Proton Radiography. University of Amsterdam. Master Thesis. M.M.A. Dietze. Prototype Development Towards Clinical Application.

Transcription

1 University of Amsterdam Master Thesis Proton Radiography Prototype Development Towards Clinical Application by M.M.A. Dietze June 2016 Supervisor: dr. J. Visser Examiners: prof. dr. ir. E.N. Koffeman dr. A.P. Colijn

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3 Abstract Proton radiotherapy can provide an addition to cancer treatment as the delivered dose in the patient can be deposited accurately. Since the path to the tumour is determined by Computed Tomography and Magnetic Resonance Imaging, a calibration between proton stopping power and photon mass attenuation coefficient needs to be made. As this translation is complex due to the difference in interaction mechanisms, it is necessary to increase the margins around the tumour location. More tissue is irradiated as a consequence, reducing the benefit of proton radiotherapy. This problem would be resolved when protons are used for imaging purposes, which introduces the field of proton radiography. As protons scatter significantly, they must be tracked individually before and after traversing the patient. A radiograph of the different densities in the body follows from the measured energy loss along the reconstructed path. The requirements of measuring the particle s position and energy deposition lead to a distinct detector design. A prototype consisting of two Time Projection Chambers and a BaF 2 calorimeter (ProPix I) was constructed in previous studies. The data collected in 2015 at the proton beam of KVI, Groningen, is used to further characterise said configuration. Several improvements on the acquisition software are performed and a data analysis is presented afterwards: the energy deposit radiograph is shown to possess a 2.5% density resolution and the scattering radiograph is constructed for the first time. Additionally, the combination of both parameters is shown to provide an excellent method of separating materials. As ProPix I is limited in its data-acquisition rate, a second generation design (ProPix II) - fully based on pixel detectors - is proposed. After simulations show the promising features of said configuration, some characterisation is performed with a radioactive source. By the collection of results on spatial resolution, density resolution and data-acquisition rate, it is shown that the proposed design has properties close to those required for clinical application.

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5 Contents 1 Introduction 1 2 Particle physics in medicine Particle interactions Radiotherapy and imaging Proton radiography Challenges and aims State-of-the-art prototypes Detector overview Time Projection Chamber Gas Electron Multiplier Timepix3 readout chip Calorimeter and trigger ProPix I prototype Large clustersize Gas flow in chamber Electric field distortions Data preparation Phantom reconstruction ProPix II prototype Prototype simulations GEM voltage scan Electric field distortions Choice of gas mixture Hybrid silicon sensor Conclusion and outlook 47 v

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7 1 Introduction Cancer is one of the main challenges faced by medicine today [1]. Sizeable progress has been made recent years with the improvement of treatment techniques, but a tremendous amount of effort remains necessary in order to reduce the mortality rate. Fortunately, many promising developments in research are ongoing. One of these emerging techniques is the usage of charged ions for radiation treatment. Protons can, like photons in gamma-treatment, transfer damaging energy to the tumour cells. The main advantage of proton usage comes from the relative high radiation dose in targeted tissue, combined with low dose in its surroundings [2]. For specific cases, the optimisation of proton therapy can result in reduced harm of healthy tissue, improving the patients quality of life by side-effect reduction. Along with the primary advantage of proton therapy, also comes the biggest risk. Since a substantial fraction of the particle s energy can be delivered in a precise target area, its location needs to be known with great accuracy. Currently, the position of the tumour is determined by a combination of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). As the underlying mechanisms of photon attenuation length and proton stopping power are fundamentally different, a calibration between both needs to be made. Error margins on the tumour location in the order of a centimetre are induced by this translation, reducing the proton therapy benefit [3]. The calibration complication would be resolved if imaging is performed with protons correspondingly, so that the same physical processes occur in both scan and treatment. A radiograph of this sort is made by the administration of a well-defined proton beam onto the patient, with the particles possessing an energy capable of traversing the soft tissue. Two main parameters can be obtained from the exiting protons: their energy loss in the material and the scattering angle. As the obtained values are correlated with the density and type of the material they have traversed, the composition of the tissue can be reconstructed. The requirements of tracking the particle s position and energy lead to a distinct detector design. Current state-of-the-art prototypes use silicon strips or scintillating fibres as their tracking devices. These sub-detectors have the unresolvable disadvantage of significant interference with the proton beam, ultimately limiting the radiograph resolution in both the spatial as the energy domain. As a proof of principle for tracking purposes, Time Projection Chambers (TPCs) have been constructed at Nikhef, Amsterdam [4, 5]. These chambers provide numerous benefits over other trackers, of which reduced beam interference and radiation hardness are most relevant. This thesis will evaluate the existing TPCs and investigate the feasibility of TPC implementation in proton radiography devices, using the measurement of density resolution, data acquisition rate and spatial resolution as a point of reference. 1

8 CHAPTER 1. INTRODUCTION Regarding the energy measurement, the most commonly used devices are range telescopes consisting of several plastic scintillator layers. Whilst these telescopes can provide excellent energy resolution, they possess a finite data acquisition rate as they lack the ability to process multiple events simultaneously. Since the BaF 2 calorimeter used in the previous studies is likewise limited in its rate, the performance of a fast hybrid pixel sensor construction will be investigated. The ability to measure position and deposited charge at the same time could be beneficial in terms of track resolution and overall detector cost. As this combination has not been performed before in relation to proton radiography, it will be investigated whether the proposed design has the intrinsic properties required for clinical application. This thesis will start with an introduction into particle physics in medicine, in which it will be shown where current challenges lie and how the two investigated prototypes can contribute. The individual components of ProPix I (consisting of two TPCs and the BaF 2 calorimeter) and ProPix II (consisting of one TPC and two hybrid silicon sensors) will be discussed afterwards. Using measurements on the proton beam at KVI, Groningen in 2015, the performance of ProPix I will be discussed and a data analysis is presented. The design of ProPix II is motived with the aid of extensive simulations and the found improvements from ProPix I, after which some characterisation with a radioactive source is performed. From the collected results it will become clear whether pixel-based detectors have a future in proton radiography. 2

9 2 Particle physics in medicine This chapter will provide some detail on the different types of particles used for radiotherapy. Several particle interactions will be discussed first, in order to get a proper insight into the physical processes occurring in the patient. Following this, an introduction into imaging and radiation treatment is given and this chapter continues with the discussion of proton radiography. The requirements for prototype application in a clinical setting will be shown afterwards and the several efforts worldwide will be reviewed. Finally, it will be discussed in which way the prototype in this work can be beneficial over other devices. 2.1 Particle interactions The particle interaction mechanisms will be discussed separately for photons (X-rays), electrons (light charged particles) and protons (heavy charged particles). The dose deposit for these different particles in water is shown, as a reference point, in Fig This figure shows that the deposition as a function of distance inside the volume can be found to be distinctly different for every particle type. The characteristics on the shape of these graphs will be discussed below. a Photo-electric effect. b Compton scattering. c Pair-production. Figure 2.1 The different photon interactions with matter. Photons Photons interact with matter primarily via the photoelectric effect, Compton scattering and pair production (Fig. 2.1) [6]. In the first process, an electron is ejected from its shell (or the lattice) by the conversion of all photon energy. Compton scattering is an event in which the photon scatters of an atom and loses only a fraction of its energy, by the ejection of an electron from its orbital position. The targeted electron and photon will both travel through the material in this case. If the energy of a photon is larger than 1.02 MeV, pair-production can occur by the creation of an electron-positron pair. 3

10 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE Figure 2.2 The fractional dose deposit of several types of particle in a target volume consisting of water simulated in Geant4. The shape of the curve for the different particles can be found to be distinctly different. Figure 2.3 The proton stopping power (MeV cm 1 ) in 2.32 g/cm 3 silicon absorbing material from NIST. Characteristic for protons, the highest energy deposit is found for a relatively low kinetic energy of 0.1 MeV. The created electrons have a certain range: i.e. the energy deposit is not equal to the energy loss of the primary particle, which is why the peak in energy deposit (see Fig. 2.2) follows after a penetration depth of few centimetres. The number of photons decreases over distance, resulting in lesser energy deposit over the penetration length. Electrons Electrons possessing an energy in the order of a few tens of MeV lose their energy primarily via bremsstrahlung and ionization [7]. Bremsstrahlung is the radiation that is produced when a charged particle is deflected by the electric field generated by a nucleus of the absorbing material. The charged particle loses some kinetic energy in this case, which is converted into a photon. Additionally, since electrons have mass, energy can be lost via elastic collisions. A large fraction of the electron kinetic energy can be lost via a single collision, as the incoming electron mass is the same as that of the orbital ones. The peak of energy deposit in Fig. 2.2 is relatively broader than in the case of incoming photons, since the path range of electrons in the material can have large deviations [8]. Protons The fractional loss of the proton energy in elastic collisions is significantly lower than the one of electrons, as a proton is much heavier. This means that more interactions are required to make an incoming proton come to a full stop. The interaction of protons with material can be accurately described by the Bethe formula (Eq. 2.1) [7], which relates the stopping power to the kinetic energy of the incoming particles. The variables of this equation are defined in Tab de dx = Kz 2 Z 1 A β 2 [ 1 2 ln 2m ec 2 β 2 γ 2 W max I 2 β 2 δ(βγ) ] 2 (2.1) The information from the Bethe formula has been verified experimentally by stopping power experiments. These results have been collected by National Institue of Standards and Technology (NIST) [9] and are shown in Fig From this figure it can be found that protons deposit the largest fraction of their energy at a relatively low kinetic energy. The peak that is evident in Fig. 2.2 due to this behaviour is called the Bragg Peak. 4

11 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE Table 2.1 Variables of the Bethe formula. Symbol Definition Value or unit K 4πN A rem 2 e c MeV mol 1 cm 2 z Charge number of incident particle e Z Atomic number of absorber material A Atomic mass of absorber material g mol 1 β v/c γ Lorentz factor m e c 2 Electron mass MeV W max Maximum transferred energy MeV I Mean exitation energy ev δ(βγ) Density effect correction 2.2 Radiotherapy and imaging The majority of tumours is treated with some form of radiotherapy. One can separate between internal and external radiation treatments. Internal treatment is performed via small inserted sources of radioactive material (brachytherapy), while external treatment uses a beam of particles targeted at the tumour (teletherapy). Several types of external radiation can be classified [10]: Photons are most commonly used for treatment. Electrons are accelerated in a linear accelerator with an energy range from 4 MeV up to around 25 MeV, after which they are targeted onto a high-density material. The interaction with this material creates the photons that travel onto the patient. Photons are relatively easy generated up to high energies, which makes this method widely available. The main drawback of MV-photons comes from the fact that the dose in the tissue cannot be localised accurately. The peak of the energy deposit as a function of distance in the body is very broad (Fig. 2.2), which means that healthy tissue can receive an undesired high radiation. As mentioned earlier, electrons have a relatively small range in the targeted material. Typical energies of tens of MeV are used after the acceleration stage, which translates to a range in water of up to 5 cm. As electrons can therefore not penetrate the body, they cover a specific niche of radiation treatment in the treatment of skin cancer [11]. Although all hadronic particles can be used for radiation purposes, protons are most common. They are accelerated in a either a cyclotron or synchrotron up to 250 MeV. Proton therapy can be used for almost all types of tumours, but because of the several body movements it is currently only issued for those which can be fixated with high precision. The biggest advantage of proton therapy comes from the existence of the Bragg peak, since the energy deposit is sharply peaked at a certain distance after the traversed tissue. If several beam energies are used, the entire tumour area can be covered. The tumour receives the target dose in this case, whilst the tissue behind it is spared. As there will however be more transversal scattering, the tissue next to the tumour will receive more dose (see Fig. 2.4). 5

12 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE Figure 2.4 Dose comparsion between X-ray and proton beam irradiation. It can be found that the damage to the spinal cord can be significantly reduced in the case of proton usage. Retrieved from [12]. There exist several advanced radiation techniques to support radiotherapy. These have the aim of maximizing the dose on the tumour and minimizing the radiation of healthy tissue. For instance, 3D Conformal Radiation Therapy (3D-CRT) and Intensity Modulated Radiation Therapy (IMRT) can tune the particle beam such that the specific tumour shape is targeted [13]. To generate an irradiation treatment plan, it is required to know the path to the tumour with high precision. The imaging techniques that are most commonly used for this purpose are the CT-, MRI- and PET-scan: Computed Tomography (CT) [14] scans are composed of individual X-ray shots made under numerous angles, so that they can be combined into a single 3D representation of the imaged object. This type of scan has a good visualizing capacity of the patients anatomy. Magnetic Resonance Imaging (MRI) [15] uses a different approach to the imaging of the patient. The nuclear magnetic moment of hydrogen atoms in the body is aligned by the application of a strong magnetic field. Short bursts of X-rays are directed onto the patient, which make the hydrogen atoms flip in spin. The protons realign after the pulses are terminated, producing photons as they flip back. These photons are detected, which makes MRI an excellent way of measuring the density of water in the body. Positron Emission Tomography (PET) [16] works through the admission of a radioactive isotope to the patient. A positron and neutrino will be produced when nucleus of said isotope decays. The positron annihilates with electrons of the tissue, generating two photons with the same energy and opposite momentum. Said photons can be detected and from their difference in arrival time, the initial position of the positron can be reconstructed. Opposed to the body-structure resolving CT- and MRI-scan, the PET-scan primarily looks at the activity of the tissue (with a poor position resolution). In modern medicine it is customary to use a combination of above scans to retrieve the best possible image of the patient and tumour location. 6

13 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE 2.3 Proton radiography The Bragg peak of protons gives an excellent opportunity for cancer treatment. However, as the path to the tumour is usually measured with a CT-scan (using photons), a calibration between the interaction strength of protons and photons needs to be made. The characteristic value of proton interaction - the proton stopping power (in MeV cm 2 g 1 ) - is related (see Eq. 2.1) with absorbing material density via: de dx Z A (2.2) In contrast, the photon interaction - the mass attenuation coefficient (in cm 2 g 1 ) - is related by [17]: µ Z ( K KN + Z 1.86 K SCA + Z 3.62 K PH) (2.3) A with K KN the cross-section for free electrons, K SCA the cross-section for coherent scattering and K PH the cross-section for the photoelectric effect. A comparison between Eq. 2.2 and 2.3 shows that the underlying physical mechanisms of both particles are fundamentally different and a calibration will not be straightforward. Therefore, error margins are introduced on the location of the targeted material in the translation between both. These margins reduce the benefit of proton therapy, as more healthy tissue will be radiated. One method to solve this problem would be the usage of protons as imaging particles, introducing the field of proton radiography [18]. A proton beam with sufficient energy can penetrate the body of the patient. As the particles energy loss in the soft tissue depends on the type of material traversed, it is possible to construct a radiograph in this way. In contrast to a CT-scan, where photons only scatter through the Coulomb force, the effect of multiple scattering of protons should be considered. The projection of the angular distribution for the center 98% can be approximated as a Gaussian function, with the RMS given by [19]: θ 0 = 13.6MeV z x/x 0 [ ln (x/x 0 )] (2.4) βcp with p the momentum, βc the velocity, z the charge number of the incident particle and x/x 0 the thickness of the scattering medium. From Eq. 2.4 it can be found that highly energetic protons will exit the patient under a significant angle (θ 0 = 34 mrad for a 200 MeV proton in 10 cm of water). As the particles energy loss depends on the traversed materials, this requires each proton to be tracked individually. Using iterative software algorithms, the most likely path in the patient can be approximated. The requirement of measuring the particles scattering angle and energy loss lead to a distinct detector design. The energy of the particles in the beam can be assumed to be constant, so that the particles energy only needs to be measured after the patient. The general design used in proton radiography prototypes is shown in Fig

14 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE 2.4 Challenges and aims As proton therapy is being introduced in The Netherlands [20], the amount of effort put into proton radiography is steadily increasing. This section lists the requirements necessary for proton radiography detectors, so that it will become clear where the current limitations lie and where more progress needs to be made. Before proton radiography devices can be commercially produced and used in the hospital, attention to the following points must be given: It should initially be possible to construct radiograph of a typical organ, of which the dimensions are approximately 10 x 20 cm 2. In some cases, it is possible to construct a larger area using multiple sub-detector as a larger detector. This will normally induce some dead area in the image, which should be avoided as critical information can be easily missed. It can additionally be taught of to combine several radiographs in order to create a larger image. The disadvantage of this method is that a patient will move in between these measurements, reducing spatial resolution as a result. The spatial resolution is physically limited by the multiple Coulomb scattering in the patient (see Eq. 2.4), which can be estimated to be in the order of 1 mm when advanced reconstruction algorithms are used [18]. This value is in the same order of magnitude of the margin in which a patient can be fixated during irradiation treatment [21]. A proton radiography detector should therefore be able to measure particles at this level. The density differences between types of soft tissues in the human body are in the order of a percent [22]. As the proton source should be stable within 0.1% of its energy to allow for proper accuracy [21], this thus will be not limiting for the detector. The differences in density are directly related to stopping power (and thus residual energy), so a proton radiography detector should similarly be able to separate energies with a resolution less than 1%. As a patient has several movements during a radiograph (e.g. breathing, swallowing and heartbeat), the time that it takes to make a proton radiograph should be in the order of a second. An image of 10 x 10 cm 2 should be constructable with 10 6 proton tracks [23]. A quick calculation learns that this requires the devices to run at a rate at least 1 MHz, assuming individual proton events. For eventual 3D reconstruction, a data acquisition rate of 10 MHz is preferred [24]. Proton radiography can be used as a quality control tool in proton therapy: a scan just before irradiation can provide the latest information on the tumour location. Results should therefore be quickly available. A maximum processing time of around 15 minutes can be set, as this is approximately the time that it takes to remove the radiography detector from the beam and everything is set up for irradiation. There are other detector features that are not essential, but generally preferred. They include the ability to track particles in 3D, in order to get more precise information on the angles of the proton tracks; it would be good if multiple particles can be measured at once, so that a larger read-out rate can be achieved; the detector should be as radiation hard as possible; and not sensitive to temperature or humidity changes. 8

15 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE Figure 2.5 A simplified schematic overview of a proton radiograpy detector. The proton beam enters the first tracker, traverses the patient, goes through the second tracker and comes to a full stop inside the calorimeter, respectively. 2.5 State-of-the-art prototypes The devices currently used most in proton radiography for tracking purposes are scintilating fibres and silicon strip detectors. Whilst their method of detecting particles is completely different, their performance is more or less comparable. Both of these methods require two orthogonal planes of detectors, as they are only able to measure in one direction. Scintillating fibres consist of plastic scintillating material. High event rates can be achieved with these wires, because of their relative short decay time (in the order of a few nanoseconds). The fibres can constructed with a big length, so that the sensitive area can easily achieve the requirements set above. The major disadvantage comes from the fact that only one particle can be constructed per frame, as there is no way to distinguish two coinciding events. The data acquisition rate of the device is therefore physically limited by the plastic scintillator s decay time. The collaboration at the Paul Scherrer Institute were in 1999 the first to use scintillating fibre hodoscopes for their position measurements. The Istituto Nazionale di Fiscia Nucleare (INFN) in Italy [25] and the collaboration between Northern Illinois University (NIU) and Fermilab National Accelerator Laboratory (FNAL) followed this method in 2014 [26]. Another popular choice for tracking devices are the Silicon Strip Detectors (SSD). These configurations use doped silicon as their detecting material. The sensitive area of such detectors is currently limited by the wafer size used in industry, which has a maximum of approximately 10 x 10 cm 2. It is, however, relatively easy to construct a modular design, which covers a larger area. Flux up to a few MHz per cm 2 will not be limited. Several collaborations have used SSD s for their tracking system. Recently, the collaboration between Loma Linda University (LLU), University of California Santa Cruz (UCSC) and Northern Illinois University (NIU) have incorporated this into their system [27], with an upgrade built some years later by LLU, UCSC and California State University (CSUSB) [28]. Later, the Niigate University in Japan [29] and the PRIMA group in Italy [30] have also shown the promising features of SSD s. The PRaVDA consortium uses multiple SSD planes as one tracking device [31], which makes it possible to resolve more protons at the same time. This does, however, increase the material budget, resulting in larger beam interference. 9

16 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE As for the measurement of the energy, two distinct ways to measure exist. One is the measurement of a particle s energy deposit inside detecting material, usually done using calorimeters; the other is to record how far a particle travels through the detecting material until it comes to a full stop, as done by range telescopes. In some cases it is possible to combine these signals, resulting in a hybrid detector. The kinetic energy of a particle can be recorded by getting it to a complete stop inside a detecting material and simultaneously measuring how much signal is generated. The magnitude of this signal corresponds to particle s kinetic energy, as shown by the Bethe formula. These residual energy measurements are traditionally done with an organic calorimeter, consisting of a crystal connected to a photomultiplier-tube. The maximum data acquisition rate of these calorimeters is limited to a few tens of khz, as their decay time is rather long. The LLU/UCSC/NIU collaboration has used CsI-crystals in their first design [27], the Niigate University uses a calorimeter of NaI [29] and the PRIMA-I collaboration used a YAG:Ce crystal [30], measuring at a rate of 10 khz. The overall rate of these detectors can be compensated using an array of segmented crystals, increasing the maximum readout to 1 MHz in the PRIMA-II upgrade [32]. A typical range telescope consists of multiple layers of sub-detectors, positioned behind one each other. When a particle traverses such a sub-detector, a signal will be generated. From the collection of signals it can be determined what the range of the incoming particle in the detector was and the kinetic energy is reconstructed, as the relation between both is known to high precision. Range telescopes can use a variety of materials, although plastic scintillators are the most common because of their short decay time. Several collaborations, such as PSI (64 layers of 3mm) [33] and NIU/FNAL (96 layers of 3.2mm) [26], have used such scintillators in their system. The INFN has used 60 layers of scintillating fibres [25] and the PRaVDA consortium is testing the use of CMOS APS chips as a measurement device [31]. These chips are pixalated, which makes it possible to resolve multiple protons at the same time. All of these detectors have the capability of measuring at a rate of MHz, as required by proton radiography. A hybrid detector can measure the deposited energy of a particle in a detecting volume, without stopping it. By positioning several of these sub-detectors behind one each other, the range of the particle can be measured in addition to its kinetic energy. These signals are then combined, reducing the errors on the kinetic energy calculation. Hybrid detectors have not been used frequently up to now. Only the LLU/UCSC/CSUSB collaboration has used a stack of five plastic scintillators in a full prototype for their measurement [28]. From the collection of above designs it is clear that there is not much room for improvement in the current generation of prototypes, as multiple sub-detectors are necessary to achieve the required performance. This work will investigate the operation of two new techniques in regards to proton radiography, which are fundamentally different in operation: a Time Projection Chamber for tracking purposes, which was developed in previous studies at Nikhef, Amsterdam; and hybrid silicon sensors for the combination of tracking and energy measurement. 10

17 CHAPTER 2. PARTICLE PHYSICS IN MEDICINE The Time Projection Chamber investigated in this study has numerous benefits over other tracking devices. For instance, the TPC is able to track multiple particles at once, making sure that the system is not initially limited in readout rate. Additionally, it is possible to perform 3D-reconstruction, from which more information on the incoming angle of the particle can be retrieved than in the case of two points in tracking planes. Another major advantage comes from the fact that the TPC is radiation hard, as there is a constant flow of ionizing gas. This makes sure that the detector will be stable over several years, necessary in a clinical environment. Most importantly, the interaction with the beam is significantly smaller in comparison with other detection methods. This will ultimately result in a better image resolution. The stack of hybrid silicon sensors is beneficial as they possess a high granularity. Similar to the TPC, this will allow for the processing of multiple particle as once, so that a higher particle flux can be resolved. The combination of energy and range information should provide a better energy resolution as two parameters are probed simultaneously. Additionally, the position of entry on the chip can be used as a method of tracking. This makes a dedicated tracker behind the patient redundant, which is beneficial in terms of detector cost and size. 11

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19 3 Detector overview Previous chapter has motived the choice for a system based on a TPC as tracker and hybrid silicon sensor as deposited energy measurement device. This chapter will describe the individual components of the two investigated prototypes - ProPix I and II - in more detail. Additionally, some attention will be given to the gas dynamics inside the TPC. To get an idea where the individual components are located, the schematics of ProPix I and II are shown in Fig. 3.1 and 3.2, respectively. Figure 3.1 ProPix I consists of a scintillator, two TPCs and a calorimeter. The scintillator is used as a timing reference when a proton enters the detector. The two TPCs are used for tracking purposes: one in front of the phantom, one behind. The calorimeter consists of a BaF 2 crystal connected to a photo-multiplier tube. Figure 3.2 ProPix II consists of a TPC and two hybrid silicon sensors. As before, the TPC provides the position measurement before the phantom. The silicon sensors simultaneously measure energy and position. A reference signal for the detector is not required in this case, as this can be provided by one of the silicon sensors. 3.1 Time Projection Chamber A Time Projection Chamber (TPC) (see Fig. 3.3) consists of a volume with a certain gasmixture enclosed. When a charged particle travels through this chamber, ionisation of gas atoms can occur. The incoming particle loses a fraction of its kinetic energy, in turn liberating an electron from the gas molecule. The probability in which this occurs depends on the atoms electron configuration and the energy of the incoming particle. The freed electrons are detected individually to allow track reconstruction. First, a connection between a cathode at the top of the chamber and an anode at the bottom is made. The cathode is put at a relatively high voltage (in the order of several hundreds of V/cm), so that its electric field drifts the created electrons downwards. A high drift velocity is beneficial in terms of data-acquisition rate, a low drift velocity in terms of z-resolution. 13

20 CHAPTER 3. DETECTOR OVERVIEW Figure 3.3 A schematic overview of the processes occurring in the Time Projection Chamber, with a 1 kv/cm drift field, 1280 V GEM voltage and 2 kv/cm transfer field. A particle enters from the left and ionizes the gas. The freed electrons drift downwards by the electric field applied by the cathode on top. The electric field is kept homogeneous by the copper strips, decreasing in voltage every step. An electron cloud is generated by the passage of the several GEMs. Finally, the signal is collected at the chip. Assuming that the electric field is properly configured, the x/y-position at arrival at the anode plane should be representable of the initial electron position. The z-direction can be calculated by measuring the time for which it takes the electron to move downwards, as the drift velocity in the gas is known. This measurement requires a reference time, which can be provided by e.g. a scintillator in the plane of the particle. From the collection of measured electrons, a track can be constructed. More specifically, the incoming position of the particle with its corresponding entry angles can be found. To make sure that the correct origin of the created electrons is retrieved, the electric field needs to be as homogeneous as possible. This is assured by a field cage enclosing the TPC, so that the largest distortions in the field are cancelled. The field cage consists of a 50 µm thick capton foil, with 0.5 mm of copper strips every mm. These strips are connected by a chain of 10 MΩ resistors, dividing the voltage linearly over the active volume. The electrons diffuse in both longitudinal and transverse direction due to interactions with the gas. The diffusion is thus dependant on the mixture of the gas and on the voltage of the cathode; both need to be carefully tuned as a low diffusion is required for a proper functioning tracker. As the diffusion can be expected to be of Gaussian shape, the mean deviation in both the x/y as the y/z-plane can be written as [34]: with L the traversed distance and D i the diffusion coefficient. σ i = D i L (3.1) 14

21 CHAPTER 3. DETECTOR OVERVIEW Figure 3.4 The geometry of a single GEM foil. Holes of 70 µm in diameter are drilled every 140 µm. Secondary electrons are generated in the holes in the copper-capton-copper foil. Drawing adapted from [35]. Figure 3.5 An electron avalanche is created after a single incoming electron passes a stack of three GEM foils. The voltages are tuned through a resistor circuit. Drawing adapted from [36]. 3.2 Gas Electron Multiplier Only a few tens of electrons are generated per passing particle in the TPC. These electrons do not induce enough current in a measurement device to create a signal. The current therefore needs to be strengthened in some way: Gas Electron Multiplier (GEM) foils [37], produced at CERN, Geneva, are used for this purpose. These foils can be put over a bare chip, without any need for post-processing. The schematic of a GEM foil is shown in Fig The GEM used in this study is made of 50 µm capton foil, with a 5 µm copper foil enclosing it on both sides. Holes with an inner diameter of 50 µm and an outer diameter of 70 µm are drilled in the foil every 140 µm over the entire surface. The canonical shape of the holes is therefore due to the manufacturing process. Even rows have an offset in relation to the odd rows, so that more area is covered. A relatively high voltage of 8-10 kv/mm is applied between the copper layers. These layers act as a parallel plate capacitor, as they are not physically connected. When an electron passes through a hole, the gas will be ionized further so that secondary electrons are produced. The number of electrons produced (or gain) depends on the strength of the electric field. The Townsend coefficient can be used to classify the gain: if a value 1 is achieved, multiplication will occur. A high electric field increases the probability of discharges, so the involved voltages should be carefully tuned. To achieve a higher gain, it is possible to stack multiple GEM foils, as is shown in Fig The secondary electrons from the first foil will drift towards the second GEM foil, where another multiplication step occurs. Finally, when the third GEM foil is passed, the signal is amplified so much (up to a million electrons) that the induced charge can be detected in the readout unit. A dedicated control system has been designed to tune the voltage on each GEM. This additionally makes sure that the best values for the transfer field can be chosen, so that diffusion is reduced to a minimum. The volume between the lowest field cage strip and the upper GEM foil must be carefully monitored, as both are supplied by a different high-voltage generator. 15

22 CHAPTER 3. DETECTOR OVERVIEW Figure 3.6 The timing inside of the Timepix3. The TOT is determined from the number of times that the internal 40 MHz clock is above threshold level. The TOA is determined by the combination of the internal 40 MHz clock and the fast 640 MHz counter that starts running when the threshold is surpassed. Drawing adapted from [4]. Figure 3.7 The effect of timewalk in the Timepix3. A highly-energetic particle will build up charge faster in the pixel than a lower-energetic one, which leads to a different Time Of Arrival for both particles. Drawing adapted from [38]. 3.3 Timepix3 readout chip The Timepix3 [40] stems from the Medipix family, which has been developed at CERN, Geneva in collaboration with Nikhef, Amsterdam. The chip has 256 x 256 pixels with a size of 55 x 55 µm each. It is based on 130 nm CMOS technology and can be classified as a hybrid pixel detector. When the collected charge passes a threshold, which can be set for every pixel individually, a 640 MHz clock will start to run until a reference point of the 40 MHz internal clock is reached. The Time Of Arrival (TOA) is determined by the combination of these two clocks. Additionally, the Time Over Threshold (TOT) is recorded as a measurement of the total collected charge. For a summary of this process, see Fig The chip can be read out in tree distinct readout modes: collecting TOA and TOT (done in this work), collecting only TOA, or collecting only the number of counts. The read-out can be either frame based or data driven and there exists a zero suppressed readout. The induced current on the chip has a limited rise time. For large deposits, the rise is faster than for low ones (see Fig. 3.7). As the threshold is a fixed value, the Time Of Arrival will be different for the particles. This effect is called timewalk. The relation between TOT and TOA can be measured in order to quantify this effect and make corrections. A dedicated chip-board for the Timepix3 chips has been designed. Both quad-boards (in a 2x2 array of chips) and single-boards are available. The readout is performed by the SPIDR unit [41], which is able to send data at a frequency of 80 MHz per chip. A reference clock and independent trigger can be used as input for the SPIDR. Instead of gas, it is possible to connect a silicon sensor to the Timepix3. These detectors will be referred to as hybrid pixel sensors. The configuration of a single pixel is drawn in Fig When a charged particle traverses the silicon layer, many electron-hole pairs are created. Depending on the applied bias voltage, either holes or electrons drift towards the implants. A sufficient current is generated without any need for further amplification, in contrast to the TPC. 16

23 CHAPTER 3. DETECTOR OVERVIEW Figure 3.8 Schematic overview of the combination of a silicon active layer, bump-bonded onto the Timepix3 ASIC. Retrieved from [39]. Figure 3.9 Schematic overview of the trigger logic in ProPix I. Based on information from [4]. 3.4 Calorimeter and trigger The calorimeter used in ProPix I consists of a BaF 2 crystal [42] combined with a photomultipliertube. Electrons in the crystal get exited when a charged particle enters, releasing photons as they fall back to their ground state. The total number of photons created is proportional to the initial kinetic energy of the incoming particle. The scintillation light of the BaF 2 crystal consists of two components: one with a decay time of 600 ps, used for trigger purposes, and the other of 630 ns, used for the measurement of the energy. The digitization of these analogue signals is done with a CAEN Digitizer [43]. The entire configuration can measure at a data-acquisition rate of approximately 10 khz. A trigger configuration has been made in order to synchronize the multiple sub-systems in ProPix I, which can be found in Fig The signals from both scintillator and calorimeter pass through a discriminator in order to create a digital signal. When both coincidence, a timer is started. This timer shuts the data-acquisition system for the time that it takes all generated electrons in the TPC to drift downwards, approximately 100 µs. Afterwards, the read-out timer is started to make sure the data-acquisition system is not overloaded with events. When all data is written, it is reset. 17

24

25 4 ProPix I prototype This work continues upon the results achieved by [4], in which a set-up as is shown in Fig. 3.1 has been used. A careful examination of former achieved test beam data - collected at KVI, Groningen [44] in is made in this chapter. It will be shown that several irregularities are present in the data, for which, wherever possible, solutions are proposed. A complete data analysis is presented afterwards. 4.1 Large clustersize One of the found deviations relates to the size of the measured signals. It is observed that clusters of up to ten pixels in width appear in the tracks, as can be seen in a typical proton event in Fig This effect would result a limited spatial resolution, as the clustersize is not constant and edge effects occur; and a lower data acquisition rate, as the information for more pixels needs to be transmitted. Figure 4.1 A typical proton event registered in the TPC in front of the phantom. The large signal size could arise from the diffusion of the electrons in the gas, as a higher diffusion results in more spread of charge. To quantify this effect, a simulation in Garfield++ [45] and Magboltz [46] of several gasses is made. The selection of gasses chosen for the simulation is made based on the availability and frequency of usage in detector experiments [47]: DME:CO 2 (50:50) Ar:CF 4 :CO 2 (45:40:15) Ar:CO 2 (90:10) Ar:CF 4 :ic 4 H 1 0 (95:3:2) He:iC 4 H 10 (80:20) Ar:iC 4 H 1 0 (90:10) He:CO 2 (90:10) Ar:CF 4 :CH 4 (75:20:5) 19

26 CHAPTER 4. PROPIX I PROTOTYPE Figure 4.2 A schematic overview of the diffusion of charge between the GEMs and the chip. In darkred, the behaviour that is desired. In light-red, the behaviour that is most probably due to diffusion of the electrons. Figure 4.3 The simulated gas flow inside the TPC. The background color is a measure of the absolute velocity at this point, the arrows represent the direction of the flow. A counter-clockwise flow is observed in the drift volume. The drift velocity, transversal and longitudinal diffusion are calculated and shown in Fig. 4.3a, 4.3b and 4.3c, respectively. During the proton test beam, the gas-mixture Ar:CF 4 :CO 2 (45:40:15) was used at a transfer voltage of 3 kv/cm. This mixture was chosen because of the recommendation of the GEMPIX-collaboration [48]. From the simulation it can be found that a transversal drift is D T 100 µm/ cm is found in this case. Previously, the relation between the diffusion coefficient and the mean displacement was given (Eq. 3.1). For the distance between the second and third GEM (L = 2 mm), this gives: σ T = 44.7 µm. As the distance between two holes in the GEM is only 70 µm, it is evident that electrons can travel to other holes after a width of only 1.5σ. The resulting avalanches will therefore spread the charge over multiple pixels (see Fig. 4.2). This behaviour is confirmed by simulation studies [49]. The only simulated gas that has a lower diffusion than the previous mentioned gas, is the DME:CO 2 (50:50) mixture. This gas needs to be operated at a higher cathode electric field in order to retrieve the same gain. As the difference in diffusion over a few millimetre is very minimal, it is however not expected that the clustersize would be significantly improved by a change of gas. When the entire drift distance of the chamber is considered (500 mm) however, an improvement can be made. It is proposed to use a 1 kv/cm field with DME:CO 2 (50:50), instead of the 300 V/cm field with Ar:CF 4 :CO 2 (45:40:15) in order to reduce the amount of cluster diffusion. The results of this change will be discussed later. Concluding, this study shows that it is possible for diffusion to be the origin of the large signal size. A change of gas would not resolve this problem. One other possibility is that the gain of the GEM foils is set too high, so that more electrons are produced than necessary for detection. It will be later investigated which settings of the voltages on the GEM foils result in the best performance. 20

27 CHAPTER 4. PROPIX I PROTOTYPE a Drift velocity. b Transverse diffusion. c Longitudinal diffusion. Figure 4.4 Simulated properties of a selection of gasses. Marked are 300 V/cm (the used drift field value in ProPix I) and 1000 V/cm (the proposed drift field value for ProPix II). 21

28 CHAPTER 4. PROPIX I PROTOTYPE a Row hit-distribution. b Column hit-distribution. Figure 4.5 The hit distributions for both the column as the row for the TPC behind the phantom. 4.2 Gas flow in chamber Data analysis reveals that the distribution of hits is not uniformly spread over the area of the chip, as can be seen in the column- and row distributions in Fig More hits are present towards the edges of the chamber. The flow of ionising gas inside the TPC could influence the processes that occur inside the TPC, as the gas density is related with the number of detected electrons by the amount of electron capture and number of primary ionizations. To study the flow rate inside the chamber, the active gas volume is modelled and a twodimensional fluid flow simulation is made in OpenFOAM [50]. The results are visualised with the ParaFOAM plug-in, which is based on ParaView [51]. As the changes in temperature and pressure are expected to be small over time, the gas will be modelled as an incompressible laminar fluid. A flow of gas is launched with an arbitrary velocity from the inlet at the bottom of the chamber, and collected from the outlet on the top. For simplicity, the GEM foils are simulated as being an open volume in which the gas can freely travel. Additionally, the diffusion from the sides of the chamber is neglected, as there is no proper way to quantify this effect. Iterations are performed up until a steady-state solution is found. The results of above simulation are shown in Fig The arrows represent the direction of the flow, with their size and the background colour visualising the magnitude. It becomes clear that a relative anti-clockwise flow exists in the chamber, with the majority of the stream being collected at the outlet. One interesting aspect comes from the lack of flow in the center of the TPC. If air from outside is trapped inside this area, less ionised electrons will reach the bottom due to electron capture. This means that there will be a relative stronger signal towards the edges of the collecting chips. This behaviour is in agreement with the hit distributions as found in Fig It is, however, unlikely that the gas flow can have such major impact on the hit-distribution, as processes such as turbulence will eventually distort the steady state. Other explanations have been found for the unusual shape of the signal in the hit-distribution, which will be discussed below. Still, the relation between the shape of the signal and the elapsed time will be studied later to make sure no unwanted effects arise. 22

29 CHAPTER 4. PROPIX I PROTOTYPE a The TPC in front of the phantom. b The TPC behind the phantom. Figure 4.6 Protons are entering the TPC from the top, for which only tracks of specific entry regions are drawn. This results in the average path the protons travelled in the TPC, which gives a good indication on the positions where curvature occurs. 4.3 Electric field distortions Tracks in the chambers are found to posses a non-negligible curvature in both the x/y as the z/y-plane. These electric field distortions would result in a reduced spatial resolution, as the position information from the tracks is less precise. Both large scale (evident in Fig. 4.6), as smaller scale (in the order of a few pixels) effects are present. Explanations for the several phenomena are presented below. S-like curvature x/y-plane In the TPC behind the phantom (Fig. 4.6b) the tracks are found to have a S-like shape. Similar behaviour is found in the TPC in front of the phantom, but in this case to a lesser extent. It is likely that these problems arise due to a discrepancy in the homogeneity of the electric field inside the chamber. As was stated before, the hit-distributions (Fig. 4.5) show an increase in the number of hits towards the edges of the chamber. The hit-distribution of the column shows other effects as well, which will be discussed later. The increase of the numbers of hits towards the edges could indicate that an electric field is present in all edges, pointing towards the center of the chamber. One possible explanation for these effects comes from the existence of a strip of insulating material inside the chamber. It could be that this strip charges up during the measurements, as the electrons have no way to escape the material. This electric field will drift the electrons towards the center of the chamber: exactly what is found in the distribution of hits. Another option would be that the voltage on the field cage is not properly configured. If this voltage is set too high, electrons could similarly drift from the side of the chamber inwards. 23

30 CHAPTER 4. PROPIX I PROTOTYPE a Potential map. b Drift lines. Figure 4.7 The simulated properties of the electric field in the case of an incorrectly set potential on the field cage. Electrons curve from the edges of the TPC inwards. An electric field simulation is performed to investigate what is happening inside the TPC in above stated situations. First, a geometry is specified in Gmsh [52]. The cathode is introduced at the top of the chamber, downwards are 50 strips of copper in the field cage and at the bottom the insulation layer and anode are positioned. The electric field is solved using the finite element electrostatic solver of Elmer [53], which requires all physical surfaces to be set at a user-defined voltage. Finally, the potential map is imported into Garfield++ for visualisation of the field and calculation of drift lines. As the relative permittivity of Kapton is close to that of air (with respect to copper), it is chosen to omit these surfaces from the simulation for simplicity. The voltage on the conducting surfaces should decrease linearly with the distance from the cathode to get homogeneity in the electric field. The first situation studied is the configuration in which this is not the case. The cathode is set at a voltage of -104V, the anode is at 0V, the surface above the anode at -2V and the potentials on the copper wires go from -100V to -10V, linearly. This is an incorrect situation as the voltage on the lowest wire should be set at -4V. The plane of this simulation at y = 0 is shown in Fig. 4.7, where both the potential and drift lines are drawn. In the potential map it can be found that the equipotential lines are curved upwards, with the largest curvature at the bottom of the chamber. This behaviour is confirmed by the drift lines: those on the edges of the chamber drift inwards, thus creating a higher electron density on the edges of the chip. The field cage potentials are set correctly (-100V to -4V) for the second simulation. This time, however, the strips on the side of the anode are set at an incorrect arbitrary voltage of -10V. This voltage could arise when electrons drift onto the surface and cannot escape. Again, the plane of y = 0 is shown for this configuration in Fig The potential map shows, similar to the previous case, upwards curvature on the bottom of the chip. Most drift lines curve inwards, but the ones of the far edges curve outwards. It not clear whether this last effect is present in the considered TPC, as it is not possible to measure over the entire surface. 24

31 CHAPTER 4. PROPIX I PROTOTYPE a Potential map. b Drift lines. Figure 4.8 The simulated properties of the electric field in the case of an charging insulating surface. Most electrons move from the edges of the TPC inwards, but a significant fraction curves towards the field cage. If there exists a relative electric field pointing towards the center of the chamber, the electrons generated by the proton track drift towards the center. As the proton enters the TPC under a small angle, the S-shape in the x/y-plane is explained. This process is visualised schematically in Fig To conclude, the major S-shaped curvature in the x/y-plane of the TPC is most likely due to a combination of insulating material in the field cage and not correctly set field cage voltages. The first effect can be resolved when a proper ground around the GEM foils is introduced. The second by careful tuning of the voltages involved in the field cage. Focal point x/y-plane In the chamber in front of the TPC (Fig. 4.6b) the tracks in the bottom-left of the chamber deviate towards a focal point. If the geometry of the chamber is compared with the position of this focal point, it is concluded that it is most likely that this effect is caused by the high voltage supply towards the GEM. This supply cable is not properly shielded, which makes it possible for electrons to drift towards this copper strip for them to be collected. This problem can be easily resolved when some electrical tape is placed over the affected cable. U-like curvature z/y-plane The tracks of the protons show an U-like shaped distortion in the z/y-plane. This means that the reconstructed tracks curve slightly upwards towards the edges of the board. This effect occurs when the Time Over Threshold value is not properly defined for the edges of the chips. It can happen that only a fraction of a signal is detected, so that the average TOT is significantly lower than in the center of the chip. As the timewalk-correction relates the values of the TOT with the TOA, this will result in incorrect TOA values. 25

32 CHAPTER 4. PROPIX I PROTOTYPE Figure 4.9 Explanation of the S-like curvature x/y-plane. As the proton enters the TPC, electrons are generated over the entire surface. However, as there is a relative electric field pointing inwards, the electrons towards the edges of the chamber are pushed inwards, resulting in the S-shaped track. This effect would be hard to get rid of physically, as ideally the clustersize should be reduced. The best way to deal with it is to define an active area of the chip that is slightly smaller than the physical size. As the clusters have been observed to be up to ten pixels in width, this will be a starting point for the decrease in active chip area from the edges. Small deviations z/y-plane Additionally to the U-shaped distortion in the z/y-plane, smaller deviations of the tracks over the region of the chips are found. Again, this is a problem related to the timewalk-correction. The first generation of Timepix3 chips had a defect, which caused the threshold level not to be constant over the entire surface of the chip [38]. More specifically, it was found that there is a dependence on the column number. This results in a deviation in TOT values, and thus, by the timewalk correction, in TOA deviations as a function of chip column. The newest generation of Timepix3 chips have had this defect in the chip-design resolved, but these chips are unavailable for this research. It is therefore not possible to resolve this effect physically. An adjustment in the data-processing software will have to be made, in order to correct for this effect. Small deviations x/y-plane Similar to the deviations in the z/y-plane, deviations of small size are found in the x/y-plane. As the average threshold is not constant over the columns (due to the error in chip design), the resulting number of hits will be different as well. This behaviour can be found in the distribution of hits as a function of column number (Fig. 4.5) as two large maxima in addition to the bias towards the edges. If some regions produce more hits than others, the fitted track will be biased towards the high hit-density region. This effect is then observed as small deviations close to the high hit-density regions. Above effect will be resolved in the analysis by grouping neighbouring hits together in clusters with a single center of gravity point. As not all individual hits are used in this case, the response of the chip will be equal over the entire surface and the discussed effect should disappear. 26

33 CHAPTER 4. PROPIX I PROTOTYPE a Raw hits. b Clustered hits. c Center of gravity. Figure 4.10 The different steps taken in the clustering algorithm. All hits that are neighbouring each other (a) are grouped into clusters (b), with an associated cluster ID. The center of gravity is calculated for all clusters (c), where the TOT is associated with the highest TOT of the cluster. 4.4 Data preparation Several algorithms are developed in order to try to get the best result in the reconstruction as possible. First, it will be briefly discussed how data comes out of the SPIDR data-acquisition system. Three distinct packets are analysed by the software: External time packets are generated by the clock inside the SPIDR and can be seen as the heartbeat of the system. Every second, the time information of this clock is pulsed as a reference, so that large acquisition times become possible. Pixel packets contain the information for the hits on the chips. These consist, among other things, of the TOA and TOT of said hit. External time packets are combined with this information for the complete timestamp, as the internal clocks have a finite sampling time range. Trigger packets are generated by the external trigger coupled to the SPIDR. Their time information is determined in the SPIDR, which is done via the same clock as is used in the external time. It is thus evident that the pixel and trigger packets rely on the external time packets for the correct timing. The data analysis however shows that some packages coming from the SPIDR data-acquisition carry corrupt information. This means that a package can be classified as being one of the packets described above, but its actual contents did not match. Wrong timing is consequently read in, resulting in incorrect associations between trigger and hits. Furthermore, as this process is random among the different chips, the tracks from different chips cannot be associated any longer. Several corrections, such as an algorithm that checks the package contents before using them, are made so that these effects have as minimal influence as possible. 27

34 CHAPTER 4. PROPIX I PROTOTYPE a Tracks as they are recorded. b Tracks after the procedure. Figure 4.11 Overview of the straighting procedure on the tracks in the TPC before the phantom. Note that the tracks on the edges start to approach each other. One of the chips in the chamber before the phantom had incorrect settings applied to it, as its threshold-level was set too low. Concretely, this means that the chip had many more firing pixels compared the other chips in the configuration. Corrections are implemented in the entire software package, so that relative differences are used in the timing information instead of absolute ones. The most probable track is furthermore selected using the height of the TOT values, resulting in an improved signal-to-noise ratio and more usable data for this specific chip. The offset between trigger information and pixel hits is not of constant value, as the chips on the quad-board were not reset simultaneously during the test beam. This offset is random for every run and involved chip. When multiple runs are combined, this would result in an undesired offset in the z-direction of the tracks and hence this should be resolved. It is chosen to make the evaluation of the integral over the correlations between pixel and trigger information. The retrieved offset is the mean of the found distribution, which gives a stable results for all runs. As was noted before, the number of pixels hit by an electron in the TPC has a major fluctuation. It is not expected that the observed width of these clusters is correlated with the physical processes occurring inside the chamber, so it is decided to develop a clustering algorithm. This will make sure that all data-points are treated with the same weight and hence some of the effects described on the electric field distortions should disappear. A simple next-neighbour design is constructed (see Fig. 4.10): all hits that are neighbouring each other are grouped into clusters. This procedure fails when clusters are overlapping each other, since they will be recognised as one. It is however expected that the improvement is greater than the errors induced. As the deviations in shape, TOT values and size are too large to introduce pattern recognition, it is unlikely that more complex models will have an increased positive influence on the obtained results. 28

35 CHAPTER 4. PROPIX I PROTOTYPE a The complete phantom. b The measured area of the phantom. Figure 4.12 Overview of the irradiated phantom, with the measured region marked. See the text for the material compositions. Based on information from [5]. To correct for the curvature effects, an offset algorithm is created. For every hit, the normalized difference between its position and the average column number is calculated in both the x/yand x/z-plane. This offset is subtracted from the hits, so that a more straight field is found. No fitting algorithm is applied in this step as the shape of the distortions has previously been found to consist of several effects. The result of this procedure is found in Fig Note that only curvature is adjusted in the offset procedure, not the uniformity. Unfortunately, a correction for the uniformity is not possible to be made, as the distribution of hits on the columns is under influence of the threshold (Fig. 4.5b). These effects are most significant on the edges of the chamber, making it possible to have shaping effects on the borders of the radiograph. These edges are not analysed for this reason. A number of cuts are performed on the found tracks in order to improve the signal-to-noise ratio. All hits on the far edges of the chips are first removed, as they do not carry reliable data due to the width of the clusters. Hits with fewer than 5 TOT-counts are ignored, as they can be expected to be noise. The tracks inside the chambers are fitted with linear functions in both the x/y- as the x/z-plane. The reduced χ 2 is tracked in order to exclude odd events (delta rays, etc.) from the data. Using the information of both chambers, the most likely path of the proton is approximated using a simple track-fitting algorithm. Several other additions are made to retrieve the best data quality. It is made sure that data is not multiply assigned over events, so that no non-physical connections are made. An offset correction for the different SPIDR times is made, which is due to incorrect values in the trigger packets. New is the association between the chamber before and after the TPC, so that an investigation of multiple scattering becomes possible for the first time. Finally, all different processes are grouped in one single analysis tool, aiding the speed of the system as well as the ease of its sustain. 29

36 CHAPTER 4. PROPIX I PROTOTYPE a Deposited energy (arb. units). b Multiple scattering magnitude (arb. units). Figure 4.13 Phantom reconstruction using two different methods. In total 259,290 tracks are used to create both radiographs. 4.5 Phantom reconstruction Now that all major irregularities are resolved, an analysis of the proton test beam data at KVI in 2015 becomes possible. During this test beam, the phantom as in Fig was irradiated. This object consists of a 94 x 54 x 60 mm PMMA block (ρ = 1.19 g/cm 3 ) with several holes of tissue-like materials. The sensitive area of the TPCs, after all corrections are applied, has additionally been marked. The middle-left hole in the marked area (r = 10 mm) consist of 20 mm Gammex 453 (fat) (ρ = 0.92 g/cm 3 ), 30 mm air (ρ = g/cm 3 ) and 10 mm Gammex 457 (solid water) (ρ = g/cm 3 ). The bottom-right hole (r = 8 mm) is made of 60 mm Gammex 454 (breast) (ρ = 0.99 g/cm 3 ) [54]. Using data from the TPC in front of the phantom, the entry position and angle of the protons is determined. All entry positions are associated with a 0.5 mm x 0.5 mm grid on the x/z-plane. For the further radiograph reconstruction, two independant ways to measure exist: one by measuring the deposited energy in the phantom with the calorimeter; the other by determining the magnitude of the multiple scattering, using the TPC behind the phantom. Both methods will be discussed below. The deposited energy is retrieved from the measured energy in the calorimeter. First, all energy depositions are associated with the entry positions. The resulting pixel distributions are fitted with Gaussian distributions, with the mean recorded as the mean energy deposition at that particular entry point. The collection of all entry points is shown in Fig. 4.12a. Although the image is blurred due to multiple scattering, the different materials in the phantom can be clearly identified. 30

37 CHAPTER 4. PROPIX I PROTOTYPE Scaled value (arb. units) 2 1 Energy deposition Multiple scattering z (mm) Figure 4.14 Section at z = 18 mm in a grid of 1 mm x 1 mm for both the energy deposition and the multiple scattering. Similarly, a radiograph can be made by investigation of the multiple scattering magnitude. The position of the first TPC is again used as entry point, but now the information of the second TPC is used instead of the calorimeter. The difference in entry and exit position is recorded for all tracks. It is expected that the resulting distributions on every entry point follow a Gaussian distribution (see Eq. 2.4). The width of said distribution is used as a measurement of the multiple scattering. A radiograph of this sort is shown in Fig. 4.12b. Whilst the same pattern is present is this case, there is more noise. The first reason for this behaviour comes from the fact that more data is required to get a proper estimate of the width of the distribution than in the case of a measurement of the mean. More recorded tracks would solve this issue. The second reason is due to the fact that particles scatter away from the second TPC, which means only part of the distribution is found towards the edges. This complication could be resolved if either the TPC behind the phantom is increased in active area, or placed closer to the phantom. To show the transition between the different materials, the section at z = 18 mm is taken for a grid size of 1 mm x 1 mm (values as desired for clinical application) and scaled for both the deposited energy and the multiple scattering. This slice can be found in Fig. 4.14, in which the errors bars are found from the errors on the fitted parameters of the distributions. The transition between the two materials is clear in the case of the energy deposition; for multiple scattering there is more variation. The difference in energy deposition between the two materials is (in arbitrary units): E = (1.00 ± 0.04) ( 1.00 ± 0.08) = 2.00 ± 0.09 (4.1) The density resolution of the detector can be determined from this information, as the difference in density of the irradiated materials is known and (from Eq. 2.2) it is known that the energy deposition scales linearly with the density of the material. The density difference is given by: ( ) mm mm ρ = 1.19 = 0.71 g/cm 3 (4.2) 60mm 31

38 CHAPTER 4. PROPIX I PROTOTYPE Figure 4.15 The correlation between deposited energy and multiple scattering. Therefore, as it can be assumed that the uncertainty between the two measured points scales linearly and signals can be distinguished at a 1σ-level, the density resolution becomes: ρ res = ρ σ E E = = 0.03 g/cm 3 2.5% (4.3) One way to distinguish the different materials is to make a correlation plot between the energy deposition and the multiple scattering. This correlation is shown in Fig Although the image is heavily blurred due to multiple scattering, two ellipses for the different materials can be found. As the scattering radiograph provides information on the radiotion length of the traversed materials (see Eq. 2.4) and the deposited-energy radiograph gives information on the density of the traversed materials (see Eq. 2.1), the combination made in Fig can be a powerful tool of separating different materials and deserves further investigation in future work. 32

39 5 ProPix II prototype Through simulations it is investigated how the proposed detector layout (see Fig. 3.2) would perform as a proton radiography device. The interference of the trackers on the proton beam is determined; the study of a typical phantom is made; and the best way to measure a particle s energy is determined. All studied simulation configurations can be found in Fig. 5.1, as a reference point. Following this, the various optimisations on the hardware of ProPix II are shown using a radioactive source. 5.1 Prototype simulations The software-framework of Geant4 [55] is used for simulation purposes, because of its capability of tracking particles through matter. A proper beam definition is required to start these simulations. It is decided to make use of 200 MeV proton beam (most proton beams used in radiotherapy can provide energies up to 250 MeV [56]) with no energy spread, so that solely the effect of the sub-detectors can be studied Tracker Two distinct configurations will be discussed for the choice of tracker: the TPC and two hybrid silicon sensors. The schematics of both configurations, along with ten typical proton tracks, can be found in Fig. 5.0a and 5.0b. The exact specifications are given by: A TPC (30 x 30 x 50 mm 3 ) filled with a Ar:CF 4 :CO 2 (45:40:15) gas-mixture. A design with such dimensions and ionising gas has been used in ProPix I. Two 2x2 arrays of silicon Timepix3 sensors (28.16 x x 2.80 mm 3 ) [57]. The overall thickness of these detectors is divided into 300 µm silicon sensitive material, 500 µm of ASIC and 2000 µm of printed circuit board [58]. This configuration uses the dimensions of a characterised Timepix3 assembly [59]. To investigate the effect of the tracking devices on the energy-spectrum, the kinetic energy of the particles just after the traversed detector is measured. These results are shown in Fig One striking observation from this figure is the clear difference between the energy-spectrum of the TPC and silicon sensors. The TPC set-up has a much lower energy fluctuation than the one of silicon detectors, due to the lower material budget protons encounter on their path. 33

40 CHAPTER 5. PROPIX II PROTOTYPE a TPC of 3x3x5 cm 3 filled with a Ar:CF 4:CO 2 (45:40:15) gas-mixture. b Two configurations of 2x2 hybrid silicon sensors (28.16 x x 2.80 mm 3 ) with a distance of 5 cm. c A phantom with the dimensions of a typical head (an elliptical tube of 14 x 20 x 16 cm 3 ) filled with soft tissue. d Two configurations of 2x2 hybrid silicon sensors (28.16 x x 2.80 mm 3 ) with a with a distance of 10 cm. e A TPC, head phantom and two hybrid silicon sensor layers, with a variable distance. The dimensions and materials are as above. f A single configuration of 2x2 hybrid silicon sensors (28.16 x x 2.80 mm 3 ). g A stack of 16 configurations of 2x2 hybrid silicon sensors (28.16 x x 2.80 mm 3 ) and lead in between them. h A stack of 50 configurations of 2x2 hybrid silicon sensors (28.16 x x 2.80 mm 3 ). Figure 5.1 Overview of all configurations used for the simulation studies, along with ten typical proton tracks. 34

41 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.2 The simulated kinetic energy spectrum of particles after traversing either the TPC filled with a gas-mixture (µ = MeV / FWHM = 0.01 MeV / E = 0.01%), or the two 2x2 arrays of Timepix3 sensors (µ = MeV / FWHM = 0.60 MeV / E = 0.30%). Figure 5.3 The simulated deflection in the plane of particles after traversing either the TPC filled with a gas-mixture (FWHM = 0.05 cm), or the two 2x2 arrays of Timepix3 sensors (FWHM = 0.42 cm). As was noted earlier, the energy spread of the particle beam required for proton radiography is in the order of 0.10%. The simulated silicon sensors cause energy straggling up to 0.30%. This means that, after particles have traversed these sensors, the energy spread will immediately be dominated by the tracker. This must be avoided, as the best image resolution is desired. The spread caused by the TPC is with 0.01% significantly lower. Similar to the kinetic energy, the exiting point of the particles is recorded. The scattering angle θ 0 can be calculated from the deflection in the plane via y rms [7]: θ 0 = 3y rms (5.1) Since the reconstruction of tracks relies heavily on the entry position in the patient, it is desired to keep the scattering angle as small as possible. The collection of particle deflections in the plane is shown in Fig Again, the difference between TPC and silicon detectors is evident. The silicon detectors have significantly more interference with the incoming particles. The combined results on the energy spectrum and the scattering angle show that the TPC causes significantly less interference with the incoming particles than the silicon configurations. As the performance of both configurations can be expected to be similar, it can be concluded that the TPC is recommended as a tracking device. Unless the overall material budget of silicon chips can be significantly decreased, the TPC should be considered as the best particle tracker Phantom After the incoming particles have travelled through the tracking device, they will encounter the patient. A geometry with the dimensions of a typical head (an elliptical tube of 14 x 20 x 16 cm 3 ) [60] filled with soft tissue ( 63.2% O, 23.0% C, 10.5% H, and 2.3% N) [61] is constructed to investigate what happens at this interaction. Again, both energy and position of the particles that have traversed the phantom are recorded for different beam energies. The schematic of the entire configuration is shown in Fig. 5.0c. 35

42 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.4 Simulated energy spectrum measured after the protons have traversed the phantom for beam energies of 175 MeV ( E = 34.5%), 185 MeV ( E = 9.9%) and 200 MeV ( E = 5.7%). Figure 5.5 Simulated scattering transition measured after the protons have traversed the phantom for a 200 MeV proton beam energy (FWHM = 5.73 cm). The spectrum of the particles energy is shown in Fig It is clear that the highest energy resolution is achieved for the beam with highest energy. The fluctuation in the spectrum of the 200 MeV beam is in the order of 5.7%, which will be used as a reference point for the energy measurement design. It should be noted that a significant fraction the spread is caused by the cylindrical shape of the phantom, which causes scattering particles to travel a shorter path in the volume. However, as the situation in a clinical setting would similar, the used approach is justified. As for the scattering angle, see Fig. 5.5, this is less dependant on the initial energy. From the figure is it obvious that there is a significant amount of scattering after the particles have passed the phantom. Again, the cylindrical shape of the phantom influences this result. It can be concluded that the remaining part of the detector should be as close on the phantom as possible in order to minimize the fractional loss of particles. It can additionally be argued that the tracker resolution is off lesser importance at this point, as the position spread will be dominated by the phantom. The setup with two hybrid silicon planes can therefore be used to measure the particles exit position Energy measurement To perform a density scan of the phantom, it is required to measure the kinetic energy of the traversing particles. In the past, this measurement was done by a BaF 2 crystal combined with a photomultiplier tube. Due to the physically limited data acquisition rate (because of the decay time of the crystal), it will be necessary to replace this device with a faster system to move towards the 1 MHz benchmark. The success of the Timepix3 detectors has inspired the study of a completely pixel-detector based readout. Detectors based on four different measurement modes are investigated: the time of flight, absolute energy deposit, penetration depth of the incoming particles and a hybrid system (combining range with energy deposit) can all be used to determine the particle s kinetic energy. These options will be further discussed below. 36

43 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.6 The simulated time of flight of incoming 195 MeV (µ = ns / FWHM = ns / E = 0.13%) and 200 MeV (µ = ns / FWHM = ns E = 0.07%) protons in the Timepix3 design. Figure 5.7 The simulated energy deposit in a single layer of silicon sensors for a proton beam of 195 MeV (µ = 0.77 MeV/ FWHM = 0.15 MeV / E = 19.5%) and 200 MeV (µ = 0.75 MeV / FWHM = 0.15 MeV / E = 20.0%). Time of flight Particles with higher kinetic energy, travel faster. This fact can be used to relate the time that a measured particle is travelling to its kinetic energy. The time of flight of the particle is defined here as the absolute time difference between the hits in the first and last silicon layer. The resulting configuration is shown in Fig. 5.0d. It is investigated whether a set-up as configured in Fig. 5.0d would be able to resolve particles of 195 and 200 MeV. The time of flight for particles with these energies, whilst traversing a distance of 10 cm between the layers, is shown in Fig It can be found that the time of flight has a relativity high resolution; the absolute differences in their ToF are however rather small. To differentiate a 195 MeV proton from a 200 MeV one, a time resolution of at least ns ns = ns is required. This results in a frequency in the order of 70 GHz, which is approximately ten times larger than possible with the current generation Timepix chips. One way to counteract the problem of the differences in TOF being too small, would be to increase the distance between the layers by a significant fraction. When this is done, however, more protons will scatter away from the detector. To investigate the fraction of particles scattering away, a complete detector is simulated as in Fig. 5.0e. The incoming beam of 200 MeV protons travels through a TPC, followed by the phantom and then starts the time of flight measurement with two planes, which have a variable distance. In this configuration, only 69.3% of protons found in the first plane are found in the second plane for a distance of 5 cm, 44.0% for 20 cm and 17.4% for 50 cm. While the speed of the system may now supersede that of ProPix I, significantly less particles are found. This counteracts the purpose of the upgrade. To conclude, the time of flight measurement will not be able to provide enough information for energy determination at present. However, this detection method looks to be very promising for a next generation prototype. 37

44 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.8 The simulated mean energy deposit in the layers for 195 MeV ( E = 5.9%) and 200 MeV ( E = 5.6%). Figure 5.9 The simulated layers in which the last hit was found for a 195 MeV (µ = 44.6 / FWHM = 1.4 / E = 3.1%) and 200 MeV (µ = 46.5 / FWHM = 1.4 / E = 3.0%) proton beam. Gaussian functions are fitted to the last layers. Energy deposit The second method to be investigated is the energy deposit measurement in a single layer. The relation between deposited energy and kinetic energy is known to high precision by the Bethe formula (Eq. 2.1), which can be used to calibrate this machine. To make the device more sensitive, the detecting silicon layer is enlarged to 1 mm thickness. This value is close to the maximum achievable thickness at present. The rather simple configuration for this simulation is shown in Fig. 5.0f. The energy deposit of particles is shown in Fig. 5.7 for 195 and 200 MeV protons. It should be noted that this is the most challenging case, as less energetic particles will deposit more energy and thus will be more easily be separated. From the figure, it can immediately be seen that the separating power is relatively low. A single layer will only provide a resolution in the order of 20%. Above method can be extended by the addition of multiple layers. The spread in energy will be reduced when multiple measurements of the energy deposit are combined. A configuration of 16 layers is simulated to further investigate this, see Fig. 5.0g. The energy deposit for the particles is recorded in Fig The typical shape of a Bragg curve can be found in the figure, where it is clear that the 195 MeV proton comes to a stop before the 200 MeV one. The information from the layer in which the Bragg peak occurs has not been used in this study, due to its complex behaviour. The energy resolution that is retrieved in the case of 16 layers ( E = 5.6%) is in the same order of magnitude of the energy spread in the proton beam after the phantom ( E = 5.7%). The resolution of the radiograph will therefore not be limited by the detector, as desired. This makes this method an option worth investigating. Particle range Similar to the time of flight, each particle has a predictable range up to where it will travel as a function of its kinetic energy. This range is well defined, which makes it a good candidate for energy calibration. 38

45 CHAPTER 5. PROPIX II PROTOTYPE Table 5.1 Approximate energy resolutions for different configurations using 200 MeV protons, assuming that the range and energy deposit can be treated as independent measurements. Number of layers Range resolution Deposit resolution Combined resolution % 14.1% 5-8.9% 8.9% % 6.3% 5.8% % 4.4% 4.2% % 2.8% 2.1% To exploit the range of the particles, it will be necessary to let them come to a full stop. It is recommended to have as many layers as possible, to allow for the best energy resolution. Lots of layers will, however, result in an expensive detector. Plates of stopping material (Wolfram, Lead, etc.) can be therefore positioned in between the layers of chips to stop incoming particles faster. A configuration with 50 layers (with no stopping material) is shown in Fig. 5.0h. The layer in which a track last had a hit is defined as the last layer. This last layer is determined for all particles entering the detector. Again, particles either scatter out of the detector or they come to a full stop. In Fig. 5.9, the last hits are drawn for incoming protons of 195 and 200 MeV. It can be found that they could easily be separated, when enough statistics are collected. The last layers can be fitted with Gaussian functions, due to the range straggling of the particles [62]. It can be concluded that the range of the particle can provide an accurate estimate of its kinetic energy. The short distance between layers is an advantage, as multiple scattering is reduced to a minimum. One drawback is, however, that many layers are required to get a good energy resolution, increasing the overall cost. Hybrid measurement Range and energy deposit have been shown to both provide a proper energy determination. A combination of both can be made in order to improve the collected statistics. Such a combination is made for configurations of multiple number of layers. Both the resolution of the energy deposit and that of the particle s range are measured for incoming 200 MeV protons. The particle range is not able to be recorded when only a few layers are used. All of these results are summarised in Tab. 5.1, where it is assumed that both values can be treated as independent measurements. It is found that even when only 10 layers are used, it is possible to retrieve an energy resolution which is in the same order of that of the beam after traversing the phantom. This method of measuring can be concluded to be the best way of retrieving the best possible energy resolution in the shortest amount of time, using the hardware available at present. Concluding, the TPC would perform best as the tracker in front of the patient. After the particles have traversed the soft tissue, however, the spatial resolution is of lesser importance and hybrid silicon sensors too can be used for tracking purposes. The combination of measuring energy deposition and particle range at the same time is the best way to measure a particle s energy, again using hybrid silicon sensors. A stack of at least 10 layers is desired, but, as the budget for this project is limited, only two layers are constructed for now. 39

46 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.10 The simulated difference in number of ionized electrons between a 200 MeV proton and a 546 kev electron. Figure 5.11 The simulated difference in the number of ionized electrons by a 200 MeV proton between the DME:CO 2 (50:50) and Ar:CF 4:CO 2 (45:40:15) gas mixtures. 5.2 GEM voltage scan To arrive at an optimal detector performance, the voltages on the GEM foils are scanned to identify the best settings. The best setting is defined here as the value in which the highest data rate is obtained, whilst lowest amount of noise is present. The total GEM-voltage is the sum of the three individual voltages on the GEM foils. These voltages increase per foil in steps of 10 or 20V, with the highest voltage on the most upper GEM foil. This is the optimal way to run these devices according to the GEM-collaboration, as the ion back-flow is suppressed. The two used gas mixtures are Ar:CF 4 :CO 2 (45:40:15), which is the mixture used in ProPix I, and DME:CO 2 (50:50), which has been found to possess characteristics worth investigating. A transfer field of 2 kv/cm is used in the rest of this work. The detector will be irradiated for ten seconds with a Sr-90 radioactive source, decaying as [63]: 90 38Sr Y + e + ν e (5.2) To get a measure of the total number of ionised electrons, a simulation in Heed [64] is performed. Although the simulation does not predict the absolute number of electrons accurately, the relative difference can provide valuable insight into the physical possesses occurring. The difference between ionisation by a 200 MeV proton and a 546 kev electron (from Sr-90) can be found in Fig It is clear that a high-energetic photon will produce more electrons than a low-energetic electron as it traverses the chamber. The spatial resolution of tracks generated by a proton beam can therefore be expected to be better than tracks generated by the radioactive source. In Fig. 5.11, the two simulated gas-mixtures are drawn. It can be found that the difference between them is minimal. Both gasses can therefore be expected to perform comparable in terms of spatial resolution, as they induce approximately the same number of electrons. It should, however, be noted that this simulation considers an ideal case in which no electron capture is present. The individual characteristics should be carefully monitored. 40

47 CHAPTER 5. PROPIX II PROTOTYPE a The number of events with more than five clusters recorded in the detector. The distributions of both gasses flatten after reaching a certain value, because the gas is fully efficient. b The number of events with less than five clusters recorded in the detector. The distributions of both gasses spike after reaching a certain value, because of the GEM-discharges. c The signal-to-noise ratio, calculated by dividing the number of data events with the number of noise events. The highest values are found for V GAIN = 1280 V, for Ar:CF 4:CO 2 (45:40:15); and V GAIN = 1480 V, for DME:CO 2 (50:50). Figure 5.12 Several properties for the different GEM-voltages in the Ar:CF 4:CO 2 (45:40:15) and DME:CO 2 (50:50) gas mixtures with irradiation by a Sr-90 source for 10 s. 41

48 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.13 The hit distribution as a function of the row number, for several points in time. The t = 0 point is made at an arbitrary time of a few minutes after starting up the configuration. Figure 5.14 The curvature of tracks inside the chamber. The white region is due to a malfunctioning area of the GEM foil. The clustering algorithm from previous chapter is used to analyse the data. An event with more than five clusters is treated as usable data; the rest is classified as noise. The data of only one chip is used, so that variations in collecting efficiency do not distort the results. The obtained results on the data rate (Fig. 5.11a), the noise rate (Fig. 5.11b) and the resulting signal-to-noise ratio (Fig. 5.11c) are given in their respective figures. From the number of data events, it is evident that the detector detects more ionized electrons as the voltage on the GEMs is increased. The collection efficiency flattens when high voltages are reached, implying that (almost) all electrons are collected. It is clear that the DME:CO 2 (50:50) gas-mixture requires a higher voltage to reach the same collection efficiencies. This is expected, as the primary ionization of this mixture is lower than that of Ar:CF 4 :CO 2 (45:40:15) [47]. When the gain-voltage is set to the lower region, it becomes more likely that an electron coming from the source will result in less than five clusters. Thus, good events can incorrectly be treated as noise in this case. It is however argued that no proper tracks can be constructed with so few clusters, so the procedure can still be considered as accurate. When the number of events with less than five clusters is concerned, jumps at approximately 1300 and 1520 V are found for Ar:CF 4 :CO 2 (45:40:15) and DME:CO 2 (50:50), respectively. This increase is due to the spontaneous discharging of the GEM foil [65]. As the amplification voltage is turned up, more charge is collected on the isolating Kapton surface in between the copper layers. This charge can randomly spark onto the chip, where it is detected as a signal. These sparks usually occur only once in an event, hence explaining the spike. Sparking should be reduced to a minimum, as the detected charge has no physical basis and damage may occur Combining above results, the signal-to-noise ratio can be calculated. The number of data events is simply divided by the number of noise events for this measurement.the highest signals are found for V GAIN = 1280 for Ar:CF 4 :CO 2 (45:40:15) and V GAIN = 1480 V for DME:CO 2 (50:50). These settings will be used in the remainder of this document. 42

49 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.15 The calculated χ 2 red of each selected event for the two different gas-mixtures. Figure 5.16 The calculated clustersize of the selected events for the two different gas-mixtures. 5.3 Electric field distortions Several improvements are made in order to correct for the electric field deformations. Electrical tape is put over the high voltage supply, all voltages involved are carefully tuned and a Kapton foil with a copper top is cut to be put over the insulating regions of the GEM. This foil is connected to the field-cage circuit, in which several adjustments are made so that the entire circuit is divided by the resistors correctly. The first check that is made to see whether these adjustments are correct, is to plot the distribution of hits along the row of the chips. Again, a Sr-90 source is used to ionize the Ar:CF 4 :CO 2 (45:40:15) gas, with a drift field of 300 V/cm. This source is positioned approximately 20 cm in front of the detector, so that the ejected electrons will be more or less parallel when entering the TPC. To check whether the gas flow influences the results, the distribution is made at several points in time. The result of said procedure is shown in Fig First, it can be noted that this distribution looks more uniform than the one of previous data (Fig. 4.5). This suggests that the made adjustments have had a positive influence on the electric field uniformity. Secondly, there is no relation found between the shape of the distribution and the elapsed time. This indicates that the gas-flow has minor influence on the primary ionization, confirming the hypothesis stated earlier. To identify the electric field in the TPC in detail, the curvature is mapped in the same manner as was previously done. As the incoming electrons in the TPC have more transverse motion than high-energetic protons due to scattering, strict cuts are applied to the used events. Only tracks that have data over the entire length and minimal distortion in the width of the chamber are used for analysis. The found curvature is shown in Fig The incoming tracks traverse a more or less straight path through the chamber. Some curvature inwards is found on the edges of the chamber, but it is most likely that this is due to the analysis procedure. It is concluded that the performed adjustments have solved the discussed electric field deformations. 43

50 CHAPTER 5. PROPIX II PROTOTYPE Figure 5.17 The irradiated sample. Marked are the 500 µm lead pieces. Figure 5.18 The radiograph of the irradiated sample, where the colours represent the number of hits in the detector. 5.4 Choice of gas mixture As was shown previously, DME:CO 2 (50:50) will give rise to less transverse diffusion than Ar:CF 4 :CO 2 (45:40:15) (see. Fig. 4.3b). The performance of the two gasses will therefore be compared. The Argon-mixture operated at a drift field of 300 V/cm, as was done in ProPix I, and the DME-mixture at 1 kv/cm, which is close to the optimal value. As was done while discussing the curvature of the chamber, only tracks over the entire length of the TPC are used in data analysis. On these events, a track fitting algorithm similar to the analysis of the test beam data is performed. The residuals of the clusters with the fitted line will give an indication on the transverse diffusion of the gas. The found χ 2 red -distribution is shown in Fig The distribution of the DME:CO 2-mixture (at 1 kv/cm) is more skewed towards the left than that of Ar:CF 4 :CO 2 (at 300 V/cm). It can be concluded that the diffusion of DME:CO 2 is indeed found to be lower than Ar:CF 4 :CO 2 and it therefore should be used in further experiments, as it is desired to have the lowest spatial resolution as possible. Additionally, one can look at the average clustersize. As was noted before, the big size of the signals was a cause for concern as the total data acquisition rate could be limited. The average clustersize consists of many different elements. As the voltage on the GEMs increases, both the number of clusters and the size of the clusters increase. This does, however, not necessarily occur in the same fashion. Adding the random discharges (which are expected to be small in size), this number is composed of many different parameters. Above reasons make it hard to draw conclusions from the clustersize distribution. Nevertheless, the distribution of both gasses is shown in Fig for illustration purposes. The clustersize of the DME:CO 2 -mixture seems to be larger than that of Ar:CF 4 :CO 2. It is suspected that the difference is due to the higher amplification field required to run the GEMs, but no conclusive evidence is found. Regardless of the field, the clustersize was earlier shown to be limited by an unresolvable amount of diffusion. No further attention to these effects will therefore be spent on this topic. 44