ASTR 3520 Aperture Extraction example

Example aperture extraction with a dark-subtracted science image and a dark subtracted cal lamp spectrum. For answers to questions you get asked on the command line, click the links below...

hedit [SCIENCE].fits field=DISPAXIS value=1 add+ noao (package) twodspec (package) apextract (package) apall [SCIENCE].fits output=[SCIENCESPEC].fits onedspec (package) scombine [SCIENCESPEC]* [SCIENCESPEC]_comb combine=median hedit [CALLAMP].fits field=DISPAXIS value=1 add+ apall [CALLAMP].fits output=[CALLAMPSPEC].fits ref=[SCIENCE].fits trace- edit- identify [CALLAMPSPEC].fits coordlist='linelists$henear.dat' reidentify [CALLAMPSPEC].fits [CALLAMPSPEC].fits verbose+ interactive+ If you have more than 1 aperture hedit [SCIENCESPEC].fits REFSPEC1 "[CALLAMPSPEC].fits" add+ dispcor [SCIENCESPEC].fits [SCIENCESPEC_DISPCOR].fits splot [SCIENCESPEC_DISPCOR].fits

The above are:
SCIENCE: fully reduced 2D science spectrum
CALLAMP: fully reduced 2D calibration lamp exposure
SCIENCESPEC: aperture-extracted science spectrum
SCIENCESPEC*: aperture-extracted science spectrum for EACH 2D reduced science image (i.e. repeat steps 1-5 for each science image)
CALLAMPSPEC: aperture-extracted cal lamp spectrum
SCIENCESPEC_DISPCOR: aperture-extracted, dispersion-corrected science spectrum

Extracting your Aperture

Most important commands:

f: change to fit window
j: plot residuals
:order N change fit to order number N
:func FUNCTION change fitting function (e.g. spline3,legendre)
q: quit / move on to the next step

You'll be prompted will a few questions. Answer yes to all of them. Find apertures for Balog_W5WC300s.0052b? ('yes'): Number of apertures to be found automatically (3): 1 Pick a number of apertures corresponding to the number of stars Resize apertures for Balog_W5WC300s.0052b? ('yes'): y Edit apertures for Balog_W5WC300s.0052b? ('yes'): y

Selecting your aperture

Selecting your aperture: zoomed in. Make sure you include the whole thing, and fit the background using b. When you are through, press q

The next step is fitting the aperture trace

Look at the residual for that attempt (use j to get residual plots)

As you go to higher orders (using the command :order n where n is an integer), the residual pattern changes

Finally, a residual pattern that appears relatively random - this is your goal

You can tell the fit is fairly good

A 2nd order spline fit is about as good as a 5th order Chebyshev polynomial in this case, but either choice is OK

This shows the fit divided by the linear part of the fit to show you nonlinear components. When you're through press q

`apall` with the cal lamp

When extracting the calibration lamp aperture, you do NOT want to find, move, edit, or trace your aperture: answer NO to all of those questions. You DO want to write to database, extract, and review your spectrum. Find apertures for Balog_W5WC300s.0052b? ('yes'): n Resize apertures for Balog_W5WC300s.0052b? ('yes'): n Edit apertures for Balog_W5WC300s.0052b? ('yes'): n Extract aperture spectra for focus_dsub? ('yes'): y

Identifying Calibration Lamp Lines

Common key commands:

m: mark a line
d: delete a line
f: go to fitting window
l: autoidentify other lines
i: initialize (start over)

My successful line identification. (compare to the NOAO spectrum corresponding to my fit, the resemblance is excellent)
Don't get discouraged - it took me nearly an hour of dozens of iterations to complete this successfully. You should use the IRAF HeNeAr linelist linelist$henear.dat instead of the SBO line list.
A few tips:
1. Fit few points at first using m
2. quickly fit using f and check with q,l in the identified lines.
3. If the fit doesn't look right, use i to initialize and start over
4. Use a 2nd-order polynomial, not a spline
5. ***When comparing to SBO plots, zoom in fairly deep. The SBO plots are scaled very poorly
If the SBO plots fail for you, try the Kitt Peak spectral atlas

You want your fit to be approximately linear. Use hjkl to change plot type.

Your residuals should look approximately like noise. If you recognize a pattern, you should use a higher order fit. You can delete outliers that will mess up the fit using d

reidentify

Example: reidentify HeNeArCal_blue.ms.fits HeNeAr_sum.ms.fits interactive=yes
Reidentify will apply a wavelength solution stored in the database/id[filename] file to all apertures in the 'image' file.

If, when you pass the verbose+ option, you get this error:
** Too many features lost **
try redoing the command with the option nlost=5 or some other number. This tells the command that it should still write the solution to the database if it loses some of the features you identified.

Extracted spectra

Before dispersion correction (dispcor)

After dispersion correction using the correct lamp