If the input data are not a FITS primary image, this name must include the extension, eg: "foo.fits[EVENTS]".

If auto-naming is used (srclist=.), the output file will have the suffix "_src_stk"

If auto-naming is used (correl=.), the output file will have the suffix "_correl_stk"

If auto-naming is used (nbkg=.), the output file will have the suffix "_nbkg_stk"

If auto-naming is used (bkg=.), the output file will have the suffix "_bkg_stk".

If auto-naming is used (thresh=.), the output file will have the suffix "_thresh_stk".

"full": assume C_correct = C_measured - avg(W*(EB_norm)) + avg(W*B_norm)E C = correlation, at pixel in question E = exposure B_norm = normalized background W = wavelet function avg(W*(EB_norm)) equal to or greater than correlation of wavelet function and product of exposure and normalized background, at pixel in question avg(W*B_norm) equal to or greater than correlation of wavelet function and normalized background This correction is applied during each iteration, since B_norm changes. "fast": assume C_correct = C_measured - B_norm * avg(W*E)

expthresh should not be less than 0.1 (or else the accuracy of normalization will be too low); if set close to 1, only very limited regions of the FOV will be considered when source correlation maxima are listed. Typical values would be 0.1-0.2.

This parameter is only used *if* the user has provided a normalized background image. A value of 0 means use the exposure time as the background time.

Use a previously computed background in the specified file in place of constructing new per-scale normalized backgrounds.

The number of scale sizes (a series of numbers separated by spaces) indicated by xscales must be the same as yscales. The scale sizes themselves for a particular run do not have to be equal, though initial usage of asymmetric wavelets is not advised.

Wtransform produces a complete set of outputs for each scale. Small scales tend to detect small features, and larger scales larger features.

Values are typically 2**n, where n is an integer, and n_lo and n_hi are chosen with respect to instrumental PSF sizes. For the ROSAT PSPC, for instance, this could mean n_lo = 1 pixel, n_hi = 16 pixels. Note that larger scales might need to be used to characterize (i.e. derive good source property estimates for) extended sources.

2**n should not be larger than the size of the image (in pixels) divided by 5 (in which case the extent of the wavelet function is larger than the image itself, which could lead to strange results).

The number of scale sizes indicated by yscales must be the same as xscales. See additional notes under 'xscales'.

The user specifies how many iterations the program should go through to calculate the background. However, the background is for all intents and purposes calculated if there are very few new pixels being cleansed (see bkgsigthresh). If the ratio of *newly* cleansed pixels to the overall number of pixels in the mask is less than the parameter iterstop, the program quits iterating and uses the current background estimate as the final background estimate.

A typical value is 0.0001: stop iterating when only one of every ten thousand pixels is being cleansed. This parameter should not be larger than 1, nor smaller than one over the number of pixels in the image/mask.

*After* the final background estimate B_final is made, the threshold correlation is recomputed by solving for C_o: sigthresh = integral(C_o dC) PSD(C(B_final))

Here, C = avg(W*D), where D are the *raw* data. (The iterations are simply to compute background. Once we have that, we go back to the beginning.)

If C is greater than or equal to C_o in a pixel, then that pixel is considered to be associated with a source. If the pixel is also the location of a local maximum in C-space, the location of that maximum is output to a FITS table.

sigthresh should not be significantly larger than one over the number of pixels in the image/mask; for a 1024x1024 image, that means sigthresh ~ 1.e-6. If, in this case, sigthresh = 1.e-5, there will be approximately 10 strong background fluctuations detected as sources.

sigthresh should not be smaller than *roughly* 1.e-9 - 1.e-10; even at this point, the accuracy of the computed detection thresholds is unknown (though probably fine for most applications).

In each pixel, during each iteration, the background, and correlation of the wavelet function and the current data (C' = avg(W*D')), are estimated. The background estimate B' implies a probability sampling distribution (PSD) for C', i.e. the distribution that C' would have if there was locally no source in the image and D' were sampled from background. A threshold C_o' is calculated from this PSD, using the supplied value of bkgsigthresh (e.g. 0.01) by solving the following for C_o':

bkgsigthresh = integral(C_o' dC') PSD(C'(B'))

If

C' is greater than or equal to C_o'

, the data in the pixel is replaced with the background estimate. In this way, putative sources are eliminated from the data image, along with weak (but otherwise undetectable) sources and background fluctuations, so that a better background estimate can be made.

This value should be no larger than 0.05 (the usual 5%, or 95%, statistical criterion for rejecting the null hypothesis, which is that the pixel in question has data sampled solely from the background); it should not be smaller than sigthresh.