H35Cl, j(0+) intensity ratio analysis and comparison of experimental data agust,www,....Jan11/PPT-210111ak.ppt agust,heima,...Jan11/Evaluation of coupling.

Slides:



Advertisements
Similar presentations
Modeling of Data. Basic Bayes theorem Bayes theorem relates the conditional probabilities of two events A, and B: A might be a hypothesis and B might.
Advertisements

HBr, E(1), one-color, VMI KER spectra VMI, E(1) vs J´(=J´´)………………………………………2 Branching ratios……………………………………………………………..3-4 Prediction calculations……………………………………………………5.
HCl,  =0, H 3 7Cl and H35Cl analysis. agust,www,.....Sept10/PPT aak.ppt agust,heima,...Sept10/XLS ak.xls agust,heima,...Sept10/Look for J ak.pxp.
Complex Power – Background Concepts
STA305 week 31 Assessing Model Adequacy A number of assumptions were made about the model, and these need to be verified in order to use the model for.
Review of the Basic Logic of NHST Significance tests are used to accept or reject the null hypothesis. This is done by studying the sampling distribution.
MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #19 3/8/02 Taguchi’s Orthogonal Arrays.
R Measurement at charm resonant region Haiming HU BES Collaboration Charm 2007 Cornell University Ithaca, NY. US.
HCl, V, v´=14, vs J´, power and operator(!), Agust,heima,...REMPI/HCl/HCl,agust07-/HClmass V(14)state Q kmak.pxp Agust,www,...rempi/hcl/HCl,agust07-/HClmass.
1) HBr 1D (2+n)REMPI spectra simulations 2) Energy level shifts and intensity ratios for the F(v´=1) state agust,www,....Jan11/PPT ak.ppt agust,heima,....Jan11/XLS ak.xls.
Chapter 4 Multiple Regression.
Ch 5.1: Review of Power Series
MAE 552 Heuristic Optimization
H37Cl, j(0+) 1)Exp. data for ion intensity ratios agust,www,...Jan11/PPT ak.ppt agust,heima,...Jan11/Evaluation of coupling strength j state-2i0111kmak.xls.
HCl agust,heima,...Sept10/aHCl(3+1)j3S(0)Calc ak.pxp (JMS paper) agust,www,....Sept10/PPT ak.ppt agust,heima,...Sept10/HCl(3+1)j3Sigma(0) Calc ak.pxp.
Excellence Justify the choice of your model by commenting on at least 3 points. Your comments could include the following: a)Relate the solution to the.
Development of Empirical Models From Process Data
HCl, ; exp vs calc potentials: Agust,www,..../rempi/hcl/HCl,agust07-/HCl-exp vs calc pot akab.ppt Agust,heima,.../REMPI/HCl/HCl,agust07-/HCl-Potentials avhwak.pxp.
HCl, potentials: Agust,heima,...REMPI/HCl/HCl,agust07-/HClpotXF ej ak.xls Agust,heima,...REMPI/HCl/HCl,agust07-/HClpotXF ej ak.pxp.
HCl, j(  =1) agust,www,...Sept10/PPT ak.ppt agust,heima,....Sept10/XLS ak.xls agust,heima,....Sept10/PXP ak.pxp agust,heima,...Sept10/Look.
HCl, V
HCl, f 3  2
GG313 Lecture 3 8/30/05 Identifying trends, error analysis Significant digits.
Experimental Evaluation
Chi Square Distribution (c2) and Least Squares Fitting
Correlation and Regression
Psy B07 Chapter 1Slide 1 ANALYSIS OF VARIANCE. Psy B07 Chapter 1Slide 2 t-test refresher  In chapter 7 we talked about analyses that could be conducted.
Physics 114: Lecture 15 Probability Tests & Linear Fitting Dale E. Gary NJIT Physics Department.
Simple Linear Regression Models
CORRELATION & REGRESSION
1 Least squares procedure Inference for least squares lines Simple Linear Regression.
1 As we have seen in section 4 conditional probability density functions are useful to update the information about an event based on the knowledge about.
Basic linear regression and multiple regression Psych Fraley.
The Supply Curve and the Behavior of Firms
Physics 114: Exam 2 Review Lectures 11-16
Correlation and Linear Regression. Evaluating Relations Between Interval Level Variables Up to now you have learned to evaluate differences between the.
LECTURER PROF.Dr. DEMIR BAYKA AUTOMOTIVE ENGINEERING LABORATORY I.
HCl, negative ion detections 1hv ion-pair spectra (slides 3-4) Loock´s prediction about H+ + Cl- formation channels(slides 5-6) Energetics vs Dye for V(v´= )
HSC Space: Section 1. Weight Whenever a mass is located within a gravitational field it experiences a force. It is that force, due to gravity, that.
HI Relevant to the HI- Perturb.II paper:pages: H(2) ion intensities (system (b))………………………2-9
Physics 430: Lecture 25 Coupled Oscillations
Lecture 22 Numerical Analysis. Chapter 5 Interpolation.
HCl, E(v´=2) agust,www,…..hcl/June11/PPT ak.ppt agust,heima,…HCl/June11/HCl E2 spectra jl.pxp agust,heima,…HCl/June11/HCl E2 spectra jlaka.pxp.
Correlation & Regression Analysis
Trees Example More than one variable. The residual plot suggests that the linear model is satisfactory. The R squared value seems quite low though,
Slide Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
1 Introduction to Statistics − Day 4 Glen Cowan Lecture 1 Probability Random variables, probability densities, etc. Lecture 2 Brief catalogue of probability.
Example x y We wish to check for a non zero correlation.
ES 07 These slides can be found at optimized for Windows)
Review of Statistical Inference Prepared by Vera Tabakova, East Carolina University.
CHAPTER- 3.2 ERROR ANALYSIS. 3.3 SPECIFIC ERROR FORMULAS  The expressions of Equations (3.13) and (3.14) were derived for the general relationship of.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
CHAPTER 4 ESTIMATES OF MEAN AND ERRORS. 4.1 METHOD OF LEAST SQUARES I n Chapter 2 we defined the mean  of the parent distribution and noted that the.
Richard Kass/F02P416 Lecture 6 1 Lecture 6 Chi Square Distribution (  2 ) and Least Squares Fitting Chi Square Distribution (  2 ) (See Taylor Ch 8,
CORRELATION-REGULATION ANALYSIS Томский политехнический университет.
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
Directions 1.With a method similar to that of the previous lab, you will be using a Rutherford analysis to uncover a value hidden in your data. 2.Begin.
CSE 330: Numerical Methods. What is regression analysis? Regression analysis gives information on the relationship between a response (dependent) variable.
The simple linear regression model and parameter estimation
Physics 114: Lecture 13 Probability Tests & Linear Fitting
H(0), one-color, VMI and slicing images
5.2 Least-Squares Fit to a Straight Line
VMI-fitting results for V(m+i), i=4-10
HBr Mass resolved REMPI and Imaging REMPI.
Product moment correlation
HBr The cm-1 system (slides 2-16)
11. Conditional Density Functions and Conditional Expected Values
11. Conditional Density Functions and Conditional Expected Values
6.1.1 Deriving OLS OLS is obtained by minimizing the sum of the square errors. This is done using the partial derivative 6.
Presentation transcript:

H35Cl, j(0+) intensity ratio analysis and comparison of experimental data agust,www,....Jan11/PPT ak.ppt agust,heima,...Jan11/Evaluation of coupling strength j state-2i0111kmak.xls agust,heima,....Jan11/PXP ak.pxp The following holds for W12 = 25 cm-1:

      error Agust,heima,....Jan11/PXP ak.pxp; Lay: 0, Gr:0 Least square minimization of I(35Cl+)/I(H35Cl+) vs J´ (for J´=0-5) with respect to  and  least sq. error (J=0-5)

least sq. error (J=0-5)  Agust,heima,....Jan11/PXP ak.pxp; Lay: 0, Gr:0agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls expCalc.(v´=19-22) v´=19,20,21,22 and sum J´  v´=18,19,20,21,22,23 and sum

 Agust,heima,....Jan11/PXP ak.pxp; Lay: 0, Gr:0 &agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls least sq. error (J=0-5) expCalc.(v´=19-22) v´=19,20,21,22 and sum J´ 

 Agust,heima,....Jan11/PXP ak.pxp; Lay: 0, Gr:0agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls least sq. error (J=0-5) expCalc.(v´=19-22) v´=19,20,21,22 and sum J´ 

1) NB!: contributions from v´ 21 CLEARLY CAN NOT BE IGNORED!!! This analysis assumes W 12 to be constant and independent with v´(ip) and to be the same value as that derived from shift analysis for v´(ip)=21.  and  are also assumed to be constant and independent with v´(ip) : Thus least square analyses on  and  (for W 12 = 25 cm -1 ) resulted in W 12 = 25 cm -1  = 2.5  = for j(0 + ) H35Cl The significantly larger  value, compared to that observed for other triplet states (  = – 0.004) might be because of a large contribution to the dissocaiation Channels from photodissociation follwed by Cl ionization, i.e. 2hv + HCl ->-> HCl*(j(0 + ),v´=0, J´) HCl*(j(0 + ),v´=0, J´) + hv -> HCl** -> H + Cl* Cl* + hv -> Cl + + e - Analogous analysis now need to be done for H 37 Cl!!!!!

The J’ = 6 peak is problematic for H35Cl since the mass peaks for J’ = 6 and 8 overlap. Hence the experimentally evaluated ion ratio for J’ = 6 will be an underestimated value. Therefore it is acceptable that the calculated ratio is higher. This should not be the problem for H37Cl. Lets try to include v´=18 and 23 interactions:

expCalc.(v´=18-23) v´=18,19,20,21,22,23 and sum W12 = 25 cm-1;  = 1.7;  = J´ agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls

It is interesting to see that the contribution falls down very slowly as  E(J´) increases / v´ “moves further away” from the Rydberg state. But what happens if W 12 changes with v´, say W 12 increases? I tried 1)W 12 = 22,23,24,25,26,27 vs v´=18,19,20,21,22,23 & 2)W 12 = 19,21,23,25,27,29 vs v´=18,19,20,21,22,23 & 3)W 12 = 28,27,26,25,24,23 vs v´=18,19,20,21,22,23 & 4)W 12 = 31,29,27,25,23,21 vs v´=18,19,20,21,22,23  No big change  Looking at calculations such as in the previous figure shows that contribution from v´ 21 is close to a constant (  ). Therefore the relevant expression for I(Cl+)/I(HCl+) is Is it perhaps possible to obtain good fit for the parameters  and  only assuming  to be zero?

No that does not seem to be the case. In other words gamma is an important parameter. Looking at: It is clear that c 2 2 is very small and the ratio for v´ 21 is simply: NB! It is interesting to see that similar  values are obtained independet of the number of v´(V) contribution:  = for v´=20-21 (KM)  = for v´=  = for v´= THIS IS IMPORTANT!

Effect of  is clearly seen below:

W 12 = 25,  = 1.7  = 0  =  = v´=18,19,20,21,22,23 and sum J´ agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls How can I make the colors in the excel graph to stay unchanged?

W 12 =25, f = 1 Minimize with respect to  and  =>  = 2.2,  = , least sq. error(J´=0-5) = agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls J´ NB!: As a rough estimate I increased The experimental Ratio value to 0.5 We realy need to analogous test on H37Cl where the peak overlap problem does not exist.

Lets´ compare the calc. sum values for different optimizaed  values: Is the graph shape perhaps comparable?: Lets look at plots normalized to the largest peak (i.e. J´=6) See note from : ** least sq.(0-6)(rel) f (in f*  *  ) f (in f*  *  ) All same! All same! agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls Fit of I rel (exp) = (I(Cl + )/I(HCl + )(J´;exp))/(I(Cl + )/I(HCl + )(J´ max ;exp)) vs J´ by I rel (calc)= (I(Cl + )/I(HCl + )(J´;calc))/(I(Cl + )/I(HCl + )(J´ max ;calc)) vs J´ NB!: J´ max = 6 All give equally good fit (see figure next slide) ERGO: 1) Use f = 0 (i.e. Neglect f*  *  ) 2) Use only v´= 20 & 21 and perform fit on I rel (exp) vs J´ by varying  only!!! Thus the  parameter drops out

agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls Fit of I rel (exp) = (I(Cl + )/I(HCl + )(J´;exp))/(I(Cl + )/I(HCl + )(J´ max ;exp)) vs J´ by I rel (calc)= (I(Cl + )/I(HCl + )(J´;calc))/(I(Cl + )/I(HCl + )(J´ max ;calc)) vs J´ : Relative Intensity ratios J´

Comparison of KM´s and JL´s ion ratios for j(0+), H35Cl: JCl/HCl(KM)(JL; ))JL( ) suitable for low J´ suitable for high J´ agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls

KM JL JL J´ agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls

KM JL JL J´ agust,heima,...Jan11/Evaluation of coupling strength j state kmak.xls