Fitting Commercial Molybdenum Disulfide
Hi Mailing list, I'm back at fitting commercial Molybdenum Disulfide to a Hexagonal p 63/m m c space group. Although my present fit is reasonable, thanks to the mailing list in August, I was hoping someone might have more suggestions to optimize it. Also, in order to better my fit, I still have seperate e0's, one for the first path, one for the remaining 2 paths. I haven't gotten a satisfactory physical explanation for this and I thought someone might have a good one. Also, the data on the same sample from two different beamlines seems to be somewhat different, namely the Reduced chi-square is very different (75.6 in one, 636.4 in the other). In both cases I use the first 3 single scattering paths. I may have included too much info, but better too much than too little. Thank You, Dan Carter Here are my parameters in both cases, values differ somewhat: Set: Amp = 0.7 guess: eO ~ 1.57 (1.52) (used for first path) guess: eO_1 ~ -4.77 (2.43) (used for remaining 2 paths) guess: ss ~ 0.0027 (0.000427) guess: alpha ~ -0.004 (0.003255) (positive for second fit) delr_1: alpha*reff First Fit: Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:10:57 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6 ============================================================ Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 779.303886008 Reduced Chi-square = 75.568861674 R-factor = 0.041210572 Measurement uncertainty (k) = 0.000827712 Measurement uncertainty (R) = 0.022111003 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0_1 = -4.7737110 +/- 2.4268840 (guessed as -4.773687 (2.426889)) ss = 0.0027360 +/- 0.0004270 (guessed as 0.002736 (0.000427)) e0 = 1.5684690 +/- 1.5209160 (guessed as 1.568507 (1.520918)) alpha = -0.0040060 +/- 0.0032550 (guessed as -0.004006 (0.003255)) Def parameters: delr_1 = -0.0158980 Set parameters: amp = 0.7 Correlations between variables: e0_1 and alpha --> 0.8622 e0 and alpha --> 0.7870 e0_1 and e0 --> 0.7064 All other correlations are below 0.25 Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:10:57 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6 ============================================================ Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 779.303886008 Reduced Chi-square = 75.568861674 R-factor = 0.041210572 Measurement uncertainty (k) = 0.000827712 Measurement uncertainty (R) = 0.022111003 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0_1 = -4.7737110 +/- 2.4268840 (guessed as -4.773687 (2.426889)) ss = 0.0027360 +/- 0.0004270 (guessed as 0.002736 (0.000427)) e0 = 1.5684690 +/- 1.5209160 (guessed as 1.568507 (1.520918)) alpha = -0.0040060 +/- 0.0032550 (guessed as -0.004006 (0.003255)) Def parameters: delr_1 = -0.0158980 Set parameters: amp = 0.7 Correlations between variables: e0_1 and alpha --> 0.8622 e0 and alpha --> 0.7870 e0_1 and e0 --> 0.7064 All other correlations are below 0.25 ===== Data set >>merge 6_17_04.chi<< ======================================== file: C:\Ifeffit\horae\stash\artemis.project.3\chi_data\merge 6_17_04.chi title lines: Athena data file -- Athena version 0.8.032 Saving merge 6_17_04 (group=merge) as chi(k) . Element=Mo Edge=K Background parameters . E0=20007.492 Eshift=0.000 Rbkg=1.000 . Standard=None . Kweight=2.0 Edge step=0.749 . Fixed step=no Flatten=yes . Pre-edge range: [ -150.000 : -75.000 ] . Normalization range: [ 150.000 : 895.199 ] . Spline range: [ 0.952 : 995.207 ] Clamps: None/Strong Foreward FT parameters . Kweight=2 Window=kaiser-bessel Phase correction=no . k-range: [ 2.000 : 16.100 ] dk=0.00 Backward FT parameters . R-range: [ 1.000 : 3.000 ] . dR=0.00 Window=kaiser-bessel Plotting parameters . Multiplier=1 Y-offset=0.000 . Merge in k space of: ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.101 (mos2_101) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.102 (mos2_102) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.103 (mos2_103) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.104 (mos2_104) k-range = 2.000 - 13.500 dk = 2.000 k-window = kaiser-bessel k-weight = 2 R-range = 1.000 - 3.000 dR = 0.100 R-window = kaiser-bessel fitting space = R background function = none phase correction = none R-factor for this data set = 0.74613 Second Fit: Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:23:36 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6 ============================================================ Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 6562.747150466 Reduced Chi-square = 636.387602469 R-factor = 0.032979787 Measurement uncertainty (k) = 0.000181935 Measurement uncertainty (R) = 0.004860095 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0_1 = -4.0720090 +/- 1.9194040 (guessed as -4.773711 (2.426884)) ss = 0.0046360 +/- 0.0004110 (guessed as 0.002736 (0.000427)) e0 = 1.7659580 +/- 1.1543880 (guessed as 1.568469 (1.520916)) alpha = 0.0010900 +/- 0.0028430 (guessed as -0.004006 (0.003255)) Def parameters: delr_1 = 0.0043250 Set parameters: amp = 0.7 Correlations between variables: e0_1 and alpha --> 0.8416 e0 and alpha --> 0.8045 e0_1 and e0 --> 0.7141 All other correlations are below 0.25 ===== Data set >>merge 10_15_04.chi<< ======================================== file: C:\Ifeffit\horae\stash\artemis.project.3\chi_data\merge 10_15_04.chi title lines: Athena data file -- Athena version 0.8.032 Saving merge 6_17_04 (group=merge) as chi(k) . Element=Mo Edge=K Background parameters . E0=20007.492 Eshift=0.000 Rbkg=1.000 . Standard=None . Kweight=2.0 Edge step=0.749 . Fixed step=no Flatten=yes . Pre-edge range: [ -150.000 : -75.000 ] . Normalization range: [ 150.000 : 895.199 ] . Spline range: [ 0.952 : 995.207 ] Clamps: None/Strong Foreward FT parameters . Kweight=2 Window=kaiser-bessel Phase correction=no . k-range: [ 2.000 : 16.100 ] dk=0.00 Backward FT parameters . R-range: [ 1.000 : 3.000 ] . dR=0.00 Window=kaiser-bessel Plotting parameters . Multiplier=1 Y-offset=0.000 . Merge in k space of: ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.101 (mos2_101) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.102 (mos2_102) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.103 (mos2_103) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.104 (mos2_104) k-range = 2.000 - 13.500 dk = 2.000 k-window = kaiser-bessel k-weight = 2 R-range = 1.000 - 3.000 dR = 0.100 R-window = kaiser-bessel fitting space = R background function = none phase correction = none R-factor for this data set = 0.71585 Paths used to fit Both Data Sets, degen, s02 the same. other values different: F1 p 63/m m c: feff0001.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0001.dat id = reff= 2.4135, nlegs= 2, path= Mo<->S label = r = 2.416130 degen = 6.000000 s02 = 0.700000 e0 = 1.765958 dr = 0.002630 ss2 = 0.004636 F1 p 63/m m c: feff0002.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0002.dat id = reff= 3.1500, nlegs= 2, path= Mo<->Mo label = r = 3.153433 degen = 6.000000 s02 = 0.700000 e0 = -4.072009 dr = 0.003433 ss2 = 0.004636 F1 p 63/m m c: feff0003.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0003.dat id = reff= 3.9683, nlegs= 2, path= Mo<->S label = r = 3.972625 degen = 6.000000 s02 = 0.700000 e0 = -4.072009 dr = 0.004325 ss2 = 0.004636
Dan, Everyone else... I have two suggestions for optimizing the fit. First, from looking at the crystal structure of the MoS2, I would probably not use the same sigma-squared for all three paths. The crystal structure I looked at online had the Mo connected to various Sulfur atoms, but not directly connected to the other Mo atoms, for example. If this is the case, the motion of the Mo and its neighboring S atoms should be much more highly correlated than the motion of th Mo and the nearest Mo atoms-- So, the Mo-S path should have a smaller debye-waller factor than the Mo-Mo path. Second, you're using alpha*reff for your delr term. Are you sure that the only difference between your model and your actual structure is an isotropic expansion? If bond angles change you won't be able to make that assumption, and from looking at the MoS2 structure, that seems likely. So, in summary, I'd use a different sigma-squared term for each path, and try to make a more sophisticated guess about the form of the delr terms. Hope this helps. Mike Groves On Thu, 28 Oct 2004 dmc@pdx.edu wrote:
Hi Mailing list,
I'm back at fitting commercial Molybdenum Disulfide to a Hexagonal p 63/m m c space group. Although my present fit is reasonable, thanks to the mailing list in August, I was hoping someone might have more suggestions to optimize it. Also, in order to better my fit, I still have seperate e0's, one for the first path, one for the remaining 2 paths. I haven't gotten a satisfactory physical explanation for this and I thought someone might have a good one.
Also, the data on the same sample from two different beamlines seems to be somewhat different, namely the Reduced chi-square is very different (75.6 in one, 636.4 in the other). In both cases I use the first 3 single scattering paths. I may have included too much info, but better too much than too little.
Thank You, Dan Carter
Here are my parameters in both cases, values differ somewhat:
Set: Amp = 0.7 guess: eO ~ 1.57 (1.52) (used for first path) guess: eO_1 ~ -4.77 (2.43) (used for remaining 2 paths) guess: ss ~ 0.0027 (0.000427) guess: alpha ~ -0.004 (0.003255) (positive for second fit) delr_1: alpha*reff
First Fit:
Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:10:57 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6
============================================================
Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 779.303886008 Reduced Chi-square = 75.568861674 R-factor = 0.041210572 Measurement uncertainty (k) = 0.000827712 Measurement uncertainty (R) = 0.022111003 Number of data sets = 1.000000000
Guess parameters +/- uncertainties (initial guess): e0_1 = -4.7737110 +/- 2.4268840 (guessed as -4.773687 (2.426889)) ss = 0.0027360 +/- 0.0004270 (guessed as 0.002736 (0.000427)) e0 = 1.5684690 +/- 1.5209160 (guessed as 1.568507 (1.520918)) alpha = -0.0040060 +/- 0.0032550 (guessed as -0.004006 (0.003255))
Def parameters: delr_1 = -0.0158980
Set parameters: amp = 0.7
Correlations between variables: e0_1 and alpha --> 0.8622 e0 and alpha --> 0.7870 e0_1 and e0 --> 0.7064 All other correlations are below 0.25
Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:10:57 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6
============================================================
Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 779.303886008 Reduced Chi-square = 75.568861674 R-factor = 0.041210572 Measurement uncertainty (k) = 0.000827712 Measurement uncertainty (R) = 0.022111003 Number of data sets = 1.000000000
Guess parameters +/- uncertainties (initial guess): e0_1 = -4.7737110 +/- 2.4268840 (guessed as -4.773687 (2.426889)) ss = 0.0027360 +/- 0.0004270 (guessed as 0.002736 (0.000427)) e0 = 1.5684690 +/- 1.5209160 (guessed as 1.568507 (1.520918)) alpha = -0.0040060 +/- 0.0032550 (guessed as -0.004006 (0.003255))
Def parameters: delr_1 = -0.0158980
Set parameters: amp = 0.7
Correlations between variables: e0_1 and alpha --> 0.8622 e0 and alpha --> 0.7870 e0_1 and e0 --> 0.7064 All other correlations are below 0.25
===== Data set >>merge 6_17_04.chi<< ========================================
file: C:\Ifeffit\horae\stash\artemis.project.3\chi_data\merge 6_17_04.chi title lines: Athena data file -- Athena version 0.8.032 Saving merge 6_17_04 (group=merge) as chi(k) . Element=Mo Edge=K Background parameters . E0=20007.492 Eshift=0.000 Rbkg=1.000 . Standard=None . Kweight=2.0 Edge step=0.749 . Fixed step=no Flatten=yes . Pre-edge range: [ -150.000 : -75.000 ] . Normalization range: [ 150.000 : 895.199 ] . Spline range: [ 0.952 : 995.207 ] Clamps: None/Strong Foreward FT parameters . Kweight=2 Window=kaiser-bessel Phase correction=no . k-range: [ 2.000 : 16.100 ] dk=0.00 Backward FT parameters . R-range: [ 1.000 : 3.000 ] . dR=0.00 Window=kaiser-bessel Plotting parameters . Multiplier=1 Y-offset=0.000 . Merge in k space of: ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.101 (mos2_101) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.102 (mos2_102) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.103 (mos2_103) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.104 (mos2_104)
k-range = 2.000 - 13.500 dk = 2.000 k-window = kaiser-bessel k-weight = 2 R-range = 1.000 - 3.000 dR = 0.100 R-window = kaiser-bessel fitting space = R background function = none phase correction = none
R-factor for this data set = 0.74613
Second Fit:
Project title : Fitting merge 10_15_04.chi Comment : Prepared by : Contact : Started : 11:49:36 on 22 October, 2004 This fit at : 11:23:36 on 28 October, 2004 Environment : Artemis 0.7.010 using Windows 2000, perl 5.006001, Tk 800.024, and Ifeffit 1.2.6
============================================================
Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 6562.747150466 Reduced Chi-square = 636.387602469 R-factor = 0.032979787 Measurement uncertainty (k) = 0.000181935 Measurement uncertainty (R) = 0.004860095 Number of data sets = 1.000000000
Guess parameters +/- uncertainties (initial guess): e0_1 = -4.0720090 +/- 1.9194040 (guessed as -4.773711 (2.426884)) ss = 0.0046360 +/- 0.0004110 (guessed as 0.002736 (0.000427)) e0 = 1.7659580 +/- 1.1543880 (guessed as 1.568469 (1.520916)) alpha = 0.0010900 +/- 0.0028430 (guessed as -0.004006 (0.003255))
Def parameters: delr_1 = 0.0043250
Set parameters: amp = 0.7
Correlations between variables: e0_1 and alpha --> 0.8416 e0 and alpha --> 0.8045 e0_1 and e0 --> 0.7141 All other correlations are below 0.25
===== Data set >>merge 10_15_04.chi<< ========================================
file: C:\Ifeffit\horae\stash\artemis.project.3\chi_data\merge 10_15_04.chi title lines: Athena data file -- Athena version 0.8.032 Saving merge 6_17_04 (group=merge) as chi(k) . Element=Mo Edge=K Background parameters . E0=20007.492 Eshift=0.000 Rbkg=1.000 . Standard=None . Kweight=2.0 Edge step=0.749 . Fixed step=no Flatten=yes . Pre-edge range: [ -150.000 : -75.000 ] . Normalization range: [ 150.000 : 895.199 ] . Spline range: [ 0.952 : 995.207 ] Clamps: None/Strong Foreward FT parameters . Kweight=2 Window=kaiser-bessel Phase correction=no . k-range: [ 2.000 : 16.100 ] dk=0.00 Backward FT parameters . R-range: [ 1.000 : 3.000 ] . dR=0.00 Window=kaiser-bessel Plotting parameters . Multiplier=1 Y-offset=0.000 . Merge in k space of: ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.101 (mos2_101) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.102 (mos2_102) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.103 (mos2_103) ** C:/USERS/Dan2/raw data MoS2 6_17_04/MoS2.104 (mos2_104)
k-range = 2.000 - 13.500 dk = 2.000 k-window = kaiser-bessel k-weight = 2 R-range = 1.000 - 3.000 dR = 0.100 R-window = kaiser-bessel fitting space = R background function = none phase correction = none
R-factor for this data set = 0.71585
Paths used to fit Both Data Sets, degen, s02 the same. other values different:
F1 p 63/m m c: feff0001.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0001.dat id = reff= 2.4135, nlegs= 2, path= Mo<->S label = r = 2.416130 degen = 6.000000 s02 = 0.700000 e0 = 1.765958 dr = 0.002630 ss2 = 0.004636
F1 p 63/m m c: feff0002.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0002.dat id = reff= 3.1500, nlegs= 2, path= Mo<->Mo label = r = 3.153433 degen = 6.000000 s02 = 0.700000 e0 = -4.072009 dr = 0.003433 ss2 = 0.004636
F1 p 63/m m c: feff0003.dat .. feff = C:\Ifeffit\horae\stash\artemis.project.3\data0.feff2\feff0003.dat id = reff= 3.9683, nlegs= 2, path= Mo<->S label = r = 3.972625 degen = 6.000000 s02 = 0.700000 e0 = -4.072009 dr = 0.004325 ss2 = 0.004636 _______________________________________________ Ifeffit mailing list Ifeffit@millenia.cars.aps.anl.gov http://millenia.cars.aps.anl.gov/mailman/listinfo/ifeffit
Mike & list, Where can I find a reasonable model for my delr terms, if I do assume that the bond angles are changing/there is no isotropic expansion? Is there a description somewhere in the archives that I could look at? Creating different sigma squared parameters for each path did help the R- Factor somewhat, but not the reduced chi. Here's some data: Fit with seperate sigma's: Independent points = 14.312500000 Number of variables = 6.000000000 Chi-square = 5778.957783654 Reduced Chi-square = 695.212966455 R-factor = 0.029041008 Measurement uncertainty (k) = 0.000181935 Measurement uncertainty (R) = 0.004860095 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0_1 = -4.0347670 +/- 2.9998980 (guessed as -4.072009 (1.919404)) ss = 0.0042860 +/- 0.0005990 (guessed as 0.004636 (0.000411)) e0 = 1.6770760 +/- 1.3230290 (guessed as 1.765958 (1.154388)) alpha = 0.0009180 +/- 0.0034050 (guessed as 0.001090 (0.002843)) ss2 = 0.0048160 +/- 0.0006910 (0.0000) ss3 = 0.0225310 +/- 0.0657250 (0.0000) Def parameters: delr_1 = 0.0036440 Set parameters: amp = 0.7 Correlations between variables: e0_1 and alpha --> 0.8542 e0 and alpha --> 0.8470 e0_1 and e0 --> 0.7407 e0_1 and ss3 --> 0.7060 alpha and ss3 --> 0.4715 ss2 and ss3 --> -0.4214 e0_1 and ss2 --> -0.3892 e0 and ss3 --> 0.3772 alpha and ss2 --> -0.2680 All other correlations are below 0.25 Fit without (original): Independent points = 14.312500000 Number of variables = 4.000000000 Chi-square = 6562.747150128 Reduced Chi-square = 636.387602437 R-factor = 0.032979787 Measurement uncertainty (k) = 0.000181935 Measurement uncertainty (R) = 0.004860095 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): e0_1 = -4.0720130 +/- 1.9194050 (guessed as -4.072009 (1.919404)) ss = 0.0046360 +/- 0.0004110 (guessed as 0.004636 (0.000411)) e0 = 1.7659560 +/- 1.1543880 (guessed as 1.765958 (1.154388)) alpha = 0.0010900 +/- 0.0028430 (guessed as 0.001090 (0.002843)) Def parameters: delr_1 = 0.0043250 Set parameters: amp = 0.7 Correlations between variables: e0_1 and alpha --> 0.8416 e0 and alpha --> 0.8045 e0_1 and e0 --> 0.7141 All other correlations are below 0.25
Hi Dan, JAQN on the comparison of reduced chi-squares from different beamlines: In order to calculate a chi-square, you need an estimate of the measurement error ("epsilon"). Although you can set that in Ifeffit (and Artemis) to any value you think reasonable, the default behavior of Ifeffit (and thus Artemis) is to make an estimate based on the value of the Fourier transform way up above where there should be any EXAFS signal (as I recall, it uses 15 to 25 angstroms). Although in ideal circumstances this might be reasonable, in practice it can cause difficulties, particularly when comparing data from two different beamlines. As an example, I've worked on beamlines where the lock-in wasn't quite working right, and there was therefore a little oscillation superimposed on the data with a period of a few eV's. This doesn't actually interfere with the EXAFS analysis much (although it could mess up XANES), because it basically just creates spurious signal high in the Fourier transform. But it <italic>does</italic> cause an overestimate of epsilon by Ifeffit, which results in a <italic>smaller</italic> reduced chi-square than appropriate. That's right, taking data on a beamline that is misbehaving can in some cases cause anomalously small chi-square values. (Of course, in the <italic>really bad</italic> scenario that there are spurious oscillations right in the same range as the EXAFS and not above, Ifeffit will give anomalously large chi-square values. But in that case the data's junk anyway.) So what's the bottom line? If you have some clever way of estimating epsilon for each beamline, you can put that into Artemis and compare the reduced chi-squares. But if you don't (and I'd love to hear from people if they do), then I wouldn't use reduced chi-square to compare data from different beamlines; the r-factor will have to do. Actually, in my opinion, reduced chi-square is even a dangerous measure if you are comparing different k-ranges or k-weights, unless you fix expsilon. On the other hand, it's a perfectly good and appropriate measure for comparing different r-ranges, guessed parameters, constraint schemes, etc., etc., because in those cases ifeffit will use the same epsilon for each fit by default. In your particular case, it is a greater concern that some of the values Ifeffit finds for the guessed parameters are significantly different from each other (i.e. they fall outside each other's error bars). --Scott Calvin Sarah Lawrence College At 08:36 AM 10/28/2004 -0700, you wrote:
Also, the data on the same sample from two different beamlines
seems to
be somewhat different, namely the Reduced chi-square is very different
(75.6 in one, 636.4 in the other). In both cases I use the first 3 single
scattering paths. I may have included too much info, but better too much than
too little.
Hi List, Here's my most recent data. I was able to look at the individual paths and changed my R-range to 2-4.5 angstroms on the first set of data, I set the second set of data to the same range, although 4 works better as a maximum for that set. The k-range on the second range had to be truncated though. Six paths are used for each fit, same parameters, except for amp (0.7 for one ~0.8 for the other). The seperate data sets still have a few parameters that are outside of the error bars of the other (ss2,ss3,ss4,alpha, alpha2,alpha5). What can I do to get these beamlines to agree? delr_# define alpha# * reff all other parameters guessed. ss for each path, only one e0 for all Thanks for the help, Dan First Data set -- Full k-range works best, Full range used for fit. Project title : Fitting merge 10_15_04.chi Guess parameters +/- uncertainties (initial guess): e0 = 0.8686930 +/- 1.1038290 (guessed as 0.868694 (1.103841)) ss = 0.0041150 +/- 0.0004990 (guessed as 0.004115 (0.000499)) ss2 = 0.0044360 +/- 0.0004450 (guessed as 0.004436 (0.000445)) ss3 = 0.0243560 +/- 0.0201400 (guessed as 0.024368 (0.020159)) ss4 = -0.0065470 +/- 0.0047820 (guessed as -0.006545 (0.004786)) ss5 = -0.0016100 +/- 0.0050490 (guessed as -0.001611 (0.005047)) ss6 = 0.0059980 +/- 0.0139390 (guessed as 0.005996 (0.013935)) alpha = -0.0011360 +/- 0.0029380 (guessed as -0.001136 (0.002938)) alpha2 = 0.0071480 +/- 0.0019780 (guessed as 0.007148 (0.001978)) alpha3 = -0.0060900 +/- 0.0273580 (guessed as -0.006078 (0.027378)) alpha4 = -0.0198570 +/- 0.0154200 (guessed as -0.019851 (0.015430)) alpha5 = 0.1399050 +/- 0.0148530 (guessed as 0.139906 (0.014848)) alpha6 = 0.0199630 +/- 0.0268690 (guessed as 0.019961 (0.026862)) Def parameters: delr_1 = -0.0051820 delr_2 = 0.0326180 delr_3 = -0.0277930 delr_4 = -0.0906110 delr_5 = 0.6384270 delr_6 = 0.0910950 Set parameters: amp1 = .7 Correlations between variables: e0 and alpha --> 0.8170 e0 and alpha2 --> 0.6726 ss and ss2 --> 0.6591 alpha and alpha2 --> 0.5516 alpha5 and alpha6 --> -0.4856 e0 and alpha3 --> 0.4083 alpha3 and alpha4 --> 0.3588 ss5 and ss6 --> -0.3581 alpha and alpha3 --> 0.3100 alpha2 and alpha3 --> 0.3045 ss2 and alpha6 --> 0.2745 e0 and ss3 --> 0.2649 ss and ss3 --> 0.2627 ss2 and ss3 --> 0.2626 ss6 and alpha5 --> -0.2510 All other correlations are below 0.25 Second Data Set -- Trucated k-range (2-13.5) Project title : Fitting merge 10_15_04.chi Independent points = 25.270507813 Number of variables = 14.000000000 Chi-square = 1202.598221935 Reduced Chi-square = 106.703108852 R-factor = 0.057229121 Measurement uncertainty (k) = 0.000827712 Measurement uncertainty (R) = 0.022111003 Number of data sets = 1.000000000 Guess parameters +/- uncertainties (initial guess): amp = 0.7976070 +/- 0.1361170 (guessed as 0.776348 (0.139898)) ss = 0.0037770 +/- 0.0015530 (guessed as 0.003589 (0.001617)) e0 = 0.5608430 +/- 1.8018380 (guessed as 0.642687 (1.861307)) alpha = -0.0061290 +/- 0.0049080 (guessed as -0.005966 (0.005078)) ss2 = 0.0029050 +/- 0.0009970 (guessed as 0.002686 (0.001078)) ss3 = 0.0075410 +/- 0.0094220 (guessed as 0.005992 (0.008858)) ss4 = -0.0110250 +/- 0.0035850 (guessed as -0.011776 (0.004653)) ss5 = -0.0042000 +/- 0.0078070 (guessed as -0.007610 (0.005250)) alpha2 = 0.0038110 +/- 0.0030450 (guessed as 0.003762 (0.003187)) alpha3 = -0.0037820 +/- 0.0145580 (guessed as 0.001200 (0.015976)) alpha4 = -0.0212550 +/- 0.0107080 (guessed as -0.011852 (0.014417)) alpha5 = 0.1151980 +/- 0.0236640 (guessed as 0.113430 (0.014973)) ss6 = 0.0028560 +/- 0.0166050 (guessed as -0.007250 (0.003624)) alpha6 = 0.0193530 +/- 0.0299640 (guessed as 0.001791 (0.008195)) Def parameters: delr_1 = -0.0279670 delr_2 = 0.0173920 delr_3 = -0.0172570 delr_4 = -0.0969910 delr_5 = 0.5256830 delr_6 = 0.0883130 Correlations between variables: amp and ss --> 0.8382 e0 and alpha --> 0.8048 amp and ss2 --> 0.7964 e0 and alpha2 --> 0.7533 ss3 and alpha5 --> 0.7397 ss5 and alpha4 --> -0.6999 ss and ss2 --> 0.6591 alpha and alpha2 --> 0.6159 alpha5 and ss6 --> 0.6116 ss5 and alpha3 --> -0.5790 ss5 and alpha6 --> -0.5023 ss3 and ss6 --> 0.4804 alpha4 and alpha5 --> -0.4375 e0 and alpha3 --> 0.4083 ss4 and ss5 --> -0.4047 ss3 and alpha4 --> -0.3972 alpha5 and alpha6 --> 0.3827 alpha3 and alpha6 --> 0.3498 ss3 and ss5 --> -0.3278 amp and ss3 --> 0.3209 ss4 and ss6 --> 0.3181 alpha and alpha3 --> 0.3100 alpha2 and alpha3 --> 0.3045 ss4 and alpha3 --> 0.2948 amp and ss5 --> 0.2820 ss2 and alpha6 --> 0.2745 ss and ss3 --> 0.2627 ss2 and ss3 --> 0.2626 amp and alpha6 --> 0.2589 alpha2 and alpha5 --> 0.2581 All other correlations are below 0.25
participants (3)
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dmc＠pdx.edu -
Michael A Groves -
Scott Calvin