1. Verifying the simulation of a novel muon radiography
system with experimental data
1105355M
School of Physics and Astronomy, Kelvin Building, University of Glasgow, G12 8SU
E-mail: 1105355M@student.gla.ac.uk
Abstract. Simulated studies presented by Aguiar et al. detailing optimal specifications for a
proposed muon radiography prototype detector, constructed using a slab of scintillating material
and 4 photomultiplier tubes, are verified. Further consideration is also given to the resolution
and a known limitation to the position reconstruction algorithm, the bias. An overall spatial
resolution is estimated at 9.32mm in comparison with ∼1cm reported by Aguiar et al. Imaging
potential of the detector is explored through both simulation and experimentation, with the
latter showing initial signs of promise.
1. Introduction
The Nuclear Physics Group (NPG) of the University of Glasgow operate a “muography” -
imaging with either radio- or tomography - project, with their current main focus on constructing
a large scale muon tomography system primarily to be used for detecting nuclear waste inside
large barrels. Other areas of research are being explored, one being looking into a new detector
module proposed by a group in Spain, Aguiar et al. [1], comprising scintillating material and
photomultiplier tubes. Further investigation comprising simulation, construction and testing of
this detector module is required however to see how viable it maybe for future use.
1.1. Muons
Muons are produced through the initial bombardment at the earth of cosmic rays originating
from deep space sources such as quasars, supernovae and the sun. Primarily made up of protons,
they interact via the nuclear force colliding with nuclei in the upper atmosphere to produce
primarily positive and negatively charged pions, alongside other particles such as kaons. These
pions then either interact or decay spontaneously via the weak force (∼26ns) into a positive or
negatively charged muon, alongside a muon neutrino or anti neutrino respectively, for example,
to satisfy conservation laws e.g.
π+
→ µ+
+ ν (1)
π−
→ µ−
+ ¯ν (2)
By the time the muons, weighing two hundred times as much as an electron, reach the
surface of the Earth the measured flux is roughly 10,000 muons per minute per square
metre(10000/min/m2) and mean energy between 3 and 4 GeV [2]. What makes imaging with
muons so enticing is the fact that they can penetrate denser materials than other imaging

2. modalities such as X-rays, without irradiating anything inside more than they already do in the
background [3].
1.2. Muon Tomography
The passage of a charged particle through air or any other medium can be described via
combining the many deflections caused by Coulomb scattering. Imaging with cosmic ray muons
was most notably first explored just outside of Cairo, Egypt in a search for hidden chambers in
the Pyramids of Giza by L. W. Alvarez et al. back in 1965 [4]. Ten years previous however, it
had already been used in Australia to measure the thickness of ice covering a mining tunnel [5],
but it wasnt until the invention of the spark chamber along with digital read-out systems that
such an imaging technique could be used with any real precision [4].
A muon tomography system can produce 3D images of objects by using the angle of deflection
of muons from the Coulomb scattering and with such scattering very sensitive to atomic number
(Z); a significantly greater level of scattering will be observed for high-Z materials. This allows
for the identification and characterization of various materials as well as clear differentiation
between materials of similar composition [6].
The set-up of a muon tomography system generally consists of four detection planes - two
above a test object and two below - predominantly comprised of lattices of either scintillating
tubes or gas filled drift tubes. Each plane can be described as a lattice of tubes running in the x-
and y-directions in order to obtain a coordinate of where the muon struck. The key to generating
the 3D images is in utilizing a Point of Closest Approach reconstruction algorithm (PoCA).
This essentially determines the point of intersection i.e. the scattering location of incoming
and outgoing vectors, or the closest point between the vectors describing a muons trajectory.
When a muon passes through the two detector planes above and below - passing through the
test object in between - its incoming and outgoing trajectories can be constructed and hence
a scattering angle calculated. When working in three dimensions, these two vectors may not
intersect and in such a case the shortest distance between them is estimated by determining a
pair of points of closest approach between the two vectors. As a result of this, the mid-point of
such a connecting line equates to the scattering point of the muon, known as the PoCA point
[7]. A diagram displaying this is included in figure 1.
Figure 1. (left) A sketch displaying the basics behind the PoCA reconstruction algorithm and
(right) the layout of the scintillating fibre lattice [8][9].

3. 1.3. Muon Radiography
Where as a muon tomography system is comprised 4 detection planes and relies upon the
incoming and outgoing trajectories of a muon to construct a 3D image, a muon radiography
system relies upon only 2 planes, measuring the transmitted energy flux - similar to X-ray
radiography. Muon radiographs construct the incoming muon vectors and trace back their
incidence to the location of the object to be imaged, this allows for the formation of a 2D image,
with less muons appearing due to absorption at the location of some denser material. They
are primarily used when imaging large, sometimes geographical, structures and can often be
combined by placing them in different positions around a structure to create a crude 3D image,
combining the resulting information from each detector. Muon radiographs can be placed, in
line, either side of a larger object to detect incoming and outgoing muons with the difference
between the collected data allowing for the formation of a rough image.
1.4. Detector Types
There are a number of variations of muography systems in use throughout the field that use
various detector modalities; scintillating fibres, gas-filled drift tubes, Gas Electron Multiplier
(GEM) tubes or emulsion gel. As previously mentioned, most systems currently in operation
use an overlapping of two layers of fibres/tubes in the x- and y-directions. When a muon strikes
a scintillating fibre the material ‘scintillates’ producing scintillation light in two fibres (showing
in red in figure 1) that is then detected by a multi channel photomultiplier tube (MAPMT).
This allows for a coordinate of the incident muon to be attained by tracing back the fibres to
their point of intersection. Such detector systems however, can prove to be delicate and difficult
to manoeuvre.
The drift tube based system, a common alternative, comprises a wire enclosed in a gas-filled
tube that upon being struck by a muon, ejects electrons from the gaseous atoms that then gather
on the positively charged wire. The position of the electron uptake combined with the muons
initial distance from the wire, provide a 2D coordinate as to where the muon struck [10].
An advance on drift tube technology involves using micro-pattern gas detectors such as Gas
Electron Multiplier (GEM) tubes. These tubes are lighter, more compact than current drift tube
technology and can also reach far more precise resolutions. A sheet of kapton foil is covered top
and bottom with copper and a surface-wide pattern of equally spaced microscopic holes punched.
Constant electric fields arise in each of the holes when a voltage is applied across the foil and
when a muon strikes, an electron is ejected from the gas, drifts into the holes and is amplified.
Adding in subsequent GEM layers one after the other is done to increase this amplification [11].
A downside to these gas based detector systems is in the use of gas itself - special maintenance
is required and unfavourable.
A less commonly used method (only available to radiography systems) is one of nuclear
emulsion comprising layers (10 - 1000µm thick) of gelatine mixed with a metallic compound,
often a combination of silver and a halogen (bromine, chlorine etc.). When a charged particle
travels through the gel, silver atoms are produced along its trajectory that allows for the tracking
of the moving particle. The downside to employing this variant of detector is its time inefficiency
in that despite its great resolution capabilities (<1µm), the gel needs to be sent away for off-site
analysis by specialists [12].
1.5. Current Research and Applications
There is currently a wide range of applications in which muography is employed, or has the
potential to do so - one being in border security. A large-scale muon tomography system is
currently in development in Canada by their Defence Research & Development Dept. as a form
of border security for detecting concealed Special Nuclear Materials (SNMs), a diagram of which
can be viewed in the left hand image of figure 2 [13].

4. Currently in use at border crossings in the United States and Canada to detect SNM are
gamma-ray radiography systems due to the fact that SNMs absorb gamma rays very well,
showing up as dark shadows. However, such a scanner uses a tiny radioactive pellet as its source
of gamma rays presenting a number of drawbacks. The pellet (Cobalt-60; half life of 5 years)
will eventually need to be replaced and handled appropriately, occupants of subject vehicles
need to step outside due to radiation exposure whilst the scan is taking place. Not only that
but imaging with gamma-rays becomes less effective when dealing with vehicles heavily packed
with other cargo and can obscure the 2D image, often producing confusing rogue shadows. A
three dimensional, radioactive-source-free muon tomography system serves to overcome such
shortcomings, allowing not only in simply the detection of SNM but also in discriminating
between several types of material that may be present [14]. Such an application of muon
tomography will potentially provide significantly increased vigilance at border crossings - a
welcome development with the increasing ever-prominent interest and threat posed through
nuclear weapons development.
Figure 2. (left) Schematic showing a potential muon tomography system as a vehicle scanner
[14] and (right) basics behind a muon radiography system used for imaging volcanoes [15].
A predominant application of muon radiography is in the imaging of volcanoes. Using
surrounding radiography telescopes, insight into their interior composition and evolution can
be obtained. Near-horizontal muons are used to measure the transmitted flux of muons through
the volcano, with a flux measured from the opposite direction to provide background data. A
subtraction method, as used later in this report, is utilized to see the difference between the
sets of data to obtain the absorption data, with denser materials absorbing more muons [15].
A diagram describing such a process is shown on the right in figure 2. Work on this has been
reported by H. K. M. Tanaka et al from University of Tokyo in their imaging of Mount Asama
[16] and Mount West Iwate, Japan [17] and similar work is being conducted as part of Project
DIAPHANE by the Paris Institute of Earth Physics through their studies on the Lesser Antilles
volcanoes in the Caribbean [18].
As previously mentioned, another notable use of muon radiography was in analysing the
damage to the Fukushima Daiichi reactor cores in the immediate aftermath of the tsunami. In
order to plan the dismantling and clear up of the site, it was important to know the extent of
the destruction and most importantly the location of the radioactive fuel. With the high levels
of radioactivity present in the reactor buildings it was impossible to visually examine the reactor
safely to find out such information[19]. Radiography detectors were placed North (N) and North
West (NW) of the Reactor Pressure Vessel (RPV) in order to obtain the outgoing muon flux
from different positions and by combining this data, a vague image of the core is obtained. A
diagram of the set up can be seen in figure 3 and the results in figure 4.

5. Figure 3. (left) A schematic showing the set up of muon radiography detectors around the
Fukushima nuclear reactor core [20] and (right) a visual simulation displaying how the Primary
Containment Vessel (PCV) should appear with a small black square in the centre indicating
dense material as the fuel [21].
Figure 4. (left) image obtained from NW detector and (right) image obtained from N detector
[21].
The left hand image in figure 4 shows the RPV as a tall white column, however when compared
with the left hand image of figure 3, the simulation, it appears that there is no sign of a denser
material inside the RPV. However, looking at the right hand image in figure 4, there is a
suggestion that there is some dense material present due to a shadow appearing at the centre.
By combining data from both detectors, a crude 3D image can be constructed by measuring
points of intersection of the incident muons from the two different angles and is concluded that
there is fuel located in the spent fuel pool, however the quantity unknown [21]. Such a use of
muon radiography was key in planning the decommissioning process of the Fukushima plant.
The scanning and imaging of spent fuel containers to detect nuclear waste is a further
application where imaging with muons is a useful technique. Inserting a container into a
tomography system allows for the detection and characterisation of nuclear fuel remnants in the
containers so they can be handled and stored appropriately - a focus of the NPG - leading into the
exploration of a new prototype detector using a slab of scintillating material and photomultiplier
tubes (PMTs) as position sensitive detectors [22].

6. The proposed new type of radiography detector has not been applied to muography before
with the initial concept envisaging the detector modules being part of a larger scale detector
system capable of imaging large objects through combining them into a bigger radiography
system. At the moment, the NPG operate a scintillating fibre based system with a spatial
resolution of 0.8mm [22] and other gas based systems are capable of producing resolutions as
precise as 50µm - this newly proposed type of detector is capable of ∼1cm resolution. In
comparison, it would initially appear that a substantial improvement is required for this type
of detector to compete with current systems, however, with the proposed used of this detector
being to image larger scale objects, the ∼1cm resolution may be of sufficient accuracy. Couple
this with increased material robustness and portability, cheaper construction and operating costs
and the ability to link multiple modules together; this new proposed detector hosts a wealth
potential and significant advantages over described muography detector variants. A diagram
showing how such a detector may be incorporated into a larger scale system is shown in figure
5 and a schematic of a single module shown in figure 6.
Figure 5. an artists impression of a how the proposed scintillator slab detector (red) may be
implemented into large scale muon radiography system [1].
1.6. Scintillator Slab Detector
A Spanish research group, Aguiar et al., have proposed this new muon radiography prototype
detector, comprised unsegmented plastic slabs of scintillating material. A square formation of
4 PMTs are used for determining the location of an incident muon, with a reported spatial
resolution of ∼1cm with 2.5-3.5cm diameter PMTs [1].
A schematic showing the inner workings, dimensions and a birds-eye view of the detector
can be seen in figure 6. The 4 PMTs, encased in plastic housing, are arranged in a square
formation on the centre of a slab of scintillating material (refractive index = 1.58) that is
contained in a light-tight structure. An optical coupling gel is placed between PMT head and
scintillating material to “impedance match” between refractive indices, maximising the amount
of transmitted light. Reflective foil (reflection coefficient = 0.98) is placed top and bottom of
the scintillating slab to reduce the amount of light that exits the material, with black paint
(reflectivity = 7.5%) covering the sides to minimise reflection back into the slab.

7. Figure 6. Schematic showing the workings and dimensions of the “Scintillator-Slab Detector”.
Figure 7. A schematic showing the inner workings of the head-on PMT implemented in the
detector [23].

8. 1.7. Photomultiplier Tubes
The photomultiplier tubes used in the detector are head-on PMTs. With reference to figure 7,
photons first strike the surface of the PMT and enter through the window onto the photocathode.
These photons excite electrons in the photocathode initiating the photoelectric effect and pass
through the focusing mesh as photoelectrons. The photoelectrons are then accelerated and
focused onto the first dynode where they are multiplied continually as they move along the
dynode chain - a process referred to as the “avalanche effect”. The multiplied photoelectrons
accumulate as charge on the anode which then provides an output signal [23]. The 4 PMTs
in the system will each be connected to a base - via the stem pins - that provides a voltage
to each of the dynodes in the dynode chain. The ratio of the photoelectrons at the anode to
those at photocathode is referred to as the gain of the PMT; an integral property in the study
of PMT-base optimisation to follow. A schematic of the type of PMT to be used in the detector
is visible in figure 7.
1.8. Anger Logic
When performing simulated studies on various aspects of the prototype detector, the quantity
but more importantly position of where the muons strike are predetermined. In reality however,
whenever the detector is in operation, the position of each muon detected is unknown and
thus a reconstruction algorithm for determining where the muon struck is required. A popular
mathematical method used in such position sensitive detectors employing a square formation of
PMTs is known as Anger logic. An Anger logic algorithm combines the position of each PMT
and the strength of signal received to produce a position in x (equation 3) and y (equation 4)
of where the particle hit.
X =
xPMT1(PMT1) + xPMT2(PMT2) − xPMT3(PMT3) − xPMT4(PMT4)
PMT1 + PMT2 + PMT3 + PMT4
(3)
Y =
yPMT1(PMT1) − yPMT2(PMT2) − yPMT3(PMT3) + yPMT4(PMT4)
PMT1 + PMT2 + PMT3 + PMT4
(4)
“PMT1” through to “PMT4” reads clockwise from bottom left (bottom of figure 6) along
with associated x and y positions. This formulation works reasonably well for particles incident
inside the square formation of PMTs however limitations are evident when a particle strikes
outside of this region along with a chronic problem associated with the distance from a PMT -
both to be discussed further[24].
2. Initial Simulations
Aguiar et al. conducted various simulations to determine optimal specifications for the detector
construction. The NPG constructed a reduced size detector of dimension 30x30cm, employing
12.7cm (0.5”) PMTs and thus several variables were studied in the simulation developed for this
project to validate them against the previous results published by Aguiar et al. The software
used for producing such simulations is GEANT4 -“a toolkit for the simulation of the passage
of particles through matter” [25]. Such software enables the construction of various shapes and
sizes of a vast range of materials to simulate how particles would behave in a desired set-up
using Monte Carlo methods. Referring back to figure 6, the prototype detector is constructed
in GEANT4. The software simulates a predetermined number of muons incident vertically
on the slab with a predefined location and mean muon momentum expected at sea-level. It
takes into consideration the generation of scintillation photons and their subsequent reflections
and absorptions produced by the interaction of a muon with the scintillating material, as they
propagate through the slab.

9. 2.1. Optical Coupling Index
The refractive index of the optical coupling gel, designed to maximize the transmission of light
from scintillator to PMT head, is tuned to determine the optimal value. The results compared
with those published by Aguiar et al. for 100 muons through (0, 0) at a fixed scintillator
thickness of 1.27cm are shown in figure 8.
Figure 8. Data (black) compared with Aguiar et al. (blue) for a varying optical coupling gel
refractive index.
Aguiar et al. reported an optimal refractive index of 1.62 and as a result of this, an increased
amount of data was taken around this point to see if slight changes in the refractive index would
show an increased level of detected photons. Figure 8 indicates a strong correlation between
data from both independent studies, with a strong peak observed at a refractive index of n=1.62.
Optical coupling gel of this refractive index is used in the constructed prototype.
2.2. Scintillator Thickness
The thickness of the scintillating slab of material is varied with other parameters fixed, including
the optimal refractive index of n=1.62. Results for 100 muons through (0, 0) can be seen in
comparison to those reported by Aguiar et al. on the left in figure 9.
As expected, an increase in thickness (and volume) of scintillating material produces a
significant increase in the average number of photons detected, with the data presented here
consistent with that published by Aguiar et al., albeit with a steeper gradient. This could
be as a result of discrepancies in the set-up of the detector in GEANT4 with slight variations
in the physics likely to produce differences in the data presented. It could also be attributed
to the surface roughness of the scintillating material -neglected in this investigation - being
much smoother than that defined by Aguiar et al. This would reduce the distance travelled by
scintillation photons in this simulation due to perfect reflections and thus more photons would
then reach the photon detectors before being attenuated.
2.3. Photon Detection Efficiency
While increasing the thickness of scintillating material comes hand in hand with an increase in
light detected - a sought after quality - the photon detection efficiency needs to be considered.
The photon detection efficiency measures the ratio of photons detected by the PMT to the

10. Figure 9. (left) Data (black) compared with Aguiar et al. (blue) for a varying thickness
of scintillating material and (right) showing the variation in photon detection efficiency with
thickness of scintillating material.
amount produced in the scintillator from the muon strike. Results for the simulation of 100
muons through (0, 0) for a varying thickness of scintillating material, in this instance versus
detection efficiency, compared with published results from Aguiar et al. are displayed in the
right hand plot of figure 9.
Observing the trends of the data in right hand plot of figure 9, it is clear there are
consistencies. Despite the increase in scintillator thickness bringing increased light, it also
decreases the photon detection efficiency considerably. While the left hand plot of figure 9 would
initially indicate that a thicker slab of scintillating material is advantageous, this is proven a
na¨ıve assumption by the right hand plot of figure 9. A thicker slab of scintillating material may
produce more photons however increases the likelihood of rogue scattering. With the operation
of the detector primarily reliant upon angles and energy, such a possibility could lead to incorrect
measurements and so a trade off between scintillator thickness and photon detection efficiency
is required - a thickness of 1.27cm is settled upon and 2.5cm by Aguiar et al.
It is also noticeable looking at the left hand plot of figure 9 that there are considerably
more photons detected than that by Aguiar et al, however when then observing the detection
efficiencies on the right in figure 9, both sets of data are very comparable. This would indicate
that there are more photons generated here than that by Aguiar et al. and points towards a
possible difference in the set up of the simulations - there are more photons produced per muon
in the simulations reported here than by Aguiar et al.
2.4. Light Response Function
The Light Response Function (LRF) presents how the PMTs perform as the interaction point
of the muon becomes further away, with an average signal from the 4 PMTs recorded as the
distance from the interaction point is increased. Results are presented in figure 10, comparing
the response of two PMT head sizes to 100 muons travelling through (0, 0) for a fixed scintillator
thickness of 2.5cm.
A 1.27cm diameter PMT was used in simulation since this is the size of PMT available to
the NPG for use in the constructed prototype. As expected, the average number of detected
photons decreases with distance from the interaction point - evident in both images in figure 10.

11. Figure 10. Comparison between data reported here (right) with that from Aguiar et al. (left)
for the LRF of a 1.27cm and 2.5cm diameter PMT and a 2.5cm and a 3.5cm diameter PMT
respectively, both as a function of distance from interaction point.
A near 4-fold increase in PMT active area (1.27cm to 2.5cm diameter) produced a ∼55% increase
in the average number of detected photons. In comparison, the data presented by Aguiar et
al. produced an increase of ∼88% in the average number of detected photons for a near 2-fold
increase in active area (2.5cm to 3.5cm diameter). This difference in the increase in detected
photons between the studies here and by Aguiar et al. (55% and 88%) could be attributed to
the small angles subtended by the 1.27cm PMT and thus a ratio between the data of 1.27cm
and 2.5cm will not produce as significant an increase in detected photons as with 2.5cm and
3.5cm diameter PMTs. Nevertheless, the general trend observed in the data presented here
shows strong consistencies with that presented by Aguiar et al.
2.5. Light Dispersion
The dispersion in the LRF is proportional to statistical variations in light detection and is
inversely proportional to the square root of the number of photons, N i.e.
Dispersion ∝
N −
√
N
√
N
(5)
This dispersion is expressed as a percentage of the average number of photons, N, and a
comparison with data reported by Aguiar et al. can be seen in figure 11.
It is clear that the dispersion of light is lower for the larger diameter PMT. Aguiar et al.
reported an average improvement in dispersion from a 2.5cm to a 3.5cm diameter PMT of
∼5% over a range from 0-50cm from PMT to interaction point. The data presented here over
approximately half of this range provides a ∼2% improvement in dispersion. Looking closely at
both sets of data in figure 11, precisely at ∼11cm, it is visible that the improvement in dispersion
is very similar, with Aguiar et al. calculating their average dispersion improvement of ∼5% over
approximately twice the range. In summary, both systems have proven relatively consistent
with each other, with this verification promoting confidence in results to follow.
3. Further Studies
In addition to verifying the studies by Aguiar et al. on the detector specifications via simulation,
further studies were performed to tune the simulation to the intended real-life scenario to assess
the precision of determining the position of a muon.

12. Figure 11. Comparison between data reported here (right) with that from Aguiar et al. (left)
for the LRF dispersion of a 2.5cm and a 3.5cm diameter PMT and the LRF of a 1.27cm and
2.5cm diameter PMT, both as a function of distance from interaction point.
3.1. Resolution
Consideration was given to investigating the resolution across one quadrant of the detector, with
symmetry across the remaining quadrants assumed. Referring to the top left image of figure 12,
the sigma value of the distribution in x provides a spatial resolution in x. Combining this sigma
value in quadrature with the equivalent sigma value in y at (0, 0) - and indeed for every other
location - a 2D distribution can be obtained (bottom of figure 12) and hence spatial resolution at
that coordinate calculated. This process is repeated for all 36 positions to produce a resolution
map of one quadrant of the detector and is shown in the left hand plot of figure 13.
It is evident from the left hand plot of figure 13 that the resolution varies across the detector
surface, but is noticeably and expectedly at its best when closest to a PMT. As mentioned
before, there are limitations in the position reconstruction algorithm when outside of the central
region and is inherent when looking at resolution - it is at its worst along the edges of the
slab, outside the central region, a consequence of the “bias effect”. Taking an average spatial
resolution across the quadrant produces an overall average spatial resolution of 9.32mm - very
comparable to the ∼1cm resolution reported by Aguiar et al.
3.2. Bias Effect
As touched upon before, there is a significant limitation to the Anger logic algorithm known as
the bias effect and is investigated further. Consider a PMT recording the largest signal after a
muon strike, the other 3 PMTs will still record a signal and thus drag the constructed position
away from (towards the centre) where the muon actually struck. The degree to which this occurs
is known as the bias. Examples of this effect can be seen in figure 12 where 1000 muons were
simulated striking the detector at two positions; (0, 0) and (80, 0).
From the diagrams in figure 12, it is clear that the mean position of the detected muons
are centred not on x=0mm and x=80mm, but on x≈-0.4mm and x≈40mm respectively,
showing a shift between actual and reconstructed positions (calculated using equations 3 and 4),
particularly evident at (80, 0). When investigating this effect further over one quadrant of the
detector, a map showing the extent of the bias at 36 equally spaced positions in the quadrant is
shown in right hand plot of figure 13.

13. Figure 12. Clockwise from top left: a 1D histogram in x showing data for 1000 muons through
(0,0), (80,0), a 2D histogram in x and y for 1000 muons through (80,0) and (0,0).
Figure 13. (left) A map displaying the average spatial resolution and (right) the extent of the
bias across a quadrant of the detector surface.
From looking at the right hand plot figure 13 it is clear that the bias has minimal effect
when muons strike inside the central region of the detector but becomes most influential when
they strike outside of this region. As expected, the farthest away point from the PMT at
(140, 140) shows the bias having maximum influence. This effect may be corrected for through
implementing an equation or perhaps a 2D matrix into the analysis software and is the subject
of future work by the NPG but was beyond the scope of this project.
4. Component Optimisation
Before the constructed prototype can be used, PMTs and bases need to be tested and appropriate
ones selected for use. As mentioned before, the gain of the PMTs is important for a uniform

14. response from each PMT to the same amount of light and thus a strategy is required for
appropriate PMT selection and base coupling.
4.1. Experimental Set-up
The PMTs and bases are tested using the Laser Testing Facility (LTF), a diagram of which can
be seen in figure 14.
Figure 14. A diagram of the Laser Testing Facility including important circuitry. Diagram
adapted from [26].
A blue laser of wavelength ∼420nm is mounted on a platform that can be moved in horizontal
and vertical directions. In the case of this experiment, the laser will remain in a fixed position,
incident on the centre of the PMT surface - it was verified that the location of the laser on
PMT head is irrelevant to signal recorded. Neutral Density Filters (NDFs) are positioned in
front of the laser to attenuate the light to an appropriate intensity, the index of which remained
at 2.0 for the duration of the testing. The PMT is mounted on the Z stage with the surface
of the PMT perpendicular to the laser and remains in a fixed position for the duration of the
experiment. The aforementioned components are all contained in a light tight box. The PMT
is connected to a high voltage supply and QDC modules both located outside of the light tight
box. The QDC modules serve to assign the charge that accumulates on the anode of the PMT to
a channel number that is then read into the computer for analysis. The laser settings remained
constant throughout the PMT testing in order to compare their response to the same level of
light - a necessity in being able to normalise their responses. When the laser is incident on the
PMT and a voltage supplied, the data recorded will take a general form visible in figure 15. The
spike on the left is known as the “pedestal” and arises from the charge that accumulates on the
anode when the PMT is not registering a hit from the laser. The wider distribution to the right
is the “data”. The difference in the mean positions of the pedestal and the data is the gain of
the PMT at that particular supply voltage. It is the gain of each PMT that will be investigated
to determine the appropriate PMTs for selection.

15. Figure 15. (left) an example of the pedestal and (right) an example of the data obtained
from taking a measurement when the laser is incident. Attention should be paid to the axes for
appropriate comparison.
4.2. Photomultiplier Tube Testing
There were around 60 PMTs available for testing, however due to obvious time constraints not
all could be tested so a random selection of 11 were taken forward for experimentation. The
manufacturer recommendation for maximum supply voltage was 1800V. Each of the 11 PMTs
were mounted on the fixed Z stage (one at a time), the laser incident on the PMT head and the
maximum voltage supplied each time. For consistency, the same PMT base was used for each
PMT being tested. The gain of each PMT, normalised with respect to the highest performing
PMT is displayed on the left in figure 16. The PMTs tested were found to provide a range of
gains.
4.3. Base Testing
There were 6 bases available for use, however with only 4 required, testing and appropriate
selection was needed. A PMT with gain in the middle of the obtained range is selected for
testing each of the 6 bases for consistency and the maximum voltage of 1800V supplied each
time. The gain of the PMT with each of the 6 bases, normalized with respect to the base
providing the highest gain, is displayed in the centre of figure 16.
4.4. Optimising PMT-Base Pairs
With testing completed and gain figures attainted for each of the PMTs and bases, an
appropriate selection strategy is required. Initially, the 4 highest gain PMTs were to be matched
up with the 4 best performing bases and matched up best PMT to worst base etc. However,
due to inconsistent performance from 2 bases and a PMT this was not possible. PMTs 1, 2, 3
and 5 were paired according to the proposed logic with bases 4, 1, 2 and 5 respectively, forming
pairs labelled 1-4. The results are presented on the right in figure 16 with the gain of the PMTs
and bases normalised with respect to the highest gain PMT and base respectively and selected
to form pairs of estimated similar gain.
As can be seen from the right hand graph in figure 16, the pairs match up reasonably well using
the aforementioned logic with only minimal voltage alteration required. The supply voltages
are tweaked so they all perform very similarly, included in table 1, with pair 4 lowest gain pair
- set fixed and the supply voltages of the remaining 3 pairs lowered to match the gain of the
fixed pair. It was important to normalise the combined gains of the PMT-base pairs prior to
data collection so as to minimise any surface correction that could introduce an error into the
calculated muon position.
With 4 PMT-base pairs optimised, they can be implemented into the detector to hopefully
provide accurate measurements for the appropriate amount of light. This is checked by recording
the response from scintillation originating at (0, 0) and is presented in section 6.1.

16. Figure 16. (left) the gain of each PMT normalised to the that with the highest gain, (centre) the
gain of each base normalised to that with the highest gain and (right) the combined normalised
gain of each PMT-base pair showing relative uniformity.
Pair Supply Voltage (V)
1. (PMT1 - Base 5) 1650
2. (PMT2 - Base 4) 1710
3. (PMT3 - Base 3) 1750
4. (PMT4 - Base 1) 1800
Table 1. Detailing the optimal supply voltages for each PMT-base pair to provide uniform
performance to the same amount of light.
5. Simulation Imaging Capabilities
Prior to experimental testing, a simulated investigation into the imaging capabilities of the single
slab system is conducted by placing uranium blocks directly on the surface of the detector. Where
as the simulations previously included the 4 PMTs, these simulations do not and simply measure
the transmitted muon flux through the slab - this was done to save time since a significantly
larger number of muons are required to produce a result. The data however can be made relevant
to the 4 PMT detector set-up by incorporating the overall average spatial resolution of 9.32mm
into the simulation. By smearing the recorded x and y positions of the incoming muons by
9.32mm, presented data will bear a resemblance to what may be recorded by the detector with
the PMTs in place.
5.1. Uranium
The detector slab is set-up with 4 pieces of uranium (60x60mm) each of different thicknesses
(10, 25, 40 and 55mm), equally spaced with 7 million muons generated. Results are presented in
figure 17 for imaging uranium with a perfect detector (top) and with the smeared data (bottom).
It is clear from the top half of figure 17 that the 4 pieces of uranium are perfectly visible and
the varying thicknesses distinguishable. Referring to the lower half of figure 17, the resolution
is significantly less now that the resolution has been incorporated and is now questionable as
to whether the 10mm thick slice of uranium is distinguishable amongst surrounding noise. An
increased amount of data from the 7 million muons generated for this simulation (∼3.5m were
detected) would possibly allow for this slice of uranium to become resolvable, nevertheless the
other 3 pieces of uranium are still visible and the differing thicknesses discernible.
This simulation involved the simplest and most ideal case where the object to be imaged is
small enough to be placed directly on top of the detector, however as discussed before, in reality

17. Figure 17. (top) the subtraction and results between detected muons with and without uranium
and (bottom) the same data smeared in x and y by 9.32mm. Clockwise from bottom left; 10,
40, 55 and 25mm pieces of uranium.
such a detector would potentially be exploited for larger scale operations. A second identical
detector module would be positioned a small distance apart from the other, acting in tandem
to reconstruct incoming muon vectors. This simplified set-up was simulated in order to provide
an indication of what is achievable with the constructed prototype slab that is tested in the
following section.
6. Testing the Detector
With the optimised PMT-base pairs obtained and in place, the detector is tested to see initially
if the they, as expected, perform uniformly.
6.1. Initial Results
A Strontium-90 source (β-emitter) is placed in the centre of the detector at (0, 0) to provide a
known position for the incident particles. The appropriate supply voltages determined for each
of the PMT-base pairs are applied and ∼20,000 events recorded. The results are displayed in
figure 18.
From observing each of the graphs in figure 18, it is clear that the gain of each of the PMTs
are very similar, almost identical, with the difference never greater than ∼10% - something that
is then corrected for in the software. This proves that the effort taken to optimise the detector
through PMT and base testing has paid off.
6.2. Light Response Function: Experimental Data
The LRF of the detector is then investigated by moving the position of the Sr-90 source and
recording the signal observed from a single PMT. A comparison between simulation using
GEANT4 and experimental data can be seen in figure 19.

18. Figure 18. Data obtained from 4 PMTs in response to a Sr-90 source located at (0, 0) for
around 20,000 events showing strong gain uniformity.
Figure 19. Comparison between the LRF obtained through simulation and experimentation,
showing very similar trends.

19. Figure 19 shows a strong correlation between simulation and data obtained from the detector
- such a correlation successfully validates the simulation.
6.3. Imaging a Block of Lead with Muons
To further examine how well the prototype detector operates, a block of lead is positioned in
the centre of the detector and data collected with and without the lead block in place. Due to
time constraints, a limited number of events, ∼200,000 muons, are taken and hence reduces the
quality and accuracy of the result presented in figure 20.
Figure 20. (left) data collected from 3 PMTs with a block of lead centred on (0, 0), (centre)
data collected from 3 PMTs without the lead in place and (right) the ratio between the data
without and with lead, showing that there is more muons detected when the lead is absent.
It is important to note that an abnormality was identified in the performance of a PMT and
thus data collected from this PMT was ignored. Despite this, Anger logic is still applied to the
data collected from the remaining 3 PMTs to produce an approximate image. It is evident from
comparing the images left and centre in figure 20 that there is a difference, with the central
image showing more muons collected in the centre - where the lead was placed. The right
hand image is the ratio between the central image (no lead) and the left image (with lead),
emphasising the increase in muons detected in the central region when the lead was not present.
This serves as an indication that the prototype is possibly detecting the lead block however a
significantly greater quantity of data is needed to provide definitive conclusions. Simulations
presented earlier detected ∼3.5 million muons compared with ∼250,000 here.
7. Conclusion
Initial simulated studies on the properties of the prototype proposed by Aguiar et al. have been
validated for a modified version built by the NPG at the University of Glasgow, with consistencies
presented for various specifications between the data reported here and Aguiar et al. Further
consideration has been handed to other properties of the detector; the spatial resolution and
the bias. The former is estimated at 9.32mm over a quadrant of the prototype detector and
the extent of the latter inherent across the detector surface, most predominantly outside of the
central region enclosed by the 4 PMTs - a consequential limitation of the Anger logic position
reconstruction algorithm. A selection of PMTs were tested and 4 optimal PMT-base couples
presented for implementation to the prototype detector for initial experimentation. Imaging
qualities of the prototype have been both simulated and physically tested with simulations
indicating that >7million muons are required to successfully resolve a 1cm thick slice of uranium.
Early stages of physical experimentation with the prototype detector served promise with a

20. lead block visible, yet not fully conclusive with more data needed to say categorically that the
prototype is capable of the imaging proficiencies simulated. The prototype muon radiography
system proposed by Aguiar et al. has shown early signs of promise and with further investigation
and experimentation could prove a potential revolution in the field.
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