the flux loss due to CTE degradation was evaluated as a function of source brightness, observation date, image background level, and vertical distance from the readout amplifier and fit with a 2nd degree polynomial whose coefficients are provided to allow observers to estimate flux corrections for their point-source photometry. As of late 2020, flux losses for sources far from the readout amplifier, in images with a minimum 12 e/pix background, ranged from 5 to 40 percent, depending on source brightness. These losses can be further reduced by using the CTE-corrected (flc) images available from the MAST. (See topic 2 below).
The formula-based correction method can be effective for isolated point sources on flat backgrounds, but it is less suitable for extended sources or sources in crowded regions. One benefit of the formula-based recalibration is that it is not impacted by the possibility of readnoise amplification, which can be a concern for the pixel-based reconstruction. Photometric corrections are also useful for planning observations: they allow an estimate of the CTE losses for point-like sources that can be expected in a near-future observation for a given background and source flux. The expected losses should be taken into consideration during observation planning and if necessary, total integration times increased to achieve signal to noise requirements.
2) Apply the empirical pixel-based correction algorithm. The ACS team developed and implemented a post-observation correction algorithm employing the Anderson and Bedin methodology (2010; PASP 122 1035). A similar capability was made available for WFC3 from the WFC3 CTE webpage in mid-2013, and was implemented in the MAST pipeline in Feb 2016. (See the article on calwf3 version 3.3 in WFC3 STAN issue 22 and the discussion of the calibration pipeline in Section E.1.) See WFC3 ISR 2017-09 for quantification of the reduction in CTE losses that can be achieved by using the corrected _FLC images. An updated CTE correction is planned for release in MAST in early 2021.
The correction algorithm is calibrated using the behavior of hot pixels and their charge trails. In the absence of CTE losses, the full charge of a hot pixel is entirely contained within a single pixel and its noise is the combination of its shot noise and the noise due to readout and background in that one pixel. If some of the hot pixel charge is lost due to imperfect CTE, there will be fewer electrons in the hot pixel itself, and more in the trailing pixels. (See Figure 6.17.) To obtain the original value of the hot pixel, the correction algorithm must determine how many electrons the original hot pixel would have to have in order to be read out as the observed number, given the number of traps left full and empty by the preceding pixels. The resulting correction essentially redistributes the counts in the image, “putting the electrons back where they belong”, i.e., undoing the effects of degraded CTE (Anderson et al., 2012).
While the pixel-based algorithm has been successful at removing trails behind stars, cosmic rays, and hot pixels, it has one serious and fundamental limitation: it cannot restore any lost S/N in the image. Faint sources, and faint features of extended sources, may be so strongly affected by CTE losses that they become undetectable and cannot be recovered (e.g., see Figure 6.18 and Figure 6.19). In addition, this pixel-based method is effectively a deconvolution algorithm, and it can amplify noise or sometimes generate artifacts. Even so, despite the limitations, the reconstruction algorithm provides the best estimate of the original image before it was read out and also aids in understanding how the value of each pixel may have been modified by the transfer process.
The pixel-based correction algorithm does not correct for sink pixels, which contain a number of charge traps (WFC3 ISR 2014-19). They comprise about 0.05% of the UVIS pixels, but can affect up to 1% of the pixels when the background is low. A calibration program to identify sink pixels and pixels impacted by them has been carried out. The strategy for flagging these pixels is presented in WFC3 ISR 2014-22. Since February 23, 2016, when calwf3 version 3.3 was implemented in the pipeline, sink pixels and their trails have been identified in the DQI array with value 1024 (see Table E.2).
Serial CTE losses (along the X direction on the detector) can easily be seen in deep exposures of the point spread function (see Figure 1 in WFC3 ISR 2013-13). The pixel-based correction algorithm currently corrects only for parallel CTE losses (along the Y direction on the detector). Serial CTE losses affect the X coordinate of bright stars and faint stars at the level of 0.0015 pixels and 0.004 pixels, respectively (WFC3 ISR 2014-02).
We end this Mitigation section by noting that, depending on the science goals, a single mitigation method may not be sufficient for some programs. Observers, particularly those with faint sources, may need to consider applying both pre- and post-observation CTE-loss mitigation strategies, e.g., increasing the image background to ~20 e–/pix to reduce CTE effects followed by an application of either the formulaic photometric or pixel-based corrections or both. (Figure 6.20.). We note that the pixel-based correction algorithms are not able to operate on binned data, but binning is not an effective way of increasing the detectability of faint sources (See Section 6.4.4).
For the most current information on the WFC3 CTE and mitigation options, as well as updates on the availability of a pixel-based correction algorithm for WFC3, please refer to the page CTE webpage at:
WFC3 Instrument Handbook
- • Acknowledgments
- Chapter 1: Introduction to WFC3
- Chapter 2: WFC3 Instrument Description
- Chapter 3: Choosing the Optimum HST Instrument
- Chapter 4: Designing a Phase I WFC3 Proposal
- Chapter 5: WFC3 Detector Characteristics and Performance
Chapter 6: UVIS Imaging with WFC3
- • 6.1 WFC3 UVIS Imaging
- • 6.2 Specifying a UVIS Observation
- • 6.3 UVIS Channel Characteristics
- • 6.4 UVIS Field Geometry
- • 6.5 UVIS Spectral Elements
- • 6.6 UVIS Optical Performance
- • 6.7 UVIS Exposure and Readout
- • 6.8 UVIS Sensitivity
- • 6.9 Charge Transfer Efficiency
- • 6.10 Photometric Calibration
- • 6.11 Other Considerations for UVIS Imaging
- • 6.12 UVIS Observing Strategies
- Chapter 7: IR Imaging with WFC3
- Chapter 8: Slitless Spectroscopy with WFC3
Chapter 9: WFC3 Exposure-Time Calculation
- • 9.1 Overview
- • 9.2 The WFC3 Exposure Time Calculator - ETC
- • 9.3 Calculating Sensitivities from Tabulated Data
- • 9.4 Count Rates: Imaging
- • 9.5 Count Rates: Slitless Spectroscopy
- • 9.6 Estimating Exposure Times
- • 9.7 Sky Background
- • 9.8 Interstellar Extinction
- • 9.9 Exposure-Time Calculation Examples
- Chapter 10: Overheads and Orbit Time Determinations
Appendix A: WFC3 Filter Throughputs
- • A.1 Introduction
A.2 Throughputs and Signal-to-Noise Ratio Data
- • UVIS F200LP
- • UVIS F218W
- • UVIS F225W
- • UVIS F275W
- • UVIS F280N
- • UVIS F300X
- • UVIS F336W
- • UVIS F343N
- • UVIS F350LP
- • UVIS F373N
- • UVIS F390M
- • UVIS F390W
- • UVIS F395N
- • UVIS F410M
- • UVIS F438W
- • UVIS F467M
- • UVIS F469N
- • UVIS F475W
- • UVIS F475X
- • UVIS F487N
- • UVIS F502N
- • UVIS F547M
- • UVIS F555W
- • UVIS F600LP
- • UVIS F606W
- • UVIS F621M
- • UVIS F625W
- • UVIS F631N
- • UVIS F645N
- • UVIS F656N
- • UVIS F657N
- • UVIS F658N
- • UVIS F665N
- • UVIS F673N
- • UVIS F680N
- • UVIS F689M
- • UVIS F763M
- • UVIS F775W
- • UVIS F814W
- • UVIS F845M
- • UVIS F850LP
- • UVIS F953N
- • UVIS FQ232N
- • UVIS FQ243N
- • UVIS FQ378N
- • UVIS FQ387N
- • UVIS FQ422M
- • UVIS FQ436N
- • UVIS FQ437N
- • UVIS FQ492N
- • UVIS FQ508N
- • UVIS FQ575N
- • UVIS FQ619N
- • UVIS FQ634N
- • UVIS FQ672N
- • UVIS FQ674N
- • UVIS FQ727N
- • UVIS FQ750N
- • UVIS FQ889N
- • UVIS FQ906N
- • UVIS FQ924N
- • UVIS FQ937N
- • IR F098M
- • IR F105W
- • IR F110W
- • IR F125W
- • IR F126N
- • IR F127M
- • IR F128N
- • IR F130N
- • IR F132N
- • IR F139M
- • IR F140W
- • IR F153M
- • IR F160W
- • IR F164N
- • IR F167N
- Appendix B: Geometric Distortion
- Appendix C: Dithering and Mosaicking
- Appendix D: Bright-Object Constraints and Image Persistence
Appendix E: Reduction and Calibration of WFC3 Data
- • E.1 The STScI Reduction and Calibration Pipeline
- • E.2 The SMOV Calibration Plan
- • E.3 The Cycle 17 Calibration Plan
- • E.4 The Cycle 18 Calibration Plan
- • E.5 The Cycle 19 Calibration Plan
- • E.6 The Cycle 20 Calibration Plan
- • E.7 The Cycle 21 Calibration Plan
- • E.8 The Cycle 22 Calibration Plan
- • E.9 The Cycle 23 Calibration Plan
- • E.10 The Cycle 24 Calibration Plan
- • E.11 The Cycle 25 Calibration Plan
- • E.12 The Cycle 26 Calibration Plan
- • E.13 The Cycle 27 Calibration Plan
- • E.14 The Cycle 28 Calibration Plan
- • Glossary