Документ взят из кэша поисковой машины. Адрес оригинального документа : http://prac-gw.sinp.msu.ru/images/nucleus/descriptions%20nucleus/Obrabotka.pdf Дата изменения: Thu Oct 24 14:38:47 2013 Дата индексирования: Thu Feb 27 20:53:38 2014 Кодировка: Поисковые слова: m 3

"Origin"
. . . .- . ,
1952-2011
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, , Origin.


1 1 2 6 " - " 2 3 2 ", -, - " 3 3.1 . . . . . . . . . . . . . . . . . . . 3 3.2 - . . . . . . . 8 4 7 15 " " 12

1



. , , . - -, . , . , , . , , Origin, . ,

1


. , . , , , , . . Origin. · , ; · .

2 6 " - "
1. . A(X ) Data1 , B (Y ) -. 2. B (Y ), B (Y ) . C olumn S et C olumn V alues , . . col(B ). : col(B ) - OK . 3. C olumn Add N ew C olumns C (Y ). ln(col(B )) C olumn S et C olumn V alues. , sq rt() Add F unction, col(B ) Add C olumn. , 1/, 1/sq rt(col(B )), OK . 4. C (Y ) C olumn S et as Y E rror Y E rror. 5. B (Y ), B (Y ) . C olumn S et C olumn V alues. S et C olumn V alues . , , ln() Add F unction. col(B ) Add C olumn. B (Y ) ln(col(B )). OK B (Y ) Data1 .

2


- . " 2. ". Data1 P lot S catter . Analy sis F it Linear. Results Log ( V iew Results Log ) , B . B (. 6), - -.

3 2 ", -, "
3.1
1. . , , (Worksheet) : A(X ) B (Y ), X Y . C olumn Add N ew C olumns. , , A(X ), B (Y ), C (Y ), D(Y ), E (Y ), F (Y ), G(Y ). 2. . (a) A(X ) , . (b) B (Y ) ( ). C olumn S et as X E rror , X E rror, B (xE r±). (c) C (Y ) , . (d) C olumn S et as X D(Y ) F (Y ) X . D(X 2), E (Y 2), F (X 3), G(Y 3). (e) C olumn S et C olumn V alues D(X 2) E (X 2) col(A) + col(B ) col(A) - col(B ) , E (Y 2) G(Y 3) col(C ). C (Y ). 3. . . , ( , ), . . Shift

3


C

6,0

5,5

Y Axis Title

5,0

4,5

4,0

350

400

450

500

550

600

650

X Axis Title

. 1: .
, *. . Control. 4. . A(X ), B (xE r ±), C (Y ). P lot P lotS tyle. (. 1) P lot Scatter, , . 5. . . T ools Lay er . Lay er . 2. , . . 2 Lay er P roperties. Lay er2 Link Axes S cales. Link to Lay er1. X Axis Link Y Axis Link Straig ht (1 to 1) . . 2, Lay er C ontents. Layer2 Available Data data1_e Lay er C ontents. data1_g OK . ,

4
* , " " , , , . .

: http://nuclphys.sinp.msu.ru/experiment/statistic/peack.htm


( ) (. 2). Line T ool ToolBar. 6. Plot Detail, F ormat P lot, (Apply ). Plot Detail , . 7. F ormat Axes X Axis , (Axis) (P roperties). Axis S election. , . , , (Axis : S cale : F rom...T o). ! , . 8. . ( ) . Draw data ToolBar, , . , . E nter, . . Plot Detail DrawN : A(X ), B (Y ), Drop Lines H orisontal V ertical, S ty le Dash Apply . S y mbol S iz e 0. 9. S creen Reader ToolBar , - (. 3). , . . 3 . 10. , , .. ToolBar. ± \(177). (F ormat Axis T itles X Axis T itle). (F ormat Axis T ics Label : S cale : M aj or(M inor) T ics) (. 3).

5


6,5

6,0

5,5

Y Axis Title

5,0

4,5

4,0

3,5

3,0 300 350 400 450 500 550 600 650 700

X Axis Title

. 2: , , , . , , , , , . , , .

6


6,5

6,0

5,5

5,0

E

1

1

,
4,5 4,0

E

2

3,5

2

3,0 300 350 400 450 500 550 600 650 700

. 3: - .

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3.2 -
1. , : A(X ) ; B (Y ) -; C (y E r±) . 2. (P lot S catter) (. 4).

B
1,2

1,0

0,8

Y Axis Title

0,6

0,4

0,2

0,0

0

2

4

6

8

10

12

X Axis Title

. 4: - I
x. .

3. (Analy sis C alculus Dif f erentiate) (. 5). 4. (V iew P roj ect E xplorer). , , . . H ide W indow. S how W indow Delete W indow. Data1. 5. DerivPlot1 , , Y . DerivPlot1. Derivative1. Data1B (Y ) [Deriv ativ e of Data1_B ] S et C olumnV alues. Add C olumn

8


Derivative of Data _B 1

0,0

-0,1

Y Axis Title

-0,2

-0,3

-0,4

-0,5

0

2

4

6

8

10

12

X Axis Title

. 5: -.
C ol(Data1B ) Add C olumn. C ol(Data1B ), OK. Data1B (Y ) [Deriv ativ e of Data1_B ] . Derivative1 Derivative of Data1_B (P lot S catter). (. 6). 6. , (Lay ers) . Derivative1 Graph2. Graph1 (. 4). 1, . (T ools Lay er). Layer . Y 2, . 7. , . "2" Layer2. Av ailable Data deriv ativ e1data1b , , Lay er C ontents OK. (. 7) . 8. (. 8). . , "1 (Analy sis F it S ig modial). "2" (Analy sis F it Gaussian).

9


Derivative of Data _B 1

0,5

0,4

Y Axis Title

0,3

0,2

0,1

0,0

0

2

4

6

8

10

12

X Axis Title

. 6: -
dI /dx.

(Analy sis S moothing . . . ). . · . · (Analy sis N on - linear C urv e F it) Non-linear Curve Fitting: Fitting Session. · . F unction S elect Non-linear Curve Fitting: Select Function C ateg ory Orig in B asic F unctions F unction Gauss. Area v ersion of Gaussian F unction, E q uation S ample C urv e, , . · . Action Dataset. Non-linear Curve Fitting: Select Dataset V ariables y , Av ailable Datasets deriv ativ e_1data1b, Assig n. V ariables , . · . Action S imulate. #P oints 1000, . · . Action F it. Non-linear Curve Fitting: Fitting Session V alue , , , V ary . · . 1 I ter. -

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B
1,2 0,5 1,0

0,4 0,8

Y Axis Title

0,3 0,6

0,2 0,4

0,2

0,1

0,0

0,0

0

2

4

6

8

10

12

X Axis Title

. 7: , .
, V alue Graph1. , Done, (. 8). 9. P lot Detail, Plot Detail. . ( , Delete, Apply ). , ( ) , Apply , , Plot Detail. 10. , Y (F ormat Axes Y Axis : S cale : F rom...T o). , R (, ) R ( ). , , (. 9).

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Data: Derivative1_Data1B Model: Gauss

Equation: y=y0 + (A/(w*sqrt(PI/2)))*

1,2

0,5

W eighting: y No weighting

1,0 0,4

Chi^2/DoF R^2

= 0.00006

= 0.99895

y0

-0.00362 8.24809 1.37893

±0.00365 ±0.00905 ±0.02161

0,8

xc w

Y Axis Title

0,3 0,6 0,2 0,4

A

0.9336 ±0.01653

0,1 0,2

0,0

0,0

0

2

4

6

8

10

12

X Axis Title

. 8: .

4 7 1 5 " "
3.1 . 3. -235 ( Cf-252), . 1. . . (F ile Import Single ASCII) ( ). A(X) , B(Y ) . 2. . (C olumn Add N ew C olumns) C (Y ). B (Y ) C (Y ). C ( Y ) N, N. C(Y ) (Column Set Column V alues) Set Column Values. Add Column Col(C) Add Column. Col(C), sqrt(Col(C)) OK. C(Y ) . 3. A(X ) (C olumn S et as X ), X . B (Y ), Y , C (Y ), Y E rror. 4. (P lot S catter) (. 10).

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./ c .
1,2

.

0,6 1,0

, I,

0,5

0,4

0,6 0,3

0,2

0,2

0,1

0,0

R

0,0

0

2

4

6

8R

10

12

, x, R , R .

.

. 9: , ,
.

13

d I/ d x ,

0,4

./ ( ·

-

)

0,8


B

20000

15000

Y Axis Title

10000

5000

0 0 200 400 600 800 1000

X Axis Title

. 10: -235
-, -234. .

14


B

1400 1200 1000
Y Axis Title

800 600 400 200 0 200 400
X Axis Title

600

800

. 11: -235 . . 5. - , . F ormat Axis X Axis : S cale : F rom...T o : Apply X , -. , S election V ertical Y (. 11). 6. 11 , . P lot Detail, Plot Detail. Lay er 1. , ( ) , Apply , , Plot Detail. . C ap W idth . (. 12). 7. , . Analy sis F it M ulti-peak s Gaussian. Numb er of Peaks 2, Initial half width estimate . -

15


B

1400 1200 1000
Y Axis Title

800 600 400 200 0 200 400
X Axis Title

600

800

. 12: -235 . .

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. (. 13) , . 8. . DataS elector T ools. . , S pace. Analy sis F it Gaussian , . Rez ults Log (V iew Rez ults Log ), . 9. (. 14) T oolB ar (. 3.1 . 3).

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Data: u235Fission_B Model: Gauss Equation: y=y W eighting: y Instrumental = 45 0.49671

0

+ (A/(w*sqrt(PI/2)))*exp(-2*((x-xc)/w)^2)

Chi^2/DoF R^2 =

3.27

683

xc

1400 1200 1000
Axis Title

0 1 w1 A1
y xc2

w2 A2

0 ±0 316.53881 195.58755 289247.88297 597.19782 118.53019 144363.28608

±0.2521 ±0.31985 ±583.70808 ±0.2281 ±0.28239 ±440.60739

800 600 400 200 0 200 400
X Axis Title

Y

600

800

. 13: -235 .
.

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Data: u235Fission_B Model: Gauss Equation: y=y0 + (A/(w*sqrt(PI/2)))*exp(-2*((x-xc)/w)^2) W eighting: y Instrumental

Chi^2/DoF R^2

= 453.27683

= 0.49671

-234

y0 xc1 w1 A1 xc2 w2

0

±0 ±0.2521 ±0.31985 ±583.70796 ±0.2281 ±0.28239 ±440.60719

316.53878 195.58725 289247.78913 597.1977 118.53005 144363.37076

1400 1200 1000 800
N

A2

600 400 200 0 100 200 300
n

1

N

2

1

n

2

400

500
,N

600

700

800

. 14: 235 . N1,2 = w1,2 , ; n1,2 , .

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