Makee: Combining Exposures

Individual exposures of a given object can be combined into a single continuous spectrum. A few techniques for accomplishing this are described here.

New page on using "spim2" to match spectra levels between orders and exposures.
The program " combine " can be used to create a single 1 dimensional spectrum from many 2 dimensional wavelength calibrated exposures (e.g. Flux-###.fits). The data is rebinned onto a linear or log-linear scale. For example, using the options "-loglin disp=2.1" would give you a log-linear scale with 2.1 km/sec per pixel.

Any overlapping regions between orders and exposures must be set to the same level. This could be accomplished by normalizing the continuum for all orders of each spectrum, for example. You could also scale the orders in a manner such that all the levels match, or simply clip off any overlapping regions (set error to "-1"). If the orders are not matched in some manner you will see discontinuities in the final spectrum.

You can also use the program "spim2" to help you match spectra between orders and exposures.

The error spectrum filename is found by assuming flux,error pairs such as: Flux-*.fits and Err-*.fits, or F-*.fits and E-*.fits, or FF-*.fits and FE-*.fits, or *.fits and *e.fits. Your filenames must be in one of these formats.

The data values are averaged and interpolated such that the approximate average level of the original 2-D spectra are maintained. For each new bin, a sum is created from all whole and fractional original pixels which fall within the new bin, and this sum is divided by the total number of whole and fractional pixels contributing.
Note: for example, if a half pixel falls within the new bin, half of the flux of that original pixel adds to the sum, and half a pixel is added to the pixel total.

The error values are found by converting to variances, creating a variance sum from all whole and partial original pixels which fall within the new bin, taking the square root of this sum, and then dividing by the total number of pixels contributing.
Note: this tends to overestimate the error slightly since it neglects the correlation between the rebinned pixels. A slight correction factor may be needed when fitting features of the spectrum using a chi-squared minimization.

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