10.2 Using the Information in this Chapter

10.2.1 Sensitivity Units and Conversions

This chapter contains plots of throughputs for each imaging mode in Section 10.3.1. Section 9.2 explains how to use these throughputs to calculate expected count rates from your source.

The first figure for each imaging mode gives the integrated system throughput, which is the combination of the efficiencies of the detector and of the optical elements in the light path. The throughputs in this handbook are based in part on ground test data, although at the time of writing the handbook, the overall detector efficiency curve and most filter throughputs have been adjusted based on in-flight data. The total system unitless quantum efficiency, i.e., throughput, at any wavelength is defined as the probability that a monochromatic photon incident on the primary mirror produces a detected photo-electron. For the CCD, "counts" is the number of electrons detected. For the MAMA, "counts" is the number of valid photo-electron events processed by the detector electronics after passing through the various pulse-shape and anti-coincidence filters. In both cases, the detected counts obey Poisson statistics. The throughput includes all reflections and transmissions in the optical train (e.g., due to the HST secondary).

To recalculate the throughput with the most recent CCD QE tables in pysynphot or stsynphot, you can create total system throughput tables (instrument plus OTA) using the pysynphot.ObsBandpass or stsynphot.band classes.

A bandpass object is created by providing any valid obsmode command string as an argument to pysynphot.ObsBandpass or stsynphot.band. For example, to evaluate the throughput of the F475W filter and the WFC detector, chip 1, you would use the command:

>> import pysynphot
>> bp_F475W = pysynphot.ObsBandpass("acs,wfc1,f475w")

The resulting throughput table is stored as an attribute of the bp_F475W bandpass object, it is accessed by the command:

>> throughput_table = bp_F475W.throughput

Or, for stsynphot,

>> import stsynphot
>> bp_F475W = stsynphot.band("acs,wfc1,f475w")
>> throughput_table = bp_F475W(bp_F475W.waveset)

The ramp filters are not included in this chapter because the passband will change depending on the chosen central wavelength. The width of the passband and available range of central wavelengths for each ramp segment are listed in Table 5.2. The passbands for ramp filters can also be obtained using pysynphot or stsynphot.

10.2.2 Signal-to-Noise

For each imaging mode, plots are provided to estimate the signal-to-noise ratio (S/N) for a representative source, see Section 10.3.1. The first figure shows S/N for point sources (GAIN=1). The second figure shows S/N for uniform extended sources of area 1 arcsecond².

The different line styles in the S/N figures delineate regions where different sources of noise dominate. A particular source of noise (read noise for example) is presumed to dominate if it contributes more than half the total noise in the observations.

The point- and extended-source S/N figures are shown for average and low sky levels. The calculations do not take into account any potential losses due to imperfect CTE. For point sources, an aperture size of 5 × 5 pixel² is used for the WFC, 9 × 9 pixel² for HRC, and 15 × 15 pixel² for the SBC S/N evaluation. For extended sources, a 1 arcsecond² aperture is used. For the CCD, the readnoise is computed assuming a number of readouts  NREAD = integer (t/1000 seconds), where t is the exposure time, with a minimum  NREAD = 2. That is, each exposure has a minimum CR-SPLIT = 2. Different line styles in the figures are used to indicate which source of noise dominates.

To the left of the vertical line in the SBC S/N plots, the count rate from the source exceeds the 50 counts/second/pixel local count rate limit. This is computed from the model PSF, which gives 14% to 22% of the flux in the central pixel.

In situations requiring more detailed calculations (non-stellar spectra, extended sources, other sky background levels, unknown target V magnitude, etc.), the ACS ETC should be used, but keep in mind that the ETC does not incorporate CTE losses. See Section 4.3.8  and ISR 2024-02 for more information about how CTE may impact S/N for different background levels and source brightnesses.

Follow these steps to use the signal-to-noise plots:

1. Determine the AB magnitude of your source at the wavelength of interest. There are several ways to do this.
   - Examine Table 10.1, 10.2, or 10.3 and find AB_V for the desired spectral type and filter. Sum the V magnitude of the target and AB_V derived from the table.
   - Alternatively, compute ABMAG (= V + AB_V) from the source flux, using the relation
   

   (1)	$$//<![CDATA[ \begin{array}{l}\displaystyle \mathrm{ABMAG} = -2.5\log{f_{\nu}} - 48.60\end{array} //]]>$$

   or

   (2)	$$//<![CDATA[ \begin{array}{l}\displaystyle \mathrm{ABMAG} = -2.5\log{f_{\lambda}} - 5\log{\lambda} -2.406\end{array} //]]>$$

   where λ is pivot wavelength.

2. Find the appropriate plot for the filter in question, and locate V + AB_V on the horizontal axis. Then read off the signal-to-noise ratio for the desired exposure time, or vice-versa.
   

The "x" characters at the top of each plot indicate the onset of saturation, in the case of the CCD. For the MAMA detector, the "x" shows where the total number of counts exceeds the 16 bit buffer size of 65,535.

Note that the plots show the S/N as a function of source magnitude for exposure times as short as 0.1 seconds, although the minimum exposure time for the WFC CCD channel is 0.5 seconds.

10.2.3 Point Spread Functions

All information about the PSFs are based on the modeled encircled energy data presented in Section 5.6.