LMS Stability, Data Correction and the Radiation Accident within the PrimEx Experiment by

LMS Stability, Data Correction and the Radiation Accidentwithin the PrimEx Experiment

by

LaRay J. Benton

M.S. Nuclear PhysicsMay 2006 Graduate

North Carolina A&T State UniversityThomas Jefferson National Laboratory

PrimEx Collaboration

Advised by Dr. Samuel Danagoulian

One issue that has had affect data analysis and calibration is the filter wheel position during data collection phase 2 of the experiment. During the experimental run, data collection was done in three phases; Phase 1: Pedestal Analysis, Phase 2: LMS Data, Phase 3: Production Runs. Where as the phase of the experiment periodically changed throughout the experimental run. Hence, the current phase of the experiment depended on the type of data that was being collected at the time. Thus the filter would rotate, depending on the phase of the experiment, and it would either allow a signal to enter the LMS trigger, or not. During Phase 2 of the experiment, light was allowed in and LMS Data was collected. However, there are different settings of filter wheel position, and depending on the position of the filter wheel, we would record LMS data that was not collimated and corresponded to the filter wheel position in which it was recorded. Therefore you have some runs that had LMS data, and some that didn't.

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ID W1005 Values for All Runs

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This absence of LMS data is displayed on our graphs, bottom right, and is seen as wholes in the graphs. The larger the whole, the more consecutive runs that were taken with the filter wheel position being closed.

W1005 DataRun # ADC Counts

4100 9164101 8994102 1858 Bad Run4103 1858 Bad Run4104 1858 Bad Run4105 1858 Bad Run4106 1858 Bad Run4107 1858 Bad Run4108 1858 Bad Run4109 1858 Bad Run4110 1858 Bad Run4111 9264112 9154113 9004114 9264115 9164116 8994117 9264118 9164119 9004120 9274121 1858 Bad Run4122 1858 Bad Run4123 1858 Bad Run

Missing LMS Data

There is a total of 332 runs without LMS data, equating to about 23.85% of the total run (1350 Runs), and I labeled these as bad runs in my analysis. This missing data is also confirmed and corresponds to wholes existent in Dr. Danagoulian's PMT ratio plots. This behavior is also seen in the actual data, as seen to the left, as ADC values that often deviate drastically from the mean, with a constant value that is the same for all runs where there is no LMS data. Hence, these bad runs are not initially included in my averaging technique to correct LMS data, but values for these bad runs will be filled in later in my analysis.


4133 912 Med

4134 896 Low

4135 923 High

4136 9134137 8984138 9254139 9144140 8984141 9254142 9134143 8974144 9244145 9134146 8984147 9254148 9144149 9254150 9254151 9134152 8984153 9234154 9134155 8984156 924

LMS Data

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ID W1005 Values for All Runs

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As you can see to the left, the actual LMS data for crystal ID W1005 displays a behavior that is directly proportional to the filter wheel position. Where as for every sequence of runs, they alternate between a High, Med, or Low ADC count readout. Hence, giving validity to the fact that there are 3 filter wheel positions in which light or a signal can enter into the LMS trigger. Thus, when we went to analyze the LMS data, particularly the stability of the data over all runs, we got graphs that looked like the one shown above. This graph displays 3 separate graphs, instead of one single graph. Hence, supporting the fact that our signal is being divided into three parts, instead of being collimate into one single signal. So to correct this problem we chose to collimate every three runs, take an average of the group, and redisplay the results. This was very possible to do and a very likely solution since each run was only giving us 1/3 of the total signal that we needed.


4133 1662 High4134 1649 Low4135 1667 Med4136 16574137 16454138 16634139 16534140 16414141 16594142 16524143 16414144 16594145 16514146 16384147 16564148 16474149 16544150 16544151 16464152 16314153 16514154 16424155 16294156 1648

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ID W1001 Vales For All Runs

Column B


4133 5874134 5874135 5864136 5864137 5874138 5874139 5854140 5884141 5864142 5874143 5884144 5874145 5884146 5874147 5874148 5874149 5884150 5884151 5874152 5874153 5874154 5854155 5874156 586

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ID W1494 Values For All Runs

Column B


4133 11394134 11424135 11414136 11404137 11444138 11414139 1143 Med4140 1144 High4141 1141 Low4142 11444143 11444144 11434145 11434146 11444147 11424148 11454149 11454150 11454151 11414152 11414153 11414154 11394155 11424156 1140


4133 9234134 9204135 9234136 9224137 9214138 9254139 924 Med4140 922 Low4141 927 High4142 9344143 9314144 9334145 9304146 9254147 9294148 9274149 9274150 9274151 9254152 9224153 9274154 9234155 9224156 924

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Column B

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Data of Averaged Groups for ID W1005Ave. Run # Ave. ADC Counts

4134 910.334137 9124140 912.334143 911.334146 9124149 921.334152 911.334155 911.67

Averaged Data

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ID W1005 Averaged Runs

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As seen above to the left, when we average every 3 runs we get a single averaged ADC value, as well as a single run number to plot it against. Now when we averaged all the runs and plotted them, as shown above right, our graphs yield a single line data that is better descriptive of the LMS data and stability over the entire run, for this particular ID. However, we did encounter situations where not all of the averaged groups had1 Med, 1 High, and 1 Low data set. Some had 2 Med and 1 Low, 2 High and 1 Med, ect. . . This resulted in averaged values that were either above or below the mean for the averaged data set. This particular situation is shown above to the left, highlighted in red. Looking back at the previous slide, the averaged group of runs 4148, 4149, and 4150, yields an ave of 921.33 which is above the overall mean, and is displayed as the first point above the mean on the graph shown above. To correct these situations, different algorithms had to be devised and entered into the code to correct this problem.

As you can see above, my program does corrects the LMS data and fixes any data points that fall outside of the mean during the averaging of the data. I edited my program to correct all LMS data and handle all possible combinations of data. Where as my program is capable of handling various data sets such as; 2 High and 1 Low, 1 Low, 1 Med, and 1 Low, ect.. Hence now all incorrect data points will be collimated and corrected.

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ID W1005 Averaged Runs

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Corrected LMS Data

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W1005 Final Correction For All Cases

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Instead of setting the value of the averaged group equivalent to a predetermined group, or value already calculated, which is a widely used way to correct data, I'm using the values given within the averaged group to correct its self. An example of the code used to correct the data is as follows;

if (fabs(((val[0]+val[1]+val[2])) - ((val[0]+val[1]+val[1]))) <= 3.0) // This works { if ((val[1]-val[2])==0.0 && val[0] < val[1]) // This fixes #1

{val[2]= (val[0]-((val[1]-val[0])));

sum = val[0]+val[1]+val[2];

// cout <<sum <<endl; // This prints out the Sum of 3 runs

cout <<sum / 3.0 <<endl; // This prints out the Average of 3 runsk=0;sum=0.0;

}

This is the code I used to correct the data point mentioned earlier, in which the data points were corrected and the averaged of the group went from 921.33, as mentioned on slide #4, down to a value of 914, which is well with in the mean. This was done by reassigning the value of the 3rd run in the set, and recalculating the average of the group. The following is an example of how I corrected of this group, and is equivalent to the code written above.

Run 3 = Run 1 - ( Run 2 – Run 1) = 914 - ( 925 – 914) = 903

New Average = (Run 1 + Run 2 + Run 3) / 3.0 = ( 914 + 925 + 903) / 3.0 = 914

How I Corrected the Data

Thus, my program collimates and corrects the data graphs, but does not correct all of the data points for every ID. There are some incidents were my program does improve the data, but doesn't correct it to the point were the graphs are linear and smooth as shown earlier. These particular ID's and graphs are a result of an over exposure to radiation of the crystals, during the experimental run. The graphs of one of these exposed ID's are as follow;

Radiation Accident

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ID W1494 Not Averaged

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As shown in both graphs by the inverse spike in the data, the radiation accident happened around run 5050. What is even more interesting is as time passed from run to run, the crystal started almost repairing its self and rather re cooperated from the radiation damage done to it. To better understand this anomaly and others, the rate dependence of the LMS gain may need to be monitored and analyzed, to understand the effects from this radiation exposure in order to correct the data for all radiated ID's. This analysis is ongoing.

Other AnomaliesOther anomalies from graphs that are not yet explained are as follows;

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Correction of Missing LMS DataW1005 Data

Run # ADC Counts

4100 9164101 8994102 1858 Bad Run4103 1858 Bad Run4104 1858 Bad Run4105 1858 Bad Run4106 1858 Bad Run4107 1858 Bad Run4108 1858 Bad Run4109 1858 Bad Run4110 1858 Bad Run4111 9264112 9154113 9004114 9264115 9164116 8994117 9264118 9164119 9004120 9274121 1858 Bad Run4122 1858 Bad Run4123 1858 Bad Run

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Data correction for all missing runs or runs without LMS data will occur as follows;1) A complete list of all IDs having the same general plots must be made, and divided into groups of rather their plots are Linear, Exponential, etc...

2) For those plots that are Linear, a Mean value will be determined, and all ADC values for missing or bad runs will be set to a value equal to the mean. All Exponential plots and other plots will have to be fitted and a function will have to be calculated, and all missing or bad ADC values for these IDs will be set to that particular function in order to fill in all wholes present with in the data.

3) After all data is corrected and filled in, all data will be re-graphed and will hopefully display a single continuous line of data, depending on the ID, and will improve our stability plots, histograms, and overall data, such that calibration of HyCal can be performed.

In relation to statistics, when the LMS was developed and implemented into the PrimEx experiment, it was calculated that we would need a total of 2000 statistics of LMS data, for the total signal needed in order to properly calibrate the detector and it's associated instruments, and correctly calculate and record the short-term stability of the experiment it's self. However, upon analysis of the data, particularly analyzing the ADC spectrum of events, it was discovered that we were only accumulating about 700 statistics or events, instead of the 2000 initially determined. Where as in the set-up of the LMS, we 3 reference PMT's and 1 pin-diode that makes up the total LMS trigger. Thus giving us the following equation for the LMS trigger;

3 yap + 1 LED = LMS Trigger

Thus giving us a 3 to 1 ratio in relation to the 3 radioactive yap sources to the LED. Hence indicating that the data received by the LMS is only 1/3 of the total signal, every run gives us a signal of about 700 statistics instead of the 2000 needed.

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LMS Stability, Data Correction and the Radiation Accident within the PrimEx Experiment by