The intermediate band blockers are used for high resolution work (R<500). The narrow band blockers can also be used but they restrict the available field of view as well as the spectral coverage. The broadband blockers (e.g. BVR) are used for low resolution work and require a slightly different calibration procedure.
The TTF is tuned by setting the "z" offset value. But first we need to calibrate the linear "z" scale against arc lines of known wavelength. Some of the BTTF blockers are the property of the WHT (see here for more details) so be sure we have these at site.
Assuming you have dialled up the spectral resolution you want, here is a reliable way to wavelength calibrate the TTF response in about 10 mins or less. The colours refer to the TAURUS control window or the CCD control window. (In fact, you can type all of the commands from the TAURUS window if you so choose.) Recall that the obeyw command is typed after hitting "." on the keypad: these are in-line commands. Of course, you can perform all the same actions with cursor clicks in the SMS control window.
First, switch in the calibration lamp mirror (chimney flap) and turn on your preferred calibration source shown here.
We
generate a sequence of little 6x3 postage stamp images over a series of
consecutive wavelengths. We
then stack the images in iraf and fit to the emission lines along the third
dimension. (If
the lamp is faint, esp. in U and B bands, try using the MITLL_ON_OFF_LARGE
window which is 60x30 format.)
obeyw taurus focal ? Select the spectral region with an order sorting filter (e.g. B, V, R0)
obeyw taurus etalon ? Ensure you have the correct TTF in the beam
obeyw taurus pupil 8 Ensure you have the clear pupil
obeyw taurus aperture 1 Ensure you have the big aperture
From the CCD window, type...
win mitll_on_off Set the CCD window size in the CCD window (read speed irrelevant)
time
1
You must set exposure time or the run sequence
will bomb (this is a
SMS bug that I
have long wanted TJF to fix) and you will need to
exit the entire system
obj ? Set the new object name to CuAr sausage cube; filter ?
For the next command, we normally initiate
from the SMS window using the pull down menus:
Follow the horrible pull downs to find......numbers are etalon z
values start, increment, stop.
run / run_ccd / run_step / 0 10 790
A much better way is to sidestep the SMS window
pull-downs completely using in-line commands
(note that "obeyw taurus" is not required)
ccd_runstep
0 10 790
Helpful hint: you can bypass all SMS window
functions with in-line commands.
We only need SMS control window for the complex charge shuffle sub-window
In the example above, the run sequence generates 80 images in rapid succession.
It makes sense to work with the files that the unix_server creates, instead of copying them over to your iraf work area. So now we stack the little images into a sausage cube within iraf. Under iraf, type
Be sure that the number of files matches the expected number of calibration steps as the unix_server sometimes skips an image. If this is the case, simply duplicate one of the images to take the place of the missing image (avoiding emission lines).
Now stack the images into a cube structure, and bin up the left hand and right hand 3x3 regions into separate spectra for fitting.
For the CCD window mitll_on_off, this perl script is particularly useful as it carries out the above commands in one hit, and also checks to see if the actual files exist. Download the file to your work area and type chmod +x sausage.p. If you are using mitll_on_off_large, use this perl script.
(Advanced tip: If you do all of your reduction within IRAF, declare sausage as a "foreign task" within the login.cl file and simply run from within IRAF. This looks like "task $sausage = sausage.pl" and "task $sausage = "$foreign".)
Within IRAF or under unix, if you type "sausage", you will see
Usage: sausage [-d dir] [-s suffix] id date start endTo generate the above file sequence, type
sausage -d /data/ssf/1/obsred/iraf/ccd_1/980825 17 25aug 7 87 (hint: rather than typing in the directory every time, edit it into the perl script at "dir")The "id" is simply an identification number; sausage now creates a file called LIST17 and reports back about any missing files. It also returns the exact command sequence you need to run within IRAF, i.e.
Now type:So grab with the mouse and execute these within IRAF.
imstack @LIST17 cube17
blkavg cube17[4:6,*] spec17
splot spec17
From an xgterm window, plot the central spectrum and fit the line profiles.
onedspec
splot spec1
(splot has a lot of cursor options: see
here)
This is a low resolution spectrum (Zc = -2) of the CuAr lamp taken through the I6 filter. The x-axis needs to be transformed to a z-axis. Type "p" and then enter the first and last "z" values of the sequence run. You should now get
Now we fit the profiles.
For old IRAF installations,
place the cursor on the flat part on either side of the 3-line group and
type "d". Mark each peak with "m".
In
the latest IRAF release (v. 2.11 for Solaris 2.6), splot now permits
lorentzian fitting by marking each peak with "l".
Now type "q", "a", "a", "n" to get . . . . . .
These are gaussian fits to lorentzian profiles. (Without the new splot, an improvement is to use lorentzian fitting in the nmisc package.) The fitted line centroids in units of "z" are given at the bottom. Cycle through them forwards using "+" or backwards using "-". To exit, keep hitting "q".
The splot package writes the line wavelengths and centres to a file splot.log., e.g.
Aug 27 14:51 [spec1[1,1,*]]:Strip the first two lines, and insert the correct wavelengths in the first column, i.e.
center cont flux eqw core sigma fwhm
365.3387 489.781 148264. -302.7 6689.07 8.843 20.82
414.65 489.781 46857.7 -95.67 2489.56 7.509 17.68
484.3594 489.781 14272.3 -29.14 782.244 7.279 17.14
8416 365.3387 489.781 148264. -302.7 6689.07 8.843 20.82and perform a least-squares fit
8521.4 414.65 489.781 46857.7 -95.67 2489.56 7.509 17.68
8668 484.3594 489.781 14272.3 -29.14 782.244 7.279 17.14
chi
sqr: 0.15791 ftest: 45220.6 correlation: 0.99999
stats
nr
pts: 3. std dev res: 0.280992
x(data)
y(calc) y(data) sigy(data)
data points vs. fitted points
8416. 365.212
365.3 0.
8521.4 414.923 414.7
0.
8668. 484.065
484.4 0.