{"id":1011,"date":"2022-10-18T14:38:24","date_gmt":"2022-10-18T21:38:24","guid":{"rendered":"https:\/\/radwatch.berkeley.edu\/?p=1011"},"modified":"2026-04-28T21:34:22","modified_gmt":"2026-04-29T04:34:22","slug":"dosenet-summer-2022-akhilandeshwari-krishnans-reportv2","status":"publish","type":"post","link":"https:\/\/radwatch.berkeley.edu\/es\/2022\/10\/dosenet-summer-2022-akhilandeshwari-krishnans-reportv2\/","title":{"rendered":"DoseNet Summer 2022: Akhilandeshwari Krishnan\u2019s Report"},"content":{"rendered":"

Overview<\/strong><\/h2>\n

Akhilandeshwari Krishnan is a high school freshman newly working with DoseNet at UC Berkeley. His first project in Summer 2022 focused on spectral data analysis and obtaining net counts from a peak.\u00a0 Akhila put together a report of the multiple procedures used and the resulting counts of significant peaks in data from the rooftop Cesium Iodide sensor.<\/span><\/p>\n

 <\/p>\n

Introduction\u00a0<\/strong><\/h2>\n

This summer, my project was to build a tool that extracted the net counts of radiation from a given peak in a spectrum.\u00a0 After cleaning spectral data, two functions were created for obtaining the net counts of a peak, with one function using integration and the other utilizing background subtraction with a choice of two procedures.\u00a0 Using several methods of extracting net counts allowed comparison and contrast between different procedures, and helped determine the limitations of each.\u00a0 The net counts of the peak were then graphed into plots.\u00a0 This tool was used in analysis of data from DoseNet\u2019s rooftop Cesium Iodide sensor.\u00a0<\/span><\/p>\n

 <\/p>\n

Procedures<\/strong><\/h2>\n

Data was taken from the rooftop sensor on Etcheverry Hall with a focus on calculating the net counts of radiation from the Potassium-40 peak in particular.\u00a0 Input data representing KeV and counts per minute were read into a Pandas dataframe from a csv file.\u00a0 Data often includes missing or NaN values that have to be cleaned, so these non-numerical values were forward-filled to avoid miscalculations.\u00a0 This complete spectrum was then plotted as a reference dataset for selecting peaks.\u00a0 Below is the spectrum of the rooftop Cesium Iodide sensor on Etcheverry Hall.\u00a0 As shown in the figure, there are multiple significant peaks visible.<\/span><\/p>\n

Figure 1. Etcheverry Roof Cesium Iodide full spectrum plot <\/span><\/p>\n

\"Selected

Net Counts (tail-trained background): 25676.10312889503<\/p><\/div>\n

Integration<\/strong><\/h2>\n

The first method to isolate the net counts of a peak used integration to sum all the counts of a peak.\u00a0 The function accepts an array of x and y values each, the peak\u2019s left bound and right bound, and Gaussian curve estimates as arguments from the user.\u00a0 A Gaussian fit is generated for the specified peak.\u00a0 Then the Gaussian is integrated, essentially summing all radiation counts under the curve.\u00a0 Figure 2 shows the Thallium-208 peak\u2019s results using integration.<\/span><\/p>\n

 <\/p>\n

Figure 2. Thallium-208 peak net counts (integration)<\/span><\/p>\n

\"selected

Net Counts: 17504.21875021408<\/p><\/div>\n

Background Subtraction (Left-tail Trained)<\/strong><\/h2>\n

Another approach was to determine and subtract the background radiation from the peak.\u00a0 Background counts come from the radiation bouncing off every surface in the environment, usually in miniscule doses but varying depending on location.\u00a0 The function accepts an array of x and y values as input data, the peak\u2019s left bound and right bound, and estimate values for an exponential curve as arguments from the user.\u00a0 Optionally, tail length around the peak can be specified, as well as a starting offset in case of noise early in the data.\u00a0 An exponential fit is then generated, trained on the left tail, and used to interpolate between the peak\u2019s bounds.\u00a0 The model is not right- or two-tail trained because of sensor settling noise; often sensors experience momentary \u2018bouncing\u2019 to settle after detecting a significant peak and such noise can cause miscalculations.\u00a0 After interpolation, the background values are subtracted from the peak\u2019s values, resulting in the net counts.\u00a0 The original peak data, background fit, and net-count peak are all plotted on a graph. Figure 3 shows results from another significant peak, using left-tail training for background subtraction. <\/span><\/p>\n

Figure 3. Significant peak net counts (tail-trained background subtraction)<\/span><\/p>\n

\"selected

Net Counts (tail-trained background): 25676.10312889503<\/p><\/div>\n

Background Subtraction (Linear Estimator)<\/strong><\/h2>\n

An option in the background subtraction function was built in case of particularly noisy or unusable data, where training a model on the left tail would produce inaccurate results.\u00a0 This procedure simply generates a straight line between peak bounds as a background fit.\u00a0 The linear estimate\u2019s values are subtracted from the peak\u2019s values, resulting in the net counts.\u00a0 The original peak data, linear estimate, and net-count peak are all plotted on a graph.\u00a0 Figure 4 shows results from a smaller peak, using linear-estimate background subtraction and resulting in <\/span>66984.17000000009 net counts.<\/span><\/p>\n


Figure 4. Smaller peak net counts (liner-estimate background subtraction)<\/span><\/p>\n

\"Full

Net Counts (linear-estimate background): 66984.17000000009<\/p><\/div>\n

 <\/p>\n

Comparison of Methods<\/strong><\/h2>\n

Integration using a Gaussian fit is excellent when given good starting estimates.\u00a0 Background subtraction using left-tail training works well for peaks with level tails, but requires adjusting the training tail length when noisy tails are present.\u00a0 Background subtraction using linear estimation is a viable option for <\/span>peaks with level tails.\u00a0 However, the line cuts through peaks with uneven tails.\u00a0 Using multiple procedures to calculate net counts allows the user to try them all and determine the best option for specific conditions.<\/span><\/p>\n

 <\/p>\n

Conclusion<\/strong><\/h2>\n

Net counts from the Potassium-40 peak were calculated using all three methods.\u00a0 The figures below illustrate each.\u00a0 Integration resulted in 162684.86434594737 counts and left-tail background subtraction resulted in <\/span>167310.97734118442 counts.\u00a0 These values are quite close to each other<\/span>, however the linear-estimate background option for this particular peak is (even visually on the plot) a somewhat inaccurate method choice at <\/span>155638.5722222222 counts.\u00a0\u00a0<\/span><\/p>\n

Next steps could involve using Plotly instead of Matplotlib to provide interactive graphs instead of static representations.\u00a0 One observation is that the left-tail trained background model is extremely sensitive.\u00a0 Further work would try to improve this oversensitivity and achieve greater accuracy.\u00a0 <\/span><\/p>\n


Figure 5. Potassium-40 peak net counts (tail-trained background subtraction)<\/span><\/p>\n

\"selected

Net Counts (tail-trained background): 167310.97734118442<\/p><\/div>\n

Figure 6. Potassium-40 peak net counts (liner-estimate background subtraction)<\/span><\/p>\n

\"selected

Net counts (linear-estimate background): 155638.5722222222<\/p><\/div>\n

Figure 7. Potassium-40 peak net counts (integration)<\/span><\/p>\n

\"selected

Net Counts: 162684.86434594737<\/p><\/div>\n

All graphs were generated using the Matplotlib library in Python.\u00a0 NumPy and SciPy libraries in Python were used for data processing.<\/em>

<\/p>\n

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