TIME_REQUIREMENTS · BUGS · SEE_ALSO
daopars -- edit the daophot fitting parameters
- function = "gauss"
- The functional form of the analytic component of the PSF model computed by the
DAOPHOT PSF task. The better this function matches the true PSF, especially in
the cores of the stars, the smaller the interpolation errors will be. The
choices are the following.
- An elliptical Gaussian function aligned along the x and y axes of the input image.
- An elliptical Moffat function with a beta parameter of 1.5.
- An elliptical Moffat function with a beta parameter of 2.5.
- An elliptical Lorentzian function with beta parameter of 1.0.
- A Gaussian core with Lorentzian wings function, where the Gaussian core may be tilted, but the Lorentzian wings are elongated along the x or y axes. The Lorentzian wings have a beta parameter of 1.0.
- A Gaussian core with Lorentzian wings function, where the Gaussian core and Lorentzian wings may be tilted in different directions. The Lorentzian wings have a beta parameter of 1.0.
- The PSF task computes the analytic PSF model for each of the six analytic model PSFs in turn and selects the one that produces the smallest standard deviation for the model fit.
- The PSF task computes the analytic PSF model for each of a subset of the six defined functions in turn, and selects the one that produces the smallest standard deviation for the model fit.
In general "gauss" is the best and most efficient choice for a well-sampled ground-based image, "lorentz" is best for old ST images, and "moffat15" or "moffat25" are best for under-sampled ground-based images.
- varorder = 0
- The order of variability of the PSF model computed by the DAOPHOT PSF task.
Varorder sets the number of look-up tables containing the deviations of the
true PSF from the analytic model PSF that are computed by the model.
- Only the analytic function specified by function is used to compute the PSF model. The PSF model is constant over the image.
- The analytic function and one look-up table are used to compute the PSF model. The PSF model is constant over the image.
- The analytic function and three look-up tables are used to compute the PSF model. The PSF model is linearly variable over the image, with terms proportional to 1, x and y.
- The analytic function and six look-up tables are used to compute the PSF model. The PSF model is quadratically variable over the image, with terms proportional to 1, x, y, x**2, xy, y**2.
- nclean = 0
- The number of additional iterations the PSF task performs to compute the PSF look-up tables. If nclean is > 0, stars which contribute deviant residuals to the PSF look-up tables in the first iteration, will be down-weighted in succeeding iterations.
- saturated = no
- Use saturated stars to improve the signal-to-noise in the wings of the PSF model computed by the PSF task? This parameter should only be set to "yes" where there are too few high signal-to-noise unsaturated stars in the image to compute a reasonable model for the stellar profile wings.
- matchrad = 3.(scale units)
- The tolerance in scale units for matching the stellar x and y centroids in the input photometry file with the image cursor position. Matchrad is currently used by the PSTSELECT and PSF tasks to match stars shown on the image display with stars in the photometry list.
- psfrad = 11.(scale units)
- The radius of the circle in scale units within which the PSF model is defined. Psfrad should be a pixel or two larger than the radius at which the intensity of the brightest star of interest fades into the noise. Psfrad can never be set larger than the size of the PSF model but may set smaller in tasks like GROUP, ALLSTAR, SUBSTAR, and ADDSTAR.
- fitrad = 3.(scale units)
- The fitting radius in scale units. Only pixels within the fitting radius of the center of a star will contribute to the fits computed by the PEAK, NSTAR and ALLSTAR tasks. For most images the fitting radius should be approximately equal to the FWHM of the PSF. Under severely crowded conditions a somewhat smaller value may be used in order to improve the fit. If the PSF is variable, the FWHM is very small, or sky fitting is enabled in PEAK and NSTAR on the other hand, it may be necessary to increase the fitting radius to achieve a good fit.
- recenter = yes (peak, nstar, and allstar)
- Compute new positions as well as magnitudes for all the stars in the input photometry list?
- fitsky = no (peak, nstar, and allstar)
- Compute new sky values for the stars in the input list (peak, nstar, allstar). If fitsky = "no", the PEAK, NSTAR, and ALLSTAR tasks compute a group sky value by averaging the sky values of the stars in the group. If fitsky = "yes", PEAK and NSTAR fit the group sky simultaneously with the positions and magnitudes. If fitsky = yes the ALLSTAR task computes new sky values for each star every third iteration by subtracting off the best current fit for the star and and estimating the median of the pixels in the annulus defined by sannulus and wsannulus . The new group sky value is the average of the new individual values.
- groupsky = yes (nstar and allstar)
- If groupsky is "yes", then the sky value for every pixel which contributes to the fit is identical and equal to the mean of the sky values of all the stars in the group. If groupsky is "no", then the sky value for every pixel which contributes to the fit is equal to the mean of the sky values of all the stars in the group for which that pixel is within one fitting radius.
- sannulus = 0.(scale units, allstar)
- The inner radius of the sky annulus used by ALLSTAR to recompute the sky values.
- wsannulus = (scale units, allstar)
- The width of the sky annulus used by ALLSTAR to recompute the sky values.
- flaterr=0.(percent, peak, nstar, allstar)
- The image flat-fielding error in percent used to compute the predicted errors of the fit.
- proferr = 5.(percent, peak, nstar, allstar)
- The profile or interpolation fitting error in percent used to compute the predicted errors of the fit.
- maxiter = (peak, nstar, allstar)
- The maximum number of times that the PSF fitting tasks PEAK, NSTAR, and ALLSTAR will iterate on the PSF fit before giving up.
- cliprange = 2.5, clipexp = 6.(peak, nstar, allstar)
- The parameters of the down-weighting scheme in the fitting code used to resist bad data. For values of clipexp greater than 1 a residual small compared to cliprange standard deviations does not have its weight significantly altered, one with exactly cliprange standard deviations is assigned half its normal weight, and large residuals are assigned weights which fall off as the standard deviation to the minus clipexp power. For normal applications users should leave these parameter at their default value.
- critsnratio = 1.(group)
- The ratio of the model intensity of the brighter star computed at a distance of one fitting radius from the center of the fainter star, to the expected random error computed from the readout noise, gain and value of the PSF. The critical signal-to-noise ratio parameter is used to group stars. In general if a small value such as 0.1 divides all the stars in an image into groups less than maxgroup , then the expected random errors will determine the accuracy of the photometry. On the other hand if a value of crtitcal overlap much greater than one is required to divide up the stars, crowding errors will dominate random errors. If a value of 1 is sufficient then crowding and random errors are roughly equivalent.
- mergerad = INDEF (scale units, nstar, allstar)
- The critical separation in scale units between two objects for an object merger to be considered. Objects with separations > mergerad will not be merged; faint objects with separations <= mergerad will be considered for merging. The default value of mergerad is sqrt (2 *(PAR1**2 + PAR2**2)), where PAR1 and PAR2 are the half-width at half-maximum along the major and minor axes of the psf model. Merging can be turned off altogether by setting mergerad to 0.0.
- maxnstar = (pstselect, psf, group, allstar, substar)
- The initial star list buffer size. If there are more than maxnstar stars in the input photometry file buffer, DAOPHOT will resize the buffers as needed. The only limitation is the memory and configuration of the host computer.
- maxgroup = (nstar, allstar)
- The maximum numbers of stars that the multiple star fitting tasks NSTAR and ALLSTAR will fit simultaneously. NSTAR will not to fit groups large than maxgroup. ALLSTAR dynamically regroups the stars in large groups until the group is either maxgroup or smaller in size or becomes too dense to group, after which the faintest stars are rejected until the group is less than maxgroup ins size.
DAOPARS is a parameter set task which stores the DAOPHOT parameters required by all those DAOPHOT tasks which compute the PSF model, fit stars to the PSF model, or evaluate the PSF model.
Typing DAOPARS on the terminal invokes the EPAR parameter editing task. The DAOPARS parameters may also be edited from within an EPAR command on task, for example PSF, which references them. The DAOPARS parameters may also be changed on the command line in the usual manner when any task which references them is executed.
Any given set of DAOPARS parameters may stored in a text file along with the data being reduced by typing the :w command from within the EPAR task. If the user then sets the value of the daopars parameter to the name of the file containing the stored parameter set, the stored parameters will be used instead of the default set in thes uparm directory.
The functional forms of the analytic PSF functions are as follows. The A is simply an amplitude or normalization constant The Pn are parameters which are fit during the PSF model generation process.
z = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 gauss = A * exp (-0.5 * z) z = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 + x * y * p3 moffat15 = A / (1 + z) ** 1.5 moffat25 = A / (1 + z) ** 2.5 z = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 + x * y * p3 lorentz = A / (1.0 + z) z = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 e = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 + x * y * p4 penny1 = A * ((1 - p3) / (1.0 + z) + p3 * exp (-0.693*e)) z = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 + p5 * x * y e = x ** 2 / p1 ** 2 + y ** 2 / p2 ** 2 + x * y * p4 penny2 = A * ((1 - p3) / (1.0 + z) + p3 * exp (-0.693*e))
The predicted errors in the the DAOPHOT photometry are computed per pixel as follows, where terms 1, 2, 3, and 4 represent the readout noise, the poisson noise, the flat-fielding error, and the interpolation error respectively. The quantities readnoise, epadu, I, M, p1, and p2 are the readout noise in electrons, the gain in electrons per ADU, the pixel intensity in ADU, the PSF model intensity in ADU, the FWHM in x and the FWHM in y, both in pixels.
error = sqrt (term1 + term2 + term3 + term4) (ADU) term1 = (readnoise / epadu) ** 2 term2 = I / epadu term3 = (.01 * flaterr * I) ** 2 term4 = (.01 * proferr * M / p1 / p2) ** 2
The radial weighting function employed by all the PSF fitting tasks is the following, where dx and dy are the distance of the pixel from the centroid of the star being fit.
wtr = 5.0 / (5.0 + rsq / (1.0 - rsq)) rsq = (dx ** 2 + dy ** 2) / fitrad ** 2
The weight assigned each pixel in the fit then becomes the following.
wtp = wtr / error ** 2
After a few iterations and if clipexp > 0, a clipping scheme to reject bad data is enabled. The weights of the pixels are recomputed as follows.
wt = wtp / (1.0 + (residual / error / chiold / cliprange) ** clipexp)
Pixels having a residual of cliprange sigma will have their weight reduced by half.
1. Print the DAOPARS task parameters.
da> lpar daopars
2. Edit the DAOPARS parameters.
3. Edit the DAOPARS parameters from with the PSF task.
da> epar psf ... edit a few psf parameters ... move to the daopars parameter and type :e ... edit the daopars parameters and type :wq ... finish editing the psf parameters and type :wq
4. Save the current DAOPARS parameter set in a text file daonite1.par. This can also be done from inside a higher level task as in the above example.
da> epar daopars ... type ":w daonite1.par" from within epar