New NICMOS Readout Sequences Chun Xu Space Telescope Science Institute Abstract On 12 June 2005, four new NICMOS multi-accum sequences (SPARS4, SPARS16, SPARS32, SPARS128) were implemented in the NICMOS flight-software. All of the SPARS sequences have two initial readouts at and seconds followed by equally spaced readouts. Observations with the new sequences were carried out in August 2005 (Proposal 10721). The observed dark currents are well matched by the dark current model used in the present pipeline. The advantages of having these new sequences is that observers may choose timing patterns that optimize the handling of cosmic rays with equally spaced reads without having the extra amplifier glow caused by the logarithmic initial readouts of the current STEP sequences. This is especially desirable for fields with moderate to low dynamic range. Another advantage of these new sequences is a reduction in thermal variations that give rise to fluctuations in bias levels, called "shading". Introduction In order to make better use of the pre-defined readout sample sequences, NICMOS group added four new readout “sparse sampling” (or SPARS) sequences, called SPARS4, SPARS16, SPARS32 and SPARS128, in addition to the existing sequences SPARS64 and SPARS256. The new sequences replace the rarely used MIF sequences. The SPARS readout sequences are recommended for observations of fields where only faint objects are present. Another advantage of using SPARS readout sequences is that these sequences primarily sample the readout time equally so the data can be better calibrated (off the pipeline if necessary) as far as the shading is concerned. The SPARS readout sequences consist of 2 rapid initial readouts at times 0.303s and 0.606s, followed by multiple readouts at equally sampled time series. Table 1 lists all the time sequences for these new SPARS readouts. Table 1. New Sequences and Readout Time (seconds) SPARS SPARS SPARS SPARS Data Reduction and Analysis The observations of dark current took place in August All the observation were scheduled in SAA free orbits to avoid excessive cosmic ray persistency, with the three cameras operating in parallel. The data are calibrated through standard pipeline. The resulted ima files off the pipeline contain practically all the dark images at the corresponding readouts. These darks should be matched exactly by the model dark images used in the pipeline if the models are correct. To perform such comparison, we generate corresponding model dark images with calnica in iraf by setting keyword darkcorr to “PERFORM” in the raw files and setting writedark option to “yes” in calnica. We then compare the generated model dark images with the observed darks (i.e., ima files off the pipeline) extension by extension. Figure 1 shows the observed darks, model darks and their differences side by side, extension by extension, for all 3 cameras and all 4 new readout sequences. We only list first 9 out of 25 readouts to save the space. This figure shows visually how well the model matches the observed darks. Figure 2 plots the detailed differences between the observed darks and model darks. Figure 1. The comparison between the observed darks, the model darks and their differences. The 3 panel images are for 3 cameras, 4 columns in each panel are for 4 sequences. In each column, there are 3 sub-columns, which are observed darks, model darks and their differences. The smoothed difference images infer that the model and observed darks are well matched. Note that the there are some quadrant by quadrant offsets in the difference images, these offsets are caused by incomplete removal of the pedestal. Figure 2, The mean value (in DN) of the difference between observed and model darks are shown for each readout in a full 25 read sequence. Different SPARS sequences are color coded as indicated. In general, the model darks are consistent to better than +/- 10 DNs compared to observed darks. Differences are due to incomplete removal of pedestal and time-dependent effects related to the bus voltage and aft-shroud temperature. Acknowledgements: The author would like to thank Louis E. Bergeron, Ilana Dashevsky and Keith Noll for their valuable comments.