Depth vs. exposure time
The Pandeia JWST Exposure Time Calculator, ETC should be used for all observation planning. Here we present ETC results using the Python engine to loop through many calculations of signal-to-noise ratio (SNR) for various readout specifications and resulting exposure times, given assumptions detailed below. Then we interpolate among these calculations to determine depth (SNR = 10) vs. exposure time. The script is available on Github.
The NIRCam Imaging overview and NIRCam Sensitivity pages show 10-sigma SNR = 10 sensitivity estimates for imaging in all NIRCam filters given a total exposure time of 10 ks (166.7 minutes = 2.78 hours). Here we show similar estimates for a range of exposure times in 8 primarily wide filters.
Sensitivity estimates can vary significantly depending on the backgrounds and the assumed photometric aperture sizes as discussed below. Please use the Pandeia JWST Exposure Time Calculator, ETC, to plan your observations.
Figure 1. Depth vs. exposure time
Depth (10-sigma) vs. total exposure time for 4 exposures of NIRCam imaging in wide filters and one medium-band filter, assuming the "1.2 × MinZodi" background defined below. We assume point sources and photometric apertures of 0.08" (0.16") diameters with 0.6" – 0.99" (1.2" – 1.98") background sky annuli for the short (long) wavelength channel. Depths are interpolated from results obtained with the Pandeia JWST ETC Python engine. Dashed line extrapolations assume depth in units of flux goes as sqrt(t), or in magnitudes: depth = depth0 + 1.25 * log(t / t0). Note 5-sigma depth estimates are 0.75 mag fainter than the 10 σ estimates shown here.
JWST Backgrounds vary with the target coordinates (RA, Dec) and time of year. For these calculations, we assume the fiducial "1.2 × MinZodi" background: RA = 17:26:44, Dec = -73:19:56 on June 19, 2019, as used in the NIRCam Imaging overview. The background model for these observations must be generated using the online ETC GUI and then imported into the Python ETC engine.
Figure 2. Background vs. wavelength
Background vs. wavelength assumed in these calculations, as generated by the online ETC GUI for "1.2 × MinZodi" at (RA = 17:26:44, Dec = -73:19:56) on June 19, 2019.
Below are the recommended readout patterns and exposure times used for the calculations on this page. They are based on ETC calculations which show these to yield optimal signal-to-noise for a given exposure time. We find RAPID, BRIGHT2, SHALLOW4, MEDIUM8, and DEEP8 yield high signal-to-noise most efficiently and are preferred to maximize depth. The other readout patterns (BRIGHT1, SHALLOW2, MEDIUM2, DEEP2) may be preferred in some cases, for example to provide a greater dynamic range (with a shorter first group) to sample bright stars before saturation.
Table 1. Recommended readout specifications for maximal depth in a given exposure time when reading out the full detectors in both modules. (In this case, RAPID and BRIGHT2 are limited to 4 groups, DEEP8 is limited to 20 groups, and all other patterns are limited to 10 groups.) The final two columns assume 4 such exposures.
Figure 3. Signal-to-noise vs. exposure time for various readout patterns
Comparison of signal-to-noise for various readout specifications for F200W imaging of an AB mag 29 point source. Each point shows the estimate given 4 exposures, each comprising a single integration that consists of multiple groups (between 2 and 10) of a given readout pattern. This analysis shows that RAPID, BRIGHT2, SHALLOW4, MEDIUM8, and DEEP8 yield higher signal-to-noise than other patterns for a given exposure time. These are plotted as filled circles; the other patterns are plotted as stars. Note BRIGHT2 is restricted to a maximum of 4 groups when reading out the full detectors in both modes.
|Readout pattern||NGROUPS||NINT||Exposure time (s)||NEXP||Total exposure time (s)|