Identifying and Blanking Noisy Bolometers

Figure: The extinction corrected data of IRC$+$10216 without despiking and sky removal. In this image the bolometers are on the x-axis and the y-axis is time. A strong signal is seen in the central bolometer (19) throughout the jiggle cycle, while neighboring bolometers (12, 13, 18, 20, 25, 26) pick up the signal only during part of the jiggle cycle. Sky noise variations, which occur at the same time for all pixels are easily seen, e.g. at the beginning of integration 2. This image is produced using the KAPPA display command with the command line option fill=true.

From the plot shown in Fig. we can see that the map suffers from some sky noise, e.g. visible as the dark striping at the beginning of integration 2. The central bolometer, 19 (h7) shows a clear signal, and we do not see any really bad (noisy) bolometers, except perhaps bolometer 23. Even so, we first need to blank out bad bolometers. There are several ways to identify bad bolometers. The easiest way, although it may not pick up all noisy bolometers for your particular map, is to run scunoise, which is a GUI that allows you to plot and identify all noisy bolometers using noise measurements done during the run. If we do this for the night of 19971208 we find a total of 9 noise measurements and we can see that some bolometers come and go, but 23 is always noisy. From the most nearby noise measurement, #88, we find that 23, 32 and 37 have noise levels above 100 nV and 12 is also about twice as noisy as the majority of the array, which should have a noise level around 40 nV. Before we set these bolometers to bad we plot the bolometers with mlinplot to check that these bolometers are indeed noisy in our map.

% mlinplot i86_lon_ext absaxs=2 lnindx='21:32'
YLIMIT - Vertical display limits /[-0.002588027,0.09385466]/ > 
DEVICE - Name of display device /@xwindows/ >

In the above example the bolometers 21 - 32 are plotted as a function of integration time (Fig. ). Bolometer 23 is indeed the noisiest pixel, but 22, and 24 are noisy as well and actually worse than 32, which was picked up by scunoise. Fig. shows strong sky noise, which makes it difficult to see the true noise level of the bolometers, and it is often wise to do a sky noise reduction (see below), so that one can more easily identify noisy bolometers. Go through the whole array by choosing suitable bolometer ranges with the parameter lnindx. In this example we choose to only blank out bolometer 23, which we set to bad using change_quality. Bolometers 8 and 14 also appear noisy, more so than 12, which we picked up in the noise measurement, but for the time being we let them stay. When we go through the bolometers with mlinplot we can also see a few spikes, which we will deal with shortly. Let us now set bolometer 23 with change_quality. Note that we need to surround the file name with both single and double quotes if we list more than one bolometer.

Figure: Using mlinplot to display a set of bolometers as a function of time is a good way to identify noisy bolometers. From this plot we can see that bolometers 22 - 24 are noisier than the rest of the sample. Note that bolometers 25 and 26, which are in the first ring surrounding the central bolometer, pick up the source at certain jiggle positions and this signal should not be confused with noise, which is random and erratic. One can also see the strong sky noise variations, which repeat the same signature for each bolometer, e.g. the dip in signal at the beginning of integration 2.

% change_quality "'i86_lon_ext{b23}'"
SURF: run 86 was a MAP observation of IRC+10216
SURF: file has data for 37 bolometers, measured at 192 positions.
 - there are data for 4 exposure(s) in 3 integration(s) in 1 
measurements.
BAD_QUALITY - Set quality to bad? (No will set quality to good) /YES/ 
>