Data Capture over Easter 2013

Note: The following was the first major batch of test data taken for RAY. The documentation, data capture procedure and processing algorithms are all outdated: nonetheless, it could serve as a useful model to be preserved. This document was originally written for the Star Centre internal wiki.

A Timepix detector was used to measure the level of background radiation at several sites over the Easter holidays, on the third of April 2013, and a few days later, on the fifth of April 2013. This data was collected according to the procedure that can be found [RAY Method.pdf|here ], and then analysed. The results seem to suggest that the background radiation does not vary much between different parts of Kent near Canterbury, but does vary in time.

Aims

To collect data to serve as part of the RAY map, and to finalise the RAY procedure.

Initial Hypothesis

Variation in background radiation level will occur between areas with different geological make-up. This will be able to be detected with the Timepix microchip.

Experiment

At the time of the experiment, it had been decided to take data for around two hours at each data capture location. Note that presently, the procedure calls for one hour's data capture, following [Number for Data Capture Method.pdf| this presentation]. Two hours' data capture at each location meant that we could only take data at three different locations, even on a good day. On the first instance, it was realised that an additional stumbling block would be the limited battery capacity of the laptop. However, this led to purchase of an adapter which allows the laptop to be charged while driving to the next data capture location. The laptop used for the data capture was <tt> CERN@School 5</tt>, and the chip used was <tt>Newton</tt>. There were two sites visited on the first day, and three on the second.

Selling

This was the first site visited. The Threshold equalisation went smoothly, and the only problem was an errant pixel that was continuously hit if the THL was lowered by 30 from the mean value. Lowering the THL by 40 made the pixel dissapear. 119 frames were taken. [Threshold equalisation.png|thumb|right|alt=Threshold Equalisation|Threshold Equalisation] Soil samples were collected.

Westbere

All went fine here; 100 frames of data were collected, and soil samples were collected.

Bigbury Forest

This was the first data captured on the fifth of April. The threshold equalisation went well, but we had to cover the laptop in a plastic bag, as it started to snow. 120 frames were taken. Soil samples were collected. [Capture - Site 3 - Threshold Equalization.png|thumb|right|alt=Threshold Equalisation|Threshold Equalisation]

Preston

120 frames were collected. Soil samples were collected.

Chislet

102 frames were collected. Soil samples were collected.

Coordinates

Coordinates of data capture locations
Location Latitude Longitude
Selling 51.265137 0.927315
Westbere 51.297188 1.163864
Bigbury 51.27432 1.021771
Preston 51.291499 1.218882
Chislet 51.339406 1.174164

Data

Data are available on DAQMAP, for further study.

Analysis of Results

Results were analysed using the following code, using modules from [cluster analysis.txt| Todd's Code] [[Background Radiation and Time of Day| Explained here]]:

from future import division
import math
import os

def get_hits_directory(directory):
total_frames = 0
totals = [0, 0, 0, 0]
for filename in os.listdir(directory):
hit_list = []
if filename[-4:] != '.dsc':
rows = open(directory + "/" + filename, 'r')
total_frames += 1
for row in rows:
xyc = row.strip().split()
hit_list.append([int(x) for x in[xyc[0], xyc[1], xyc[2]]])
x = clusters(hit_list)
totals[0] += x[0]
totals[1] += x[1]
totals[2] += x[2]
totals[3] += x[3]
return totals, total_frames

def test(test_pixel, hit_pixels_working):
'For use in clusters()'
for pixel in hit_pixels_working:
if pixel[0] == test_pixel[0] and pixel[1] == test_pixel[1]:
return pixel
return False

def add_to_check(pixel):
'For use in clusters()'
return [[pixel[0]+1, pixel[1]], [pixel[0]+1, pixel[1]+1], [pixel[0], pixel[1]+1],
[pixel[0]-1, pixel[1]+1], [pixel[0]-1, pixel[1]], [pixel[0]-1, pixel[1]-1],
[pixel[0], pixel[1]-1], [pixel[0]+1, pixel[1]-1] ]

def clusters(hit_pixels):
hit_pixels_working = []
for term in hit_pixels:
hit_pixels_working += [term]
clusters, dots, blobs, large_particles, to_check = [], [], [], [], []
for pixel in hit_pixels:
if hit_pixels_working.count(pixel) != 0:
cluster = []
hit_pixels_working.remove(pixel)
cluster += [pixel]
to_check += add_to_check(pixel)
for test_pixel in to_check:
result = test(test_pixel, hit_pixels_working)
if result != False:
hit_pixels_working.remove(result)
cluster += [result]
to_check += add_to_check(result)
to_check = []

clusters += [cluster]
if len(cluster) == 1:
dots += [cluster]
elif 2 <= len(cluster) <= 4:
blobs += [cluster]
else:
large_particles += [cluster]
return len(clusters), len(dots), len(blobs), len(large_particles)

totals, total_frames = get_hits_directory(raw_input())
print 'Total Frames:', total_frames
errors = [a ** 0.5 for a in totals]
print 'Poissonian Errors:', errors
print 'Total count, dots, blobs, large_particles:', totals
print 'Total rate, dots, blobs, large_particles:', [a/total_frames for a in totals]
print 'Errors:', [a/total_frames for a in errors]

==Errors==
The errors are calculated by assuming a Poissonian distribution, where how far the count recorded is from the mean will be proportional to the square root of the number of clusters recorded. Note that this value denotes one standard deviation, so in order to have a 95% chance of being in the region, two standard deviations should be taken.

==Results==

Results have been rounded to four significant figure for legibility.
{| class="wikitable"
|+ Results
! Location !! Latitude !! Longitude !! Cluster Rate !! Dot Rate !! Blob Rate !! Large Particle Rate
|-
| Selling ||51.265137 || 0.927315 || 11.11 ± 0.3043 || 4.500 ± 0.1936
|| 3.592 ± 0.1730 || 3.017 ± 0.1586
|-
| Westbere || 51.297188 || 1.163864 || 10.31 ± 0.3195 || 4.023 ± 0.1997, || 3.208 ± 0.1782 || 3.069 ± 0.1743
|-
| Bigbury || 51.27432 || 1.021771 || 7.165 ± 0.2433 || 2.397 ± 0.1407 || 3.570 ± 0.1718 || 1.198 ± 0.09952
|-
| Preston || 51.291499 || 1.218882 || 7.496 ± 0.2489 || 2.421 ± 0.1415 || 3.455 ± 0.1690 || 1.6198 ± 0.1157
|-
| Chislet || 51.339406 ||1.174164 || 8.553 ± 0.2882 || 2.757 ± 0.1636 || 3.903 ± 0.1947 || 1.893 ± 0.1356
|}

==Results 2==

The same procedure was followed on 2013-04-29, and results are tabulated below.

Results have been rounded to four significant figure for legibility.
{| class="wikitable"
|+ Results
! Detector !! Location !! Latitude !! Longitude !! Cluster Rate !! Dot Rate !! Blob Rate !! Large Particle Rate
|- ±
| Leibniz || Selling || 51.265137 || 0.927315 || 10.2167 ± 0.4126 || 4.95 ± 0.2872 || 4.1333 ± 0.2625 || 1.1333 ± 0.1374
|-
| Newton || Selling || 51.265137 || 0.927315 || 8.8667 ± 0.2718 || 3.5167 ± 0.1712 || 2.95 ± 0.1568 || 2.4 ± 0.1414
|-
| Leibniz || Chilham || 51.231183 || 0.954952 || 8.0667 ± 0.3667 || 4.0167 ± 0.2587 || 3.3667 ± 0.2369 || 0.6833 ± 0.1067
|-
| Newton || Chilham || 51.231183 || 0.954952 || 8.1167 ± 0.3678 || 2.95 ± 0.2217 || 2.7333 ± 0.2134 || 2.4333 ± 0.2014
|-
| Leibniz || Westwell || 51.184999 || 0.869089 || 9.6833 ± 0.4017 || 4.8 ± 0.2828 || 3.95 ± 0.2566 || 0.9333 ± 0.1247
|-
| Newton || Westwell || 51.184999 || 0.869089 || 11.0333 ± 0.4288 || 4.3333 ± 0.2687 || 3.8167 ± 0.2522 || 2.8833 ± 0.2192
|-
| Leibniz || Brabourne || 51.145721 ||1.001164 || 10.5 ± 0.4183 || 3.4667 ± 0.2404 || 3.9833 ± 0.2577 || 3.05 ± 0.2255
|-
| Newton || Brabourne || 51.145721 ||1.001164 || 9.7333 ± 0.4028 || 4.8333 ± 0.2838 || 3.8 ± 0.2517 || 1.1 ± 0.1354
|-
| Leibniz || Folkestone || 51.10226 ||1.228982 || 10.4167 ± 0.4167 || 5.6 ± 0.3055 || 3.9667 ± 0.2571 || 0.85 ± 0.119
|-
| Newton || Folkestone || 51.10226 ||1.228982 || 10.3667 ± 0.4157 || 4.0333 ± 0.2593 || 3.4167 ± 0.2386 || 2.9167 ±0.2205
|-
| Leibniz || Whitstable || 51.362609 || 1.046278 || 11.6667 ± 0.441 || 6.1333 ± 0.3197 || 4.4333 ± 0.2718 || 1.1 ± 0.1354
|-
| Newton || Whitstable || 51.362609 || 1.046278 || 11.8302 ± 0.4725 || 4.6415 ± 0.2959 || 3.8491 ± 0.2695 || 3.3396 ± 0.251

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