PROTOTYPING NEUTRON DETECTORS FOR ......Greg Kochanski, Ian Dell’Antonio, and Tony Tyson (Bell Labs).[4] nuclear recoil. Figure 1.2 shows an example nuclear recoil and electron recoil

PROTOTYPING NEUTRON DETECTORS FOR SUPERCDMS CALIBRATION

by

Nicholas Hall

A senior thesis submitted to the faculty of

Brigham Young University - Idaho

in partial fulfillment of the requirements for the degree of

Bachelor of Science

Department of Physics

Brigham Young University - Idaho

April 2017

Copyright c© 2017 Nicholas Hall

All Rights Reserved

BRIGHAM YOUNG UNIVERSITY - IDAHO

DEPARTMENT APPROVAL

of a senior thesis submitted by

Nicholas Hall

This thesis has been reviewed by the research committee, senior thesis coor-dinator, and department chair and has been found to be satisfactory.

Date Todd Lines, Advisor

Date Evan Hansen, Senior Thesis Coordinator

Date Stephen Turcotte, Committee Member

Date Stephen McNeil, Chair

ABSTRACT

PROTOTYPING NEUTRON DETECTORS FOR SUPERCDMS CALIBRATION

Nicholas Hall

Department of Physics

Bachelor of Science

The Super Cryogenic Dark Matter Search (SuperCDMS) detector will look for

nuclear recoils. At low energies, the quenching factor is highly nonlinear, and

needs calibration. As a monoenergetic neutron beam is aimed at SuperCDMS,

a neutron detecting backing array will supply the information needed for the

calibration. Each individual detector in the backing array is comprised of a

scintillator and photomultiplier tube (PMT). The goal of this project was to

maximize the light yield as well as the signal to noise ratio of one of these

neutron detectors while testing the prototypes with gamma emitters such as

57Co and 22Na, and achieve a background gamma trigger rate of less than 10

Hz at a trigger threshold of less than 10 keV. The prototypes in question had a

sufficient signal to noise ratio, however the light yield will need to be increased

before the lowest desired energy interactions are detectable.

ACKNOWLEDGMENTS

First and foremost I would like to thank my wife, Patty. Her support and

encouragement to work harder at physics has been important to my success.

I am also thankful to Lauren Hsu and Erik Ramberg at Fermilab. Lauren was

my mentor, and I am thankful for her patience and expertise. And I owe a

big thanks to Erik Ramberg who pulled some strings, allowing me to work

at Fermilab with Patty. Their support along the way helped me accomplish

not just my research this summer, but also set me up for more success in the

future.

Contents

Table of Contents xi

List of Figures xiii

1 Introduction 11.1 Dark matter and SuperCDMS . . . . . . . . . . . . . . . . . . . . . . 11.2 Quenching factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Methods 72.1 Building and testing the neutron detectors . . . . . . . . . . . . . . . 72.2 Compton edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3 Neutron detector calibration . . . . . . . . . . . . . . . . . . . . . . . 92.4 Daqman and data analysis . . . . . . . . . . . . . . . . . . . . . . . . 122.5 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Results 153.1 What was found . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.3 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Bibliography 23

A Code 25A.1 ana.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25A.2 prep.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31A.3 adc graph.C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

xi

List of Figures

1.1 Evidence of dark matter seen as a buldge beneath the condensed masspeaks of individual stars when looking at gravitational lensing. Imagecredit goes to Greg Kochanski, Ian Dell’Antonio, and Tony Tyson (BellLabs).[4] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Nuclear and electron recoil in a lattice structure. Neutrons and WIMPscause nuclear recoils, while photons cause electron recoils. Image creditgoes to University of California, Berkeley. . . . . . . . . . . . . . . . . 3

1.3 Path of the neutron beam. The neutron detector backing array (thefocus of this project) is shown on the right. . . . . . . . . . . . . . . . 4

1.4 Another angle of the path of the neutron beam. See figure 2.2 andrelated text for an explanation of the concentric circles and uniqueshape of scintillators. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 Quenching facor of various experiments. Graph credit goes to Barker& Mei.[6] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1 One photon released from the scintillator generates a cascade of elec-trons in the PMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Neutrons deflected towards the backing array will pass through one ofthe scintillators (four scintillators are shown here in blue). The angularresolution of this setup would be 5◦. . . . . . . . . . . . . . . . . . . . 9

2.3 A radioactive source is placed near the neutron detector being tested.The signal in ADC is what is being recorded. . . . . . . . . . . . . . . 10

2.4 After the peaks are found, a line is drawn at 80% of each peak. Above,Compton edges correspond to values about 1000 ADC and 1345 ADC.That value is recorded by the script and printed as text (see ApendixA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 The slope of the linear fit was used to estimate the lower energy region. 163.2 Background trigger rate at varying trigger thresholds. Each data point

was taken one at a time. . . . . . . . . . . . . . . . . . . . . . . . . . 173.3 By dividing the trigger threshold (or x axis) by the slope of the linear

fit in figure 3.1, ADC is converted to keVee. . . . . . . . . . . . . . . 18

xiii

xiv LIST OF FIGURES

3.4 With a trigger threshold of less than 10, the left hand of the peak isexpected to be much farther to the left if the Compton edge is to beseen. Instead, this shape possibly the lowest energy detectable: singlephotons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.5 The height of the afterpulses ranges from roughly 20 ADC to 40 ADC(ADC is on the y axis) . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.6 This picture was taken for demonstrative purposes only. A scintillator(right) coupled with a PMT (left) in a clear tube. Note the electricaltape coupling. Electrical tape was later used to cover the clear tube. . 21

Chapter 1

Introduction

1.1 Dark matter and SuperCDMS

There is over 5 times as much dark matter in the universe as there is normal matter

that we are used to studying, yet dark matter has remained undetected. Dark matter

is massive, as it holds together galaxies and is the primary contributor to galactic

gravitational lensing (see figure 1.1), and yet it does not interact with the strong

force, the weak force, or the electromagnetic force; it is only known to interact through

gravity. This means that photons pass through dark matter without interacting; for

this reason, it is called “dark” matter.[1]

To combat the illusiveness of the dark matter, the Super Cryogenic Dark Matter

Search (SuperCDMS) detector at Fermilab is taking a creative approach; this detector

will look for dark matter by assuming that dark matter particles will interact with

nuclei of normal matter such as silicon and germanium through some new previously

undiscovered force.[2][3] If this assumption is true, energy will be transferred from

the dark matter particle to a nucleus, causing the nucleus to be dislodged from its

location and sent at some velocity through the target material; this event is called a

1

2 Chapter 1 Introduction

Figure 1.1: Evidence of dark matter seen as a bulge beneath the condensed mass

peaks of individual stars when looking at gravitational lensing. Image credit goes to

Greg Kochanski, Ian Dell’Antonio, and Tony Tyson (Bell Labs).[4]

nuclear recoil. Figure 1.2 shows an example nuclear recoil and electron recoil.

As a nuclear recoil occurs in the SuperCDMS detector, some of the silicon or

germanium atoms are ionized, and an electric field drags the free electrons to where

they may be detected. This type of experiment has been done before, however none

of the other detectors have been unsuccessful. SuperCDMS is special because it looks

at very low recoil energies, which translates to lower mass dark matter particles, or

low mass Weakly Interacting Massive Particles (WIMPs), which is this experiment’s

target dark matter candidate.

The goal of the SuperCDMS detector is to quantify the mass of dark matter

particles, which can be done theoretically by measuring all of the energy deposited

by the dark matter. In reality, however, the detector will not be able to collect

100% of the energy from the recoiling nucleus. As the nucleus travels through the

1.2 Quenching factor 3

Figure 1.2: Nuclear and electron recoil in a lattice structure. Neutrons and WIMPs

cause nuclear recoils, while photons cause electron recoils. Image credit goes to Uni-

versity of California, Berkeley.

detector, some of the kinetic energy of this nucleus is converted into ionization and

phonons (which are quantum vibrations in a lattice). Unfortunately, not all of the

energy deposited can be measured, and the SuperCDMS detector must be calibrated

to account for this missing energy.

1.2 Quenching factor

A monoenergetic neutron beam will be fired at the SuperCDMS detector, which will

record energies which correspond to (but are not equal to) the true energy deposited

into the detector. One of the neutrons will interact with the detector, deflected

at some angle. This deflected neutron will then interact with a secondary neutron


Figure 1.3: Path of the neutron beam. The neutron detector backing array (the focus

of this project) is shown on the right.

detecting backing array. Each node in the array corresponds to the scattering angle of

the deflected neutron. The neutron leaves the SuperCDMS detector with less energy

than it had when it entered, and this change in energy can be calculated with the angle

of deflection; this is the calculated energy. The ratio between the calculated energy

and the energy measured in the SuperCDMS detector is the quenching factor. The

SuperCDMS detector can be calibrated for all energies by calculating a spectrum

of different deflection angles, and comparing that to how much of that energy the

detector measures at each angle.

Figures 1.3 and 1.4 shows that the scattering angle is measured when an event

is simultaneously detected in both the neutron detector array and the SuperCDMS

detector. Once this angle is known, the true energy deposited in the system is known

with the use of the following equation: [5]

∆E = EnM2

n

(Mn +MT )2

(MT

Mn

+ sin2θ − cosθ√(MT

Mn

)2− sin2θ

)(1.1)

∆E is the energy transferred,En is the energy of the incident neutron, Mn and MT

are the masses of the neutron and target nucleon respectively, and θ is the measured

1.2 Quenching factor 5

Figure 1.4: Another angle of the path of the neutron beam. See figure 2.2 and related

text for an explanation of the concentric circles and unique shape of scintillators.

angle of deflection.

The neutron detectors in the backing array need to be calibrated for two reasons.

First of all, the detectors need to be tested before they are confirmed to be sensitive

enough for the SuperCDMS calibration to be completed within a timely manner.

The monoenergetic neutron beam, SuperCDMS detector and backing array will all

need to be committed for the duration of the SuperCDMS calibration, while the

deflected neutrons are detected as they fly through the backing array. To decrease

the calibration time, the backing array will need to detect a high percentage of the

deflected neutrons (this is one reason why the shape and size of the neutron detectors

are important).

As this project looks at the neutron detector backing array, it is important to note

influential factors when building these neutron detectors. The size and shape of the

scintillator, the signal to noise ratio, and the signal strength all influence the accuracy

of the detector. As these factors are addressed, the neutron detector design for the


Figure 1.5: Quenching facor of various experiments. Graph credit goes to Barker &

Mei.[6]

backing array will approach the parameters outlined in the goal of this project.

Figure 1.5 shows the quenching factor of several different experiments, including

the first CDMS detector. The ionization energy shows how much of the energy de-

posited in the system is measurable. The quenching factor is extremely important,

as it is often times the largest contributor to statistical uncertainty.

Chapter 2

Methods

2.1 Building and testing the neutron detectors

To calibrate the quenching factor of the SuperCDMS detector, a neutron detecting

backing array consisting of 15 to 30 linearly arranged Photomultiplier Tubes (PMTs),

each with its own custom sized scintillator will need to be prototyped, i.e. designed,

built, and tested in order to measure the scattering angle of these monoenergetic

neutrons. After performing this calibration on the neutron detector array, the Super-

CDMS calibration will be possible.

As the neutron passes through the scintillator in the neutron detector, the elec-

trons get excited, and are free to scintillate (or fall back down and emit a photon),

allowing the PMT to measure the sum of the energy of all the photons that reach the

photocathode. Each photon can excite one electron in the photocathode, and a high

voltage that is applied to the PMT pulls that electron through a series of dynodes,

each of which increases the number of electrons that are being pulled along by the

PMT (see figure 2.1). By the time the electrons reach the anode, there are enough

electrons for the voltage difference to be measured.

7

8 Chapter 2 Methods

Figure 2.1: One photon released from the scintillator generates a cascade of electrons

in the PMT

2.2 Compton edge

In order to test the neutron detectors seen in figure 1.3, each detector needs to be

calibrated, again by using a known energy source. The known energy source used

for this project was the Compton edge. When a photon is incident on a material,

the photon will scatter inelastically off of the electrons. The amount of energy left

by the photon depends on the energy of the photon and the angle of the scattering.

The maximum amount of energy that can be deposited when the photon scatters at

180 degrees. The Compton edge is a visual feature of the data which is seen when

number of events per energy level is plotted.

Compton edges are commonly used for calibration since the Compton edge can

be both calculated and seen in the data clearly, as long as the energy of the photon

in known. The equation below is used, where EC is the energy of the Compton edge,

EP is the energy of the photon, and Me is the mass of an electron.

EC = EP

(1− 1

1 + 2EP/Me

)(2.1)

In theory, the Compton edge is a rigid drop–off, but because of smearing the

Compton edge looks more like a hill. Because of this, instead of measuring the

Compton edge as the peak, the Compton edge is commonly measured at 80% of the

2.3 Neutron detector calibration 9

Figure 2.2: Neutrons deflected towards the backing array will pass through one of the

scintillators (four scintillators are shown here in blue). The angular resolution of this

setup would be 5◦.

peak.[5] Refer to figure 2.4.

2.3 Neutron detector calibration

To make sure a specific neutron detector is sensitive enough to be used for Super-

CDMS calibration, it needs to be set up to trigger an event when it measures an

energy level of less than 10 keV while recording events due to the cosmic background,

to be sure that it is not triggered at more than 10 Hz. If the trigger rate is more than

10 Hz, the neutron detector would have more noise than tolerable.[7] This will ensure

that each detector has a signal to noise ratio such that the probability of triggering

10 Chapter 2 Methods

Figure 2.3: A radioactive source is placed near the neutron detector being tested.

The signal in ADC is what is being recorded.

a false positive during SuperCDMS calibration is negligible. The backing array will

also need to have an angular resolution of less than 10◦ once completed, so the array

must be able to detect a large percentage of the deflected neutrons that pass through.

Committing to a fine angular resolution helps reduce the error bars in the quenching

factor (see figure 1.5). With larger angular resolution, more area is covered by each

detector. The sensitivity requirements for these neutron detectors are much stricter

because each neutron detector covers much less area. Figure 2.2 shows how the scin-

tillators of the neutron detectors will be arranged, depicted with blue rectangles. The

rings in the figure show an example angular resolution of 5◦.

The scintillators are what detect the neutrons, which is why the scintillators are

shown having different sizes. The scintillators must be rectangular prisms to maximize

light yield, thus the outer scintillators are longer; this maximizes area coverage while

maintaining angular precision.

The neutron detector must first be calibrated, as it records the voltage corre-

sponding to the energy in Analog to Digital Conversion (ADC) units, as opposed to

keV. Once the ADC to keV conversion factor for the neutron detector is known, ADC

converts to keV electron equivalence (keVee), as it is not a true keV measurement,

but a voltage analog to keV. Then the background data may be recorded and tested

against the set parameters of less than 10 keV and less than 10 Hz.

2.3 Neutron detector calibration 11

Figure 2.4: After the peaks are found, a line is drawn at 80% of each peak. Above,

Compton edges correspond to values about 1000 ADC and 1345 ADC. That value is

recorded by the script and printed as text (see Apendix A).

Acquiring the ADC to keV conversion requires using another known energy source.

For this experiment, gamma ray emitters were used, as seen in the simplistic mockup

of the experiment found in figure 2.3. Two of the gamma rays emitted by 22Na were

very useful for this experiment, and have energies corresponding to the Compton

edges 340.67 keV and 1062.15 keV.

The Compton edge creates a clear peak, as seen in figure 2.4, which can be used

to compare the energy of the Compton edge of that photon in keV to the energy of

the peak in ADC. As scintillators are also nonlinear, many energies must be plotted,

and a rough fit can allow for a linear estimate for the lower energies.


2.4 Daqman and data analysis

After the signal is detected by the neutron detector, the signal needs to be recorded

digitally for analysis. To do this, Fermilab has a program built on CERN’s framework

called ROOT. ROOT is a C++ based data analytical program written to process the

data collected at CERN. Daqman and DaqROOT are Fermilab specific libraries for

ROOT which have a few added functions, such as data collection and real time data

monitoring.[8]

The scripts written by the author for this experiment include the following func-

tions: retrieve the data, analyze it, and produce both readable graphs and text files

containing arrays of data to be used in future scripts. The graphs show the different

measured energies of the Compton edges, plotted against their true values in keV.

A linear fit was applied which works as an estimate for lower energies. The slope of

that line is a conversion factor between the ADC units and keV electron equivalence

(keVee) of the neutron detector. Once this conversion factor is known, background

data can be taken and converted from trigger threshold vs Hz in ADC to trigger

threshold vs Hz in keVee. If there are data points in the graph within both 10 Hz

and 10 keVee, the initial parameters have been met.

2.5 Materials

The scintillators used in this project were EJ-200 plastic scintillators developed by

Eljin Technologies, have a fall time of 2 ns, and expect 10,000 photons per MeV

deposited into the scintillator. The PMTs were Hamamatsu R760, have a 1.3 cm

diameter photocathode, and their Full Width Half Maximum (FWHM) is about 5 ns.

These PMTs were borrowed from a different experiment, and are the most expensive

piece of equipment needed for this backing array.

2.5 Materials 13

The digitizer used was a 4 channel CAEN DT5720, with a 250 MHz sample rate

(4 ns bin size), and a 12–bit dynamic range. An oscilloscope was used for preliminary

testing, basic high voltage supplies were used for the PMTs, and a dark box was used

to help minimize light leakage into the prototypes, which were feebly made ”light

tight” with nothing but black electrical tape. As added protection, all data was

recorded inside the dark box. Other items were used, including Eljin manufactured

liquid scintillator and PMT neutron detectors, which aided in script testing and

development.


Chapter 3

Results

3.1 What was found

After using equation 2, the Compton edges were found for each radioactive source

that was to be used. These values can be seen in figure 3.1. The Compton edge values

are on the x axis, and they correspond to the following sources, which are as follows,

from left to right: 133Ba (207.25 keV Compton edge from a 356.0 keV photon), 22Na

(340.67 Compton edge keV from a 511.0 keV photon), 137Cs (477.65 keV Compton

edge from a 662.0 keV photon), and 22Na (1062.15 keV Compton edge from a 1275.0

keV photon).

Equipped with the slope, the neutron detector was run at varying trigger thresh-

olds with all radioactive sources removed, leaving only the cosmic background radi-

ation. This result is found in figure 3.2 Using the quenching factor for the neutron

detectors, it was found that the prototype neutron detector nicknamed ”twins” had a

trigger rate of less than 10 Hz at a trigger threshold of less than 10 keVee; this fulfills

the preset requirements.

It is important to note that although the linear estimate is inadequate for any

15

16 Chapter 3 Results

Figure 3.1: The slope of the linear fit was used to estimate the lower energy region.

value above about 200 keVee, this linear fit is roughly equal to the exponential fit

for values below 200 keVee. As figure 3.3 is below 100 keVee, this linear fit does an

adequate job showing what is going on.

What was not anticipated, however, was that the neutron detector being tested

would have a high signal to noise ratio, but not be sensitive enough. Figure 3.4 shows

what should be the Compton edge of 241Am, yet it is unclear if the Compton edge is

visible, as 241Am has a Compton edge of 38 ADC; 38 ADC is to the left of the peak

and not to the right where it should be.

The trigger threshold of this run was at 5 ADC (at trigger thresholds much below

5 ADC, noise begins dominating, spiking up to the left of the graph, rendering the rest

of the information unreadable). It appears that a single photon deposits roughly 20 to

40 ADC of energy into the neutron detector, which is comparable to the afterpulsing

3.1 What was found 17

Figure 3.2: Background trigger rate at varying trigger thresholds. Each data point

was taken one at a time.

issue seen in figure 3.5. Afterpulsing is not expected for this case, and is evidence

that there is a potential error in the construction of the detector. The information in

section 2.5 about the expected light yield of these scintillators shows that only about

1.7% of the light produced by the neutron was collected by the PMT, assuming the

single photon theory is true. This percentage, or light yield, will need to be increased

before the neutron detector will be useful. as for now, only energies of about 12

keVee and higher are detectable. This means that the previous conclusion, that data

below 10 keV can be recorded without background events occurring at a frequency

of greater than 10 Hz, is incorrect.


Figure 3.3: By dividing the trigger threshold (or x axis) by the slope of the linear fit

in figure 3.1, ADC is converted to keVee.

3.2 Discussion

The goal of this project was to design, build, and test a neutron detector that had a

signal to noise ratio capable of detecting neutrons with a low probability of measuring

a false positive. Eventually, the research done in this project will be used to build the

final neutron detector backing array, and that backing array will be used to calibrate

SuperCDMS. The hope was to maximize the efficiency of the 30 PMTs that were

available, as they are the most expensive piece of equipment used in the backing

array. Originally the plan was to use one PMT for each scintillator, yielding 30

detectors. After testing the single PMT detector named “Junior”, it was obvious

that one PMT on each end of each scintillator was not enough. After running the

tests, it was found that even the detector named “Twins” wasn’t sensitive enough;

the Twins detector had a good signal to noise ratio, but light was most likely leaking

3.3 Future work 19

Figure 3.4: With a trigger threshold of less than 10, the left hand of the peak is

expected to be much farther to the left if the Compton edge is to be seen. Instead,

this shape possibly the lowest energy detectable: single photons.

or getting absorbed. More work needs to be done to increase the light yield in the

individual neutron detectors.

3.3 Future work

Several factors could have contributed to the low light yield of the neutron detectors,

and each of those factors will need to be experimented with. The detector’s coupling

was very crude, and is a prime candidate for reexamination (see figure 3.6). Other

factors may include scintillator quality, a malfunctioning DAQ, and more. One im-

mediate plan is to use a different scintillator, which has a small hole running down

its length; an optical fiber can be placed inside as a way to trap light and send it

towards the photocathode.

Another idea for future work is to increase the quality of the coupling between


Figure 3.5: The height of the afterpulses ranges from roughly 20 ADC to 40 ADC

(ADC is on the y axis)

scintillator and PMT. Optical gel was used, however a more solid construction with

braces might dramatically increase the light yield of the neutron detector.

This project must succeed. SuperCDMS needs to be calibrated — it is our best

bet at finding low mass weakly interacting dark matter particles.

3.3 Future work 21

Figure 3.6: This picture was taken for demonstrative purposes only. A scintillator

(right) coupled with a PMT (left) in a clear tube. Note the electrical tape coupling.

Electrical tape was later used to cover the clear tube.


Bibliography

[1] K. Freese, “Review of observational evidence for dark matter in the universe

and in upcoming searches for dark stars,” EAS Publications Series, 36, 113–126

(2009)

[2] R. Agnese, A. J. Anderson, M. Asai, D. Balakishiyeva, R. Basu Thakur, D. A.

Baur, J. Billard, A. Borgland, M. A. Bowles, D. Brandt, et al., “CDMSlite: a

search for low mass WIMPs using voltage assisted calorimetric ionization detec-

tion in the SuperCDMS experiment,” Phys. Rev. Lett. 112, 041302 (2014)

[3] D. Bauer, A. Borgland, B. Cabrera, F. Calaprice, J. Cooley, P. Cushman, T.

Empl, R. Essig, E. Figueroa–Feliciano, R. Gaitskell, et al., “Snowmass CF1

summary: WIMP dark matter direct detection,” arXiv:1310.8327 (2013)

[4] J. Anthony Tyson, Greg P. Kochanski, and Ian P. DellAntonio, “Detailed mass

map of CL0024+1654 from strong lensing,” arXiv:astro-ph/9801193 (1998)

[5] M. J. Safari, F. Abbasi Davani, H. Afarideh, “Differentiation method for lo-

calization of Compton edge in organic scintillation detectors,” arXiv:1610.09185

(2016)

[6] D. Barker, W. Z. Wei, D. M. Mei, and C. Zhang, “Ionization efficiency study for

low energy nuclear recoils in Germanium,” Elsevier, 48, 8–15

23

24 BIBLIOGRAPHY

[7] P. Brink, “SuperCDMS results and plans for SNOLAB”, Presentation for PA-

TRAS 2015 — Zaragonza, Spain, (Acessed October 15, 2016)

[8] Ben Loer, Daqman and DaqROOT software (2014)

[9] R. Agnese, A. J. Anderson, T. Aramaki, I. Arnquist, W. Baker, D. Barker,

R. Basu Thakur, D. A. Bauer, A. Borgland, M. A. Bowles, et al., “Projected

Sensitivity of the SuperCDMS SNOLAB experiment”, arXiv:1610.00006 (2016)

[10] Z. Ahmed, D. S. Akerib, S. Arrenberg, C. N. Bailey, D. Balakishiyeva, L. Baudis,

D. A. Bauer, P. L. Brink, T. Bruch, R. Bunker, et al., “Dark matter search results

from the CDMS II experiment,” www.sciencemag.org 327, 1619–1621 (March

2010)

[11] R. B. Thakur, “The cryogenic dark matter search low ionization–threshold ex-

periment,” University of Illinois at Urbana–Champaign doctoral thesis (2015)

Appendix A

Code

ana.C, ana amp.C, prep.C, and adc graph.C are the four most important scripts used

for this project. They were all written by the author, and were intended to be modular

and easily tweakable.

A.1 ana.C

This script was used to analyze (abbreviated to ana) the data collected by the DAQ.

The raw data was collected by Daqman, but needed this script to show a graph. This

code generated the graphs where the Compton edges are visible.

Comments, including unused code, were left in the following script as they may

inform the reader on the writing process.

///////////////////////////////////////////////////////////////////

void ana ( s t r i n g s e r i e s =”1607221634” , s t r i n g sourceType=”cs137 ” ,

s t r i n g ampIntg=”amp” , s t r i n g det=”det2 ”){

/∗∗∗∗∗∗∗∗∗∗∗∗INDEX∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗

Number Source Detector

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

25

26 Chapter A Code

//1606291607 −− N/A, 2 l i qu id−s c i n t

//1606291624 −− Co57 , 2 l i qu i d−s c i n t

//1606291632 −− Co57 , 2 l i qu i d−s c i n t

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

//1606300933 −− Co60 , 2 l i qu i d−s c i n t

1606301019 −− Cs137 , 2 l i qu i d−s c i n t

1606301057 −− Na22 , 2 l i qu i d−s c i n t

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

//1607071634 − Cs137 − EJ Junior

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607201630 − Cs137 − EJ Junior

1607201704 − Na22 − EJ Junior

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607221622 − Na22 − TWINS

1607221634 − Cs137 − TWINS

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607260934 − Cs137 − TWINS − Just turned on

1607261019 − Cs137 − TWINS − a f t e r 45 min

1607261103 − Cs137 − TWINS − a f t e r 1 . 5 hrs


−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607291016 − Ba133 − TWINS

∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗/

gROOT−>Se tS ty l e (” Pla in ” ) ;

// −− p i ck ing up con f i gu r a t i on f l a g −− //

i n t s e r i e s i n t = a t o i ( s e r i e s . c s t r ( ) ) ;

s t r i n g c on f i gu r a t i on ;

i n t co inFlag = 0 ; // 0 i s one PMT, 1 i s two PMTs but no co inc idence ,

//2 i s two PMTs with co in c id enc e

A.1 ana.C 27

// f o r new con f i gu ra t i on s , e d i t l a t e s t e l s e i f

i f ( s e r i e s i n t < 1607070000 && s e r i e s i n t > 1606290000){

c on f i gu r a t i on = ”2 l i qu id−s c i n t ” ;

co inFlag = 1 ;

}

e l s e i f ( s e r i e s i n t < 1607220000 && s e r i e s i n t > 1607070000){

c on f i gu r a t i on = ”EJ Junior ” ;

}

e l s e i f ( s e r i e s i n t > 1607220000 && s e r i e s i n t < 1607300000){

c on f i gu r a t i on = ” twins ” ;

co inFlag = 2 ;

}

e l s e i f ( s e r i e s i n t > 1607300000){

c on f i gu r a t i on = ”new setup ” ;

}

// −−− Read the histograms −−− //

TFile ∗ myf i l e = new TFile (Form(”/ data1/ pub l i c / nha l l / a n a l y s i s /

histograms/%s/ prep %s %s . root ” , c on f i gu r a t i on . c s t r ( ) ,

sourceType . c s t r ( ) , s e r i e s . c s t r ( ) ) ) ;

// d e c l a r e s a TFile ob j e c t which i s the s to r ed f i l e from prev i ou s l y

// Retr i eve the f i r s t histogram //

TH1F∗ h1 = myf i l e−>Get (Form(” h %s %s ” , det . c s t r ( ) ,

ampIntg . c s t r ( ) ) ) ;

i n t numBins=h1−>GetNbinsX ( ) ;

f l o a t xMin=h1−>GetXaxis()−>GetXmin ( ) ;

f l o a t xMax=h1−>GetXaxis()−>GetXmax ( ) ;

// h1−>SetLineColor ( kVio l e t ) ;

// h1−>SetXTit le (” Amplitude o f Peaks ” ) ;

28 Chapter A Code

h1−>Rebin ( 1 0 ) ; numBins /= 10 ;

// // Retr i eve the second histogram //

// TH2F∗ h2 = myf i l e−>Get (” h det2 amp ” ) ;

// h2−>SetLineColor (kRed ) ;

// h2−>SetXTit le (” Amplitude o f Peaks ” ) ;

// h2−>Rebin ( 1 0 ) ;

///////////////// −−− Get l o c a l maxima −−− ////////////////////////

i n t i ; // I t e r a t i v e bin number

i n t iBin ; // I t e r a t i v e bin value

i n t iBin2 ;

i n t hiBinNum ; // Bin number o f h i ghe s t va lue

i n t hiBinNum2 ;

i n t hiBinVal=0; // Highest va lue

i n t hiBinVal2=0;

i n t locMaxBin [ 2 0 ] ; // Array o f l o c a l max va lues

i n t locMaxBin2 [ 2 0 ] ;

i n t locMaxVal [ 2 0 ] ; // Array o f l o c a l max bin numbers , cor re spond ing to locMaxBin

i n t locMaxVal [ 2 0 ] ;

i n t numMax=0; // Number o f l o c a l maxima

in t numMax2=0;

i n t j ; // i t e r a t e s in ar rays

i n t k ; // i t e r a t e s in ar rays to f i nd bin w/ va l <= 80% of locMaxVal [ j ]

// −−− For h1 −−− //

cout << ”\ t−−−\tRunning f o r h1 . . . \ t−−−\n ” ;

i n t numPeaks=0;

f l o a t xval [ 2 0 ] ;

f o r ( i =3; i<numBins−4; i++) // i t e r a t e s through bins in h i s t o

{

A.1 ana.C 29

iB in= h1−>GetBinContent ( i ) ; // iBin i s now the value o f bin # i

i f ( iBin > 1 && // i f peak , f i nd 80%

iBin> h1−>GetBinContent ( i −4) &&




iBin>=h1−>GetBinContent ( i +1) &&



iBin>=h1−>GetBinContent ( i +4))

{

j=i ;

whi l e (h1−>GetBinContent ( j )>h1−>GetBinContent ( i )∗0 . 8 && j<numBins )

{ // f i n d s 80%

j++;

}

cout << ”−−Bin : ”<< i << ”\n−−Value : ”<< iB in << endl ;

cout << ”Peak value : ” << h1−>GetBinContent ( i ) << endl

<< ”Eighty Percent : ” << h1−>GetBinContent ( i )∗ . 8 << endl

<< ”80% Bin number : ” << j << endl

<< ”Actual he ight : ” << h1−>GetBinContent ( j ) << ”\n ” ;

xval [ numPeaks ]=(xMax−xMin)∗ j /numBins+xMin ;

cout << xMax <<endl<< xMin <<endl<<j<<endl<<numBins<<endl<<xMin ;

cout << ”True X value : ” << xval [ numPeaks ] << ”\n\n ” ;

numPeaks++;

}

}

// −−− Draw and save −−− //

TCanvas∗ can1 = new TCanvas (” can1 ” , ”Canvas 1” , 1000 , 600 ) ;

h1 −> SetSta t s (kFALSE) ;

30 Chapter A Code

gPad−>SetLogy ( ) ;

h1−>Draw ( ) ;

/∗ −−− Draws l i n e s −−− ∗/

f o r ( i n t k=0;k<numPeaks ; k++){

TLine ∗ l=new TLine ( xval [ k ] , 0 , xval [ k ] , 1 . 7 e5 ) ;

l−>SetLineColor ( kBlue ) ;

l−>SetLineWidth ( 1 . 3 ) ;

l−>Draw ( ) ;

}

can1−>Print (Form(”/ data1/ pub l i c / nha l l / an a l y s i s / p n g f i l e s/%s /

Ana %s %s %s %s . png ” , c on f i gu r a t i on . c s t r ( ) ,

det . c s t r ( ) , ampIntg . c s t r ( ) , s e r i e s . c s t r ( ) , sourceType . c s t r ( ) ) ) ;

// −−− Save numbers to a f i l e −−− //

ofstream fout (Form(”/ data1/ pub l i c / nha l l / an a l y s i s / t x t f i l e s /

%s /compt %s %s %s %s . txt ” , c on f i gu r a t i on . c s t r ( ) ,

det . c s t r ( ) , ampIntg . c s t r ( ) , s e r i e s . c s t r ( ) , sourceType . c s t r ( ) ) ) ;

f out << numPeaks << ” ” ;

f o r ( i n t i =0; i<numPeaks ; i++){

f out << xval [ i ] << ” ” ;

}

f out << ”\nGraph length = ” << xMax − xMin << ” , and number o f b ins = ”

<< numBins << ”\nSo bin width = ” << (xMax − xMin)/numBins

<< endl << xMax << ” ” << xMin ;

f out . c l o s e ( ) ;

}

A.2 prep.C 31

A.2 prep.C

This script was used to prepare the data for graphing by compiling it into histograms.

This made for quicker graph generation.



///////////////////////////////////////////////////////////////////

void prep ( s t r i n g s e r i e s =”1607291342” , s t r i n g sourceType=”na22 ”){

/∗∗∗∗∗∗∗∗∗∗∗∗INDEX∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗

Number Source Detector

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

//1606291607 −− N/A, 2 l i qu id−s c i n t

//1606291624 −− Co57 , 2 l i qu i d−s c i n t

//1606291632 −− Co57 , 2 l i qu i d−s c i n t

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

//1606300933 −− Co60 , 2 l i qu i d−s c i n t

1606301019 −− Cs137 , 2 l i qu i d−s c i n t

1606301057 −− Na22 , 2 l i qu i d−s c i n t

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

//1607071634 − Cs137 − EJ Junior

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607201630 − Cs137 − EJ Junior

1607201704 − Na22 − EJ Junior

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607221622 − Na22 − TWINS

1607221634 − Cs137 − TWINS

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

32 Chapter A Code

1607260934 − Cs137 − TWINS − Just turned on

1607261019 − Cs137 − TWINS − a f t e r 45 min

1607261103 − Cs137 − TWINS − a f t e r 1 . 5 hr


−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1607290929 thresh scan

1607290940 . . .

1607290950 . . .

1607291001 . . .

∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗∗/

gROOT−>Se tS ty l e (” Pla in ” ) ;

// −− p i ck ing up con f i gu r a t i on f l a g −− //

i n t s e r i e s i n t = a t o i ( s e r i e s . c s t r ( ) ) ;

s t r i n g c on f i gu r a t i on ;

i n t co inFlag = 0 ; // 0 i s one PMT, 1 i s two PMTs but no co inc idence ,

//2 i s two PMTs with co in c id enc e

// f o r new con f i gu ra t i on s , e d i t l a t e s t e l s e i f

i f ( s e r i e s i n t < 1607070000 && s e r i e s i n t > 1606290000){

c on f i gu r a t i on = ”2 l i qu id−s c i n t ” ;

co inFlag = 1 ;

}

e l s e i f ( s e r i e s i n t < 1607220000 && s e r i e s i n t > 1607070000){

c on f i gu r a t i on = ”EJ Junior ” ;

}

e l s e i f ( s e r i e s i n t > 1607220000 && s e r i e s i n t < 1607300000){

A.2 prep.C 33

c on f i gu r a t i on = ” twins ” ;

co inFlag = 2 ;

}

e l s e i f ( s e r i e s i n t > 1607300000){

c on f i gu r a t i on = ”new setup ” ;

}

// −−− Open . root f i l e l o c a t i o n −−− //

s t r i n g dataDir=”/data1/ pub l i c /daqmandata test / proce s s ed /

”+con f i gu r a t i on+”/ l l h s u t e s t ”+ s e r i e s +”. root ” ;

i n t numBins=1000;

// f o r backup , i f I don ’ t want to add f i l e s

f l o a t xMinAmp;

f l o a t xMaxAmp;

f l o a t xMinIntg ;

f l o a t xMaxIntg ;

i f ( sourceType==”cs137 ”){

xMinAmp=0;

xMaxAmp=1750;

xMinIntg=0;

xMaxIntg=10000;

i f ( c on f i g u r a t i on == ”2 l i qu id−s c i n t ”){

xMaxIntg=2400;

xMaxAmp=800;}

} e l s e i f ( sourceType==”na22 ”){

xMinAmp=0;

34 Chapter A Code

xMaxAmp=1600;

xMinIntg=0;

xMaxIntg=16000;


xMaxIntg=10000;

xMaxAmp=2250;}

} e l s e i f ( sourceType==”co60 ”){

xMinAmp=0;

xMaxAmp=1750;

xMinIntg=0;

xMaxIntg=10000;


xMaxIntg=2400;

xMaxAmp=800;}}

TFile ∗ f = TFile : : Open( dataDir . c s t r ( ) ) ;

i f ( f == 0){

cout <<”Cannot open” << dataDir ;

r e turn ;

}

// −−− Make TTree from open f i l e −−− //

TTree∗ Events=(TTree∗) f−>Get (” Events ” ) ;

// Get number o f events //

cout <<”Number o f events in t h i s t r e e = ” << Events−>GetEntr ies ( ) << endl ;

A.2 prep.C 35

///////////////////////////////////////////////////////////

// −−− Create Histogram , populate i t with data from TTree−−− //

TH1F∗ h det1 amp = new TH1F(” h det1 amp ” , Form(”Det 1 Amplitude , %s ,

f i l e ∗%s . root ” , sourceType . c s t r ( ) , s e r i e s . c s t r ( ) ) , numBins ,

xMinAmp, xMaxAmp) ;

h det1 amp−>SetLineColor ( kVio l e t ) ;

h det1 amp−>SetMinimum ( 0 . 1 ) ;

// h det1 amp−>SetMaximum (1 0 0 0 0 . ) ;

h det1 amp−>SetXTit le (” Amplitude o f peaks ” ) ;

TH1F∗ h de t 1 i n t g = new TH1F(” h de t 1 i n t g ” , Form(”Det 1 In t eg ra l , %s ,

f i l e ∗%s . root ” , sourceType . c s t r ( ) , s e r i e s . c s t r ( ) ) ,

numBins , xMinIntg , xMaxIntg ) ;

h de t1 in tg−>SetLineColor ( kVio l e t ) ;

h de t1 in tg−>SetMinimum ( 0 . 1 ) ; //must be on f o r l og y

// h det1 in tg−>SetMaximum (1 0 0 0 0 . ) ;

h de t1 in tg−>SetXTit le (” I n t e g r a l o f peaks ” ) ;

// −−− Create second Histogram −−− //

TH1F∗ h det2 amp = new TH1F(” h det2 amp ” , Form(”Det 2 Amplitude , %s ,


numBins , xMinAmp, xMaxAmp) ;

h det2 amp−>SetLineColor ( kVio l e t ) ;

h det2 amp−>SetMinimum ( 0 . 1 ) ;

// h det2 amp−>SetMaximum (1 0 0 0 0 . ) ;

h det2 amp−>SetXTit le (” Amplitude o f peaks ” ) ;

36 Chapter A Code

TH1F∗ h de t 2 i n t g = new TH1F(” h de t 2 i n t g ” , Form(”Det 2 In t eg ra l , %s ,


numBins , xMinIntg , xMaxIntg ) ;

h de t2 in tg−>SetLineColor ( kVio l e t ) ;

h de t2 in tg−>SetMinimum ( 0 . 1 ) ; //must be on f o r l og y

// h det2 in tg−>SetMaximum (1 0 0 0 0 . ) ;

h de t2 in tg−>SetXTit le (” I n t e g r a l o f peaks ” ) ;

//////////////////////////////////////////////////////

// −−− Cut d e f i n i t i o n s −−− //

TCut cDet1Pulse (” channe l s [ 0 ] . pu l s e s [ 0 ] . found peak == 1” ) ;

TCut cDet2Pulse (” channe l s [ 1 ] . pu l s e s [ 0 ] . found peak == 1” ) ;

TCut cDet1NotSaturated (” channe l s [ 0 ] . pu l s e s [ 0 ] . peak saturated == 0” ) ;

TCut cDet2NotSaturated (” channe l s [ 1 ] . pu l s e s [ 0 ] . peak saturated == 0” ) ;

TCut cDet1GoodStart (” channe l s [ 0 ] . pu l s e s [ 0 ] . f ound s t a r t == 1” ) ;

TCut cDet2GoodStart (” channe l s [ 1 ] . pu l s e s [ 0 ] . f ound s t a r t == 1” ) ;

TCut cDet1GoodEnd (” channe l s [ 0 ] . pu l s e s [ 0 ] . found end == 1” ) ;

TCut cDet2GoodEnd (” channe l s [ 1 ] . pu l s e s [ 0 ] . found end == 1” ) ;

TCut cGoodEvent ;

i f ( co inFlag == 0){

A.2 prep.C 37

cGoodEvent = cDet1Pulse + cDet1NotSaturated +

cDet1GoodStart + cDet1GoodEnd ;

} e l s e i f ( co inFlag == 1){

TCut cDet1GoodEvent = cDet1Pulse + cDet1NotSaturated +

cDet1GoodStart ; // + cDet1GoodEnd ;



cGoodEvent = cDet1GoodEvent | | cDet2GoodEvent ;

} e l s e i f ( co inFlag == 2){





cGoodEvent = cDet1GoodEvent + cDet2GoodEvent ;}

// good end cuts out tons o f events , p o s s i b l y due to a f t e r p u l s i n g

////////////////////////////////////////////////////////////////////////

// −−− Draw −−− //

TCanvas∗ can1 = new TCanvas (” can1 ” , ”Canvas 1” , 1700/2 , 1000/2) ;

can1−>Divide ( 2 , 2 ) ;

can1−>cd ( 1 ) ;

gPad−>SetLogy ( ) ; // add << ,”” ,5000 >> a f t e r cGoodEvent

Events−>Draw(” channe l s [ 0 ] . pu l s e s [ 0 ] . peak amplitude>>

h det1 amp ” ) ; / / , cGoodEvent ) ;

38 Chapter A Code

can1−>cd ( 2 ) ;


Events−>Draw(” channe l s [ 0 ] . pu l s e s [ 0 ] . i n t e g r a l∗−1>>

h de t 1 i n t g ” , cGoodEvent ) ;

i f ( co inFlag > 0){

can1−>cd ( 3 ) ;


Events−>Draw(” channe l s [ 1 ] . pu l s e s [ 0 ] . peak amplitude>>

h det2 amp ” , cGoodEvent ) ;

can1−>cd ( 4 ) ;


Events−>Draw(” channe l s [ 1 ] . pu l s e s [ 0 ] . i n t e g r a l∗−1>>

h de t 2 i n t g ” , cGoodEvent ) ;

}

can1−>Print (Form(”/ data1/ pub l i c / nha l l / an a l y s i s / p n g f i l e s/%s /

prep %s %s . png ” , c on f i gu r a t i on . c s t r ( ) ,

sourceType . c s t r ( ) , s e r i e s . c s t r ( ) ) ) ;

cout << ”Done\n ” ;

// −−− To wr i t e the f i l e −−− //

TFile ∗ o u t f i l e = new TFile (Form(”/ data1/ pub l i c / nha l l / an a l y s i s /

histograms/%s/ prep %s %s . root ” ,

c on f i gu r a t i on . c s t r ( ) , sourceType . c s t r ( ) ,

A.2 prep.C 39

s e r i e s . c s t r ( ) ) , ” r e c r e a t e ” ) ;

h det1 amp−>Write ( ) ;

h de t1 in tg−>Write ( ) ;

i f ( co inFlag > 0){

h det2 amp−>Write ( ) ;

h de t2 in tg−>Write ( ) ; }

o u t f i l e−>Close ( ) ;

}

40 Chapter A Code

A.3 adc graph.C

This script was used to plot the Compton edges and find a fit to be used to convert

ADC to keVee. The numbers being graphed were taken from text generated in the

code ana.C and represent the energies of the Compton edges.



///////////////////////////////////////////////////////////////////

void adc graph ( ){

const i n t num=5;

/∗//////////////////

Compton Edges ( in keV) o f

Na22 :

340.669333335

1062.15289121

Co57 :

0.727272727273

39.4278145695

47.2439335888

Co60 :

963.392062714

1118.59553037

Cs137 :

477.65013624

Ba133 :

19.0760059613

A.3 adc graph.C 41

207.254292723

Am241 :

11.410459588

//////////////////∗/

f l o a t compt [num]={477.65 , 207 .25 ,340 .669 , 1062 . 15 , 0} ;

f l o a t data1 [num]={4550 , 2558 .8 ,3680 ,7200 ,0} ;

// f l o a t data2 [num]={ } ;

f l o a t Ecompt [num]={0 ,0 ,0 , 0 , 0} ;

f l o a t Edata1 [num]={100 ,100 ,100 ,100 ,100} ;

s t r i n g sourceType ;

TCanvas ∗ c1 = new TCanvas (” c1 ” ,” Energy graphs ” , 0 , 0 , 1000 , 700 ) ;

// c1−>Divide ( 1 , 2 ) ;

// c1 −> cd ( 1 ) ;

I n t t n = num;

gr = new TGraphErrors (n , compt , data1 , Ecompt , Edata1 ) ;

gr −> Se tT i t l e (” I n t e g r a l Pulse vs Energy (keV ) ;

True Energy o f Compton Edge (keV ) ; I n t e g r a l o f Pulse ” ) ;

// gr −> GetYaxis ( ) −> S e tT i t l eO f f s e t ( . 9 5 ) ;

gr −> GetYaxis ( ) −> S e tT i t l e S i z e ( 0 . 0 5 ) ;

gr −> GetXaxis ( ) −> S e tT i t l e S i z e ( 0 . 0 4 8 ) ;

gr −> SetMinimum ( 0 ) ;

gr −>GetXaxis()−>SetL imits (0 , 1200 ) ;

gr −> Fit (” pol1 ” , ”” , ”0” , 0 , 250 ) ;

42 Chapter A Code

TF1∗ pol1 = new TF1 (∗ (TF1∗) gr −> GetFunction (” pol1 ” ) ) ;

gr −> Fit (” pol2 ” , ”” , ”” , 0 , 1200 ) ;

TF1∗ pol2 = new TF1 (∗ (TF1∗) gr −> GetFunction (” pol2 ” ) ) ;

gr −> SetMarkerStyle ( 8 ) ;

gr−>Draw(”Ap” ) ;

po l2 −> Draw(” same ” ) ;

// pol1 −> GetXaxis ( ) −> SetLimit ( 0 , 1 200 ) ;

// pol1−>GetXaxis()−>SetRange ( 0 , 1 200 ) ;

TF1 ∗ po l1 2=new TF1(” pol12 ” ,” pol1 ” , 0 , 1400 ) ;

po l1 2−>SetParameter (0 , pol1−>GetParameter ( 0 ) ) ;

po l1 2−>SetParameter (1 , pol1−>GetParameter ( 1 ) ) ;

pol1−>SetMaximum(1200 ) ;

// pol1−>Draw(” same ” ) ;

po l1 2 −> SetL ineSty l e ( 7 ) ;

po l1 2 −> SetLineColor ( 9 ) ;

po l1 2 −> Draw(” same ” ) ;

c1 −>Print (”/ data1/ pub l i c / nha l l / Integra lPu l s evsEnergy . png ” ) ;

// c1 −> cd ( 2 ) ;

// gr = new TGraph(n , compt , data2 ) ;

// gr −> Se tT i t l e (” I n t e g r a l o f pu l s e vs Energy (keV ) ” ) ;

// gr −> SetMinimum ( 0 ) ;

// gr −>GetXaxis()−>SetL imits (0 , 1100 ) ;

// gr −> Fit (” pol1 ” ) ;

// gr−>Draw(”A∗” ) ;

}

Documents

PROTOTYPING NEUTRON DETECTORS FOR ......Greg Kochanski, Ian Dell’Antonio, and Tony Tyson (Bell Labs).[4] nuclear recoil. Figure 1.2 shows an example nuclear recoil and electron recoil