Physics307L:People/Mondragon/Notebook/071003

firstweek

Poisson statistics second week
We took data using one sweep through 256 channels with dwell times of 10ms, 20ms, 40ms, 80ms, 100ms, 200ms, 400ms, 800ms, 1s, and 10s.

data files
I will post the ascii files I got here for instructional purposes

10ms Oct 03 2007    03:07:41 am     Elt: 000000 Seconds. Real Time: 000000

ID: No spectrum identifier defined

Memory Size: 16384 Chls Conversion Gain: 1024  Adc Offset: 0000 Chls

Chn   Counts  ROI

0,       0, 000

1,       0, 000

2,       0, 000

3,       0, 000

4,       0, 000

5,       0, 000

6,       0, 000

7,       0, 000

8,       0, 000

9,       0, 000

10,       0, 000

11,       0, 000

12,       0, 000

13,       0, 000

14,       0, 000

15,       0, 000

16,       0, 000

17,       0, 000

18,       0, 000

19,       0, 000

20,       0, 000

21,       0, 000

22,       0, 000

23,       0, 000

24,       0, 000

25,       0, 000

26,       0, 000

27,       0, 000

28,       0, 000

29,       0, 000

30,       0, 000

31,       0, 000

32,       0, 000

33,       0, 000

34,       0, 000

35,       0, 000

36,       0, 000

37,       0, 000

38,       0, 000

39,       0, 000

40,       0, 000

41,       0, 000

42,       0, 000

43,       0, 000

44,       0, 000

45,       0, 000

46,       0, 000

47,       0, 000

48,       0, 000

49,       0, 000

50,       0, 000

51,       0, 000

52,       0, 000

53,       0, 000

54,       0, 000

55,       0, 000

56,       0, 000

57,       0, 000

58,       0, 000

59,       0, 000

60,       0, 000

61,       0, 000

62,       0, 000

63,       0, 000

64,       1, 000

65,       0, 000

66,       0, 000

67,       0, 000

68,       0, 000

69,       0, 000

70,       0, 000

71,       0, 000

72,       0, 000

73,       0, 000

74,       0, 000

75,       0, 000

76,       0, 000

77,       0, 000

78,       0, 000

79,       0, 000

80,       0, 000

81,       0, 000

82,       0, 000

83,       0, 000

84,       0, 000

85,       0, 000

86,       0, 000

87,       0, 000

88,       0, 000

89,       0, 000

90,       0, 000

91,       0, 000

92,       0, 000

93,       0, 000

94,       0, 000

95,       0, 000

96,       0, 000

97,       0, 000

98,       0, 000

99,       0, 000

100,       0, 000

101,       0, 000

102,       0, 000

103,       0, 000

104,       0, 000

105,       0, 000

106,       1, 000

107,       0, 000

108,       0, 000

109,       0, 000

110,       0, 000

111,       0, 000

112,       0, 000

113,       0, 000

114,       0, 000

115,       0, 000

116,       0, 000

117,       0, 000

118,       0, 000

119,       0, 000

120,       0, 000

121,       0, 000

122,       0, 000

123,       0, 000

124,       0, 000

125,       0, 000

126,       0, 000

127,       0, 000

128,       0, 000

129,       0, 000

130,       0, 000

131,       0, 000

132,       0, 000

133,       0, 000

134,       0, 000

135,       0, 000

136,       0, 000

137,       0, 000

138,       0, 000

139,       0, 000

140,       0, 000

141,       0, 000

142,       0, 000

143,       0, 000

144,       0, 000

145,       0, 000

146,       1, 000

147,       0, 000

148,       0, 000

149,       0, 000

150,       0, 000

151,       0, 000

152,       0, 000

153,       0, 000

154,       0, 000

155,       0, 000

156,       0, 000

157,       0, 000

158,       0, 000

159,       0, 000

160,       0, 000

161,       0, 000

162,       0, 000

163,       0, 000

164,       0, 000

165,       0, 000

166,       0, 000

167,       1, 000

168,       0, 000

169,       0, 000

170,       0, 000

171,       0, 000

172,       0, 000

173,       0, 000

174,       0, 000

175,       0, 000

176,       0, 000

177,       0, 000

178,       0, 000

179,       0, 000

180,       0, 000

181,       0, 000

182,       0, 000

183,       0, 000

184,       0, 000

185,       0, 000

186,       0, 000

187,       0, 000

188,       0, 000

189,       0, 000

190,       0, 000

191,       0, 000

192,       0, 000

193,       0, 000

194,       0, 000

195,       0, 000

196,       0, 000

197,       0, 000

198,       0, 000

199,       0, 000

200,       0, 000

201,       0, 000

202,       0, 000

203,       0, 000

204,       0, 000

205,       0, 000

206,       0, 000

207,       0, 000

208,       0, 000

209,       0, 000

210,       0, 000

211,       0, 000

212,       0, 000

213,       0, 000

214,       2, 000

215,       0, 000

216,       0, 000

217,       0, 000

218,       1, 000

219,       0, 000

220,       0, 000

221,       0, 000

222,       0, 000

223,       0, 000

224,       2, 000

225,       0, 000

226,       0, 000

227,       0, 000

228,       0, 000

229,       0, 000

230,       0, 000

231,       0, 000

232,       0, 000

233,       0, 000

234,       0, 000

235,       0, 000

236,       0, 000

237,       0, 000

238,       0, 000

239,       0, 000

240,       0, 000

241,       0, 000

242,       0, 000

243,       0, 000

244,       0, 000

245,       0, 000

246,       0, 000

247,       0, 000

248,       0, 000

249,       0, 000

250,       0, 000

251,       2, 000

252,       0, 000

253,       0, 000

254,       0, 000

255,       0, 000

other files
 * [[Image:Dwell20ms.asc ]]
 * [[Image:Dwell40ms.asc]]
 * [[Image:Dwell80ms.asc]]
 * [[Image:Dwell100ms.asc]]
 * [[Image:Dwell200ms.asc]]
 * [[Image:Dwell400ms.asc]]
 * [[Image:Dwell800ms.asc]]
 * [[Image:Dwell1s.asc]]
 * [[Image:Dwell10s.asc]]

Octave script for analysis & plotting
chop off the first ten lines of the data files before use On a Unix box you can use tail -n 256 filename >newfilename

rename variable and filenames as necessary. Probably only needs minor tweeks so that it can run in matlab. #octave
 * 1) a sort of diary of commands to automate the data processing day to day
 * 2) some of octave's functions do not work in matlab,and vice
 * 3) versa. Some functions may work, but not in the same way
 * 4) Most things work, though.


 * 1) Tomas Mondragon

load LAB3_D10.ASC;    #The load function is intended load LAB3_D20.ASC;    #to read files saved by octave. load LAB3_D40.ASC;    #Load can also read .mat files, load LAB3_D80.ASC;    #but only versions saved by matlab load LAB3_D100.ASC;   #version 4. It can also read text files. load LAB3_D200.ASC;   #if no variable name info is in the file load LAB3_D400.ASC;   #it names the variable after the file name load LAB3_D800.ASC; load LAB3_D1S.ASC; load LAB3_D10S.ASC;

dwell10ms=LAB3_D10(:,2);    #load put what was on LAB3_D10.ASC into the dwell20ms=LAB3_D20(:,2);    #variable LAB3_D10. I only care about the second dwell40ms=LAB3_D40(:,2);    #column of the data, so here I extract it to an dwell80ms=LAB3_D80(:,2);     #appropriately named variable. dwell100ms=LAB3_D100(:,2); dwell200ms=LAB3_D200(:,2);  #The first column of LAB3_D10 is the channel number dwell400ms=LAB3_D400(:,2);  #but a more appropriate name would be the bin index. dwell800ms=LAB3_D800(:,2);  #The second column is a count of how many events fell dwell1s=LAB3_D1S(:,2);      #into the bin. The third column isn't important for this dwell10s=LAB3_D10S(:,2);    #experiment, but knowing what it is will allow one to take #advantage PCA3's region of interest feature. It just marks #where one has marked ROI's. for example, 4 indicates the bin or channel #lies within ROI 4


 * 1) idea:use max(dwell10s) as max bin for all so grapfh are same x scale


 * [y,x]=hist(data,bincenters) is a a function that sets up bins whose values are centered
 * 1) at the values given by the vector bincenter and counts the number of times the values in
 * 2) data fall into each of the bins. y contains the frequncy counts and x contains the
 * 3) corresponding bin index. bar(x,y) will plot the histogram of data. In general, x=bincenters,
 * 4) so one can shorten [y,x]=hist(data,bincenters) to y=hist(data,bincenters) and use
 * 5) bar(bincenters,y) to plot the same thing.

bincenters=0:max(dwell10s);

freq10ms=hist(dwell10ms,bincenters); freq20ms=hist(dwell20ms,bincenters); freq40ms=hist(dwell40ms,bincenters); freq80ms=hist(dwell80ms,bincenters); freq100ms=hist(dwell100ms,bincenters); freq200ms=hist(dwell200ms,bincenters); freq400ms=hist(dwell400ms,bincenters); freq800ms=hist(dwell800ms,bincenters); freq1s=hist(dwell1s,bincenters); freq10s=hist(dwell10s,bincenters);

bar(bincenters,freq10ms) title("frequency counts for dwell time=10ms") xlabel("number of events occuring during dwell time") ylabel("frequency count") pause replot bar(bincenters,freq20ms) title("frequency counts for dwell time=20ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq40ms) title("frequency counts for dwell time=40ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq80ms) title("frequency counts for dwell time=80ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq100ms) title("frequency counts for dwell time=100ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq200ms) title("frequency counts for dwell time=200ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq400ms) title("frequency counts for dwell time=400ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq800ms) title("frequency counts for dwell time=800ms") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq1s) title("frequency counts for dwell time=1s") %xlabel("number of events occuring during dwell time") %ylabel("frequency count") pause replot bar(bincenters,freq10s) title("frequency counts for dwell time=10s") %xlabel("number of events occuring during dwell time") %ylabel("frequency count")
 * 1) plots. press any key to move to next plot

Compilation of plots
The plot that the script above produced with the data I obtained are shown in sequence below. As the dwell time increases, the most often occurring event count increases and the frequency vs. event count plots drift from something resembling a poisson distibution of a rarely occuring event to a the poisson distribution of a more common event, which resembles a gaussian. I should point out that something interesting happened while we we counting events with a dwell of 200 ms. Our equipment recorded one instance where 27 events happened in that small dwell time. The mode of the data for dwell time=200ms was 0 events, and were were rarely getting counts more than or equal to 5. I've kept this data point for fitting, since it is a very minor outlier, adnd won't trhough my data of by much.

In Dr. Gold's lab manual, the poisson distribution is a model for the results of experiments that count random events that occur at a definite average rate and the frequency that one should expect to get a certain count number. If the expected average count is $$\lambda$$, the probability that one will obtain the result of $$k$$ after performing the counting experiment is

$$f(k;\lambda)=\frac{\lambda^k e^{-\lambda}}{k!}$$

So, to fit my data, I will have to normalize my data and find a suitable $$\lambda$$ for each data set.

First off, normalize. If the frequency count of an event count $$k$$ is $$x_k$$, find some normalizing constant $$N$$ so that $$\sum_{k=\operatorname{min}\ k}^{\operatorname{max}\ k} \frac{x_k}{N}=1$$

Then, find $$\lambda$$ such that$$\sum_{k=\operatorname{min}\ k}^{\operatorname{max}\ k}\left(f(k,\lambda)-\frac{x_k}{N} \right)^2$$ is a minimum

Duurrrrr... $$N=256$$ because 256 tests were performed each time.

Through an iterative process, I determine $$\lambda$$ to be 0.0261205 for dwell=10ms. Ack, I won't go any further, I'll just stop at 6 significant digits for the others too.
 * 10ms,0.0261205
 * 20ms,0.0736898
 * 40ms,0.185892
 * 80ms,0.457154
 * 100ms,0.468385
 * 200ms,0.937453
 * 400ms,2.58578
 * 800ms,5.74562
 * 1s,7.27233
 * 10s,73.9022



how good are these fits, since I decided to stop at 6 sig figs (quite arbitrarily). I suppose I shall quantify this by taking the average of how far off the actual data is from the fit.

I think the formula is $$Error=\frac{\sqrt{\sum_{k=\operatorname{min}\ k}^{\operatorname{max}\ k}\left(f(k,\lambda)-\frac{x_k}{N} \right)^2}}{\sqrt{N}\sqrt{N-1}}$$
 * 10ms, 0.0261205, 5.45905*10^-6
 * 20ms, 0.0736898, 1.19310*10^-4
 * 40ms, 0.185892, 3.28259*10^-4
 * 80ms, 0.457154, 5.95464*10^-4
 * 100ms, 0.468385, 3.15888*10^-4
 * 200ms, 0.937453, 5.23153*10^-4
 * 400ms, 2.58578, 5.22450*10^-4
 * 800ms, 5.74562, 2.97039*10^-4
 * 1s, 7.27233, 2.59775*10^-4
 * 10s, 73.9022, 2.96150*10^-4

The poisson distribution fits count data of random events that happen at a definite average rate. Therefore $$\frac{\lambda}{dwell time}=some constant$$ A plot of $$\lambda$$ vs. dwell time should fit on a line. to do this, I use Dr. Gold's linefit matlab function, modified for use with Octave. (www-hep.phys.unm.edu is down at the moment, maybe provide link when it comes back up or upload modded program here?) slope=7.39233+/-0.00003 intercept= -0.048099+/-0.000005 cov(m,b)=0.000000 correlation=0.999983 point error estimate=0.231612 chisq/ndf=265341 So, with our equipment set up the way it was (sorry, no data on this, oops!) we were observing events that occured at an average rate of 7.39 events per second.