by hayes » Sat Mar 03, 2012 3:03 pm
Hi Christopher,
I hope this is more helpful than it is long!
The first numbered items are very important. After these, I have other
suggestions, but the first ones must be addressed first, or your results may
not be accurate.
1. I see that you have all experiments normalized to experiment 1 within each
input. It seems that experiments 2 and 3 in the 140Nd input are at a different
beam energy from experiment 1. If you try to normalize an experiment to one
from another beam run, you will get ridiculous results, unless you have some
very good charge integration. If you do, please let me know so that I
understand.
2. I see that you have target-detection experiments normalized to other
experiments. This will not work in Gosia, but there will be no complaint in
the output. (This is in the new manual, but we have not added an error in the
code.) If you normalize a target-detection experiment to any other target- or
projectile-detection experiment, you will get systematic errors in chi-squared
that can be huge in some cases.
3. If you are able to accurately change these experiments to the equivalent
projectile scattering range, then you can normalize them together. If you do,
read the newest Gosia manual (OP,YIEL section) to see how to calculate the
normalization constants. Even if you measure them, you will need extra factors
that are not intuitive (but easy to calculate).
4. Be careful if you change to the equivalent projectile scattering range. I
haven't looked at your kinematics, but this can present accuracy problems,
which are described in the scattering theory section of the new manual. If you
don't want to do this, then normalize each experiment individually by setting
LN to its own number:
EXPT
3,60,140
-30,64,335.,20.0,3,1,0,0,360,1,1
-30,64,366.,-26.0,3,1,0,0,360,1,2 ! was 1
-30,64,366.,-23.0,3,1,0,0,360,1,3 ! was 1
Since gosia2 normalizes beam to target excitation, you will still have enough
sensitivity to the matrix elements, if you don't have too many fit parameters.
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The comments below may help, but the ones above should be considered/fixed before trying anything below.
I had trouble running your inputs. I think these files are not all from the
same calculation, because they reference 140Nd_Zn* and the yield file has only
2 experiments, while EXPT lists 3. But that is ok. I now see that it would
take a lot of time to repeat what you're doing, so I'll comment on what I read
in the input files. I may not have too much time to re-run more than a few
calculations.
Fit parameters:
I see 6 total free parameters in the inputs, and 8 data points (5 experiments
plus 3 previous measurements). I think that even in gosia2 the overall
normalization could be an additional parameter, but I sometimes get confused
about this. If so, then you have 7 free parameters in total. I'll think about
this again later. Usually, you need many more than 7 data points to fit 7
parameters, but with the high precision in the previous lifetime measurements,
it seems like 6 user-parameters might be ok.
My confusion is that with too many fit parameters, it seems like you would get
a much *larger* correlated error, since the additional free parameters should
overcompensate for a change in one individual parameter.
Maybe the following questions will help to clear this up:
Do you see major conflicts between some of the data? For instance, do you see
that a lifetime and a fitted matrix element are in large conflict?
You could try a test fit with more parameters locked. Do you get a sensible
correlated error in the one-dimensional method, and roughly the same error in
the 2D method? I get a lot of insight by calculating yields with OP,INTI and
then putting them back in the *.yld files to do test fits. If you can't do a
fit with simulated data, then you almost certainly will have a problem with the
real data, using the same fit parameters, etc.
Can you take a few more points around the minimum in the 1-D error calculation?
I see only 1 point within the 1-sigma limits on the pdf plot, if I understand
correctly. The shape may have a smaller slope around the minimum, and going up
to chi-squared = 10 probably doesn't help you to understand the shape around
the minimum.
How are the fits terminating? Sometimes OP,MINI parameters need to be
adjusted, such as if the fit terminates because it hit chi-squared=1. and the
best fit value is a little less than 1.
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Some other concerns:
It seems like you have data only for the 2-->0 transition, and you're using the
4+ state as a "buffer state." This is good.
You have a weight of 10 for the spectroscopic data (lifetimes, etc.). Do you
mean to weight them so heavily? This adjustable weight can be somewhat useful,
but I don't think that there is a real mathematical justification to pick any
number other than 1. The error bars should take care of the weighting, in my
opinion; changing the weight seems no different from changing the error bars,
and that should be done individually for each measurement, if at all. If there
is no justification I haven't thought of, then increasing their weight
artificially reduces the correlated error measurement.
You could try using OP,MAP every time you use OP,MINI. This will update for
the present set of matrix elements. I have found that this can be done in the
same step, preceding OP,MINI with OP,MAP:
OP,REST
0,0
OP,MAP
OP,MINI
2100....
It would be worth trying to run OP,CORR (with OP,INTI and NTAP = 3) at each
minimization step. Sometimes this is not too important, but it will be if the
correction factors change significantly as a function of the matrix elements.
Adam