Monte Carlo Simulation Techniques Pravata K Mohanty Tata Institute of Fundamental Research, Mumbai...
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Monte Carlo Simulation Techniques
Pravata K Mohanty
Tata Institute of Fundamental Research, Mumbai
Winter School on Astroparticle Physics, Bose Institute, Darjeeling, 14 - 22 December 2009
s
Deterministic Process
s Example:
For a given input, the outcome can be exactly determined.
X1 X2X3
Y1 Y2Y3
f(x) = x2
Stochastic Process
sExample: Interaction of primary cosmic rays in the atmosphere
and development of EAS
Outcome can’t be exactly determined
E1 E2 E3
Atmosphere
N1 N2 N3
E1 = E2 = E3 = E
N1 = N2 = N3
Stochastic Process
s
Q. what are the life time of the muons?
P + P
+ -0
+ -
Probability and Random numbers
s
Number of muons survive after time t
N (t) = N(0) e –t/
Number of decays after time t
Ndecay(t)= N(0) - N(t)= N(0) – N(0) e -
t/
Decay probability P = N decay(t) / N(0) = 1 – e -t/
Or t = - ln(1-P), 0 < P <1
Replace P with R, where you call R as a randomnumber and 0<R<1
t = - ln(1-R)
Muon decay
s
Monte Carlo simulation
s
• The average behaviour of the process obtained from the measurements i.e. the current knowledge about the process
Ex. N (t) = N(0) e –t/
• Convert it to a probability distribution and use random numbers for probability to generate the variates.
Random Numbers True random numbers:
Obtained from natural processes Pseudo random numbers:
generated by computers using some algorithm.
Example: Linear Congruential Generator
X n+1 = a X n + c mod m
m is the period, X0 is the SEED
m and c should be relatively prime
In C++, m = 232, a=214013, c = 2531011
How to generate Random Numbers
In C or C++, you can generate random numbers like this
for (int i=0; i<10000; i++) {
r = rand(); //Here rand() is the random
} //number generator
Value of using random numbers
-a/2
a/2
-a/2 a/2(0,0)
Area of Circle/ Area of Square = (a/2)2 /a2 = /4
Manual Method: 1. Randomly throw pebbles inside the square 2. Count the number of pebbles inside the circle 3. Take ratio of the number of pebbles inside circle to the total.
Using Computer
1. Generate points with x and y coordinates uniformly inside the square of side a x = -a/2 + a/2*R1 R1 and R2 are Random Numbers y = -a/2 + a/2**R22. count the number of points inside the circle r = (x2 + y2 ) < a/2
History of Monte Carlo Simulation
The name "Monte Carlo" was popularized by Stanislaw Ulam, Enrico Fermi, John von Neumann, and Nicholas Metropolis, among others; the name is a reference to the Monte Carlo Casino in Monaco where Ulam's uncle would borrow money to gamble. The use of randomness and the repetitive nature of the process are analogous to the activities conducted at a casino.
Perhaps the most famous early use was by Enrico Fermi in 1930, when he used a random method to calculate the properties of the newly-discovered neutron. Monte Carlo methods were central to the simulations required for the Manhattan Project, though were severely limited by the computational tools at the time. Therefore, it was only after electronic computers were first built (from 1945 on) that Monte Carlo methods began to be studied in depth. In the 1950s they were used at Los Alamos for early work relating to the development of the hydrogen bomb, and became popularized in the fields of physics, physical chemistry, and operations research.
Applications of Monte Carlo
Monte Carlo method is used in almost every field of science, mathematics to
economics
Monte Carlo methods are very important in computational physics, physical
chemistry, and related applied fields, and have diverse applications from
complicated quantum chromodynamics calculations to designing heat shields
and aerodynamic forms.
The Monte Carlo method is widely used in statistical physics
In experimental particle physics, these methods are used for designing detectors,
understanding their behavior and comparing experimental data to theory.
Designing a plastic scintillator detector using Monte Carlo
Design Goals
High photon yield
Good spatial uniformity
Good timing
Low Cost
Ease for fabrication
Plastic scintillator detector
s
For total internal reflection,
sin > sin c c = 38.7o
Meridional and Skew Ray Mode
Fiber axis
12
34
5
Skew ray1
3
2
4
5
Fiber axis
1
2
3Meridional ray
1, 3
2
(a) A meridionalray alwayscrosses the fiberaxis.
(b) A skew raydoes not haveto cross thefiber axis. Itzigzags aroundthe fiber axis.
Illustration of the difference between a meridional ray and a skew ray.Numbers represent reflections of the ray.
Along the fiber
Ray path projectedon to a plane normalto fiber axis
Ray path along the fiber
© 1999 S.O. Kasap, Optoelectronics (Prentice Hall)
In skew ray modethe incident anglechanges at everyreflection
Important steps of MC
Production and propagation of muons
Generation and propagation of photons inside scintillator considering the various losses
Propagation of photons in WLS fiber
Collection of photon and convert then to photo electrons
Generating Cosmic Ray Muons
Angular distribution of muons
dN/d cos2
can be randomly chosen from this distribution
The probability of getting a muon between 0 to is
P = 2 cos2 cos sin d d
602cos2 cos sin d d
= (1 – cos4)/0.9375
Hence = cos-1(1-0.9375P)1/4
Continued
As we know the value of probability between is between 0 to 1, this can be selected using a uniform random number between 0 to 1
P R, R is the random number
As distribution is uniform between 0 to 2
Probability P = / 2
or = 2P = 2R
Then distribute the muons uniformly over the surface of the scintillator .
X = Xmin + ( Xmax – Xmin)*R1
Y = Ymin + (Ymax – Ymin)*R2
Energy loss calculation Though mean energy loss remains fairly constant over a large energy range above minimum ionizing energy (Bethe-Block formula), however there is fluctuation in the energy loss around the mean. The fluctuation of energy loss is described by Landau distribution. Landau distribution gives distribution of a universalparameter called , which is independent of thematerial and particle velocity.
The relation between energy loss ΔE and is
ΔE = [ + ln(5.596707 x109 2 )/(1- 2 )Z2+1 - 2 - E ]
Where = (0.1536/ 2) (Z /A) S S is mass density of the material
The probability distribution of is used from ROOT Mathematical function LandauI
Photon Production
Generate and for each photon randomly from an isotropic distribution Track the photon and for every reflection check for the critical angle condition sinI > sinc
c = 39o
Number of photons producedN = ΔE Δl / , -> Energy required to produce a single photon Δl -> Incremental path length = 100 eV, Navg = 20,000 /cm for vertical muons
Attenuation loss of photons
Scintillator is not fully transparent to the blue wave length photons because of self-absorption in POPOP
The attenuation formula is
I = Io exp(-x/), Here = Mean attenuation length
I/Io = exp(-x/ )
P = 1 - I/Io = 1 - exp(-x/ ), x = - *ln(1-P) = - *ln(1-R)
Determine the path length of each photon a priori and compare with total path length traversed at each reflection.
Diffuse Reflection
Lambert’s cosine law
dI/d cosHence probability of photons reflected between 0 to
P = cos d /2 cos d
Hence P = sin, = sin -1 R and = 2R
Photon propagation in WLS fiber
Core n0
Inner clad n1
Outer clad n2
Conversion of photons to Photo-electrons
PMT converts the photons to photo-electrons. The conversion efficiency
depends on the quantum efficiency of the PMT.
Simulation Inputs
Photon statistics No of photons produced 46,000
Fraction of photons escaped 25%
Fraction of photons lost due to 60% attenuation in scintillator
Fraction of photons captured by 15% WLS fiber
Trapping Efficiency in Fiber = 14% of the captured photons
Number of photon collected at PMT = 208Collection efficiency = 0.45%
Photo electron yield and timing comparison
parallel
matrix
parallel
matrix
Photo–electron yield with Number of fibers
Ne Nfib
Number of Fibers
The simulation code
This is a single C++ program of ~ 1000 lines of cde
The code can be compiled by g++ or C++ command
Easy to modify the inputs
Any one interested to use this can contact
Summary
The good agreement of simulation with measurement would allow us to design and optimize detector in future by doing simulation prior to the actual construction which would save lot of time and cost
THANKS
Plastic scintillator detector
s
Photon statistics
With Tyvek reflector No reflector
No of photons produced 46,000 46,000
Fraction of photons escaped 25% 75%
Fraction of photons lost due to 60% 20% attenuation in scintillator
Fraction of photons captured by 15% 5% WLS fiber
Trapping Efficiency in Fiber: For skew rays = 14 % (Real case) For meridional rays = 11%
Number of photon collected at PMT = 208Collection efficiency = 0.45%
Photo electron yield and timing comparison
parallel
parallel
matrix
Photo electron yield and timing comparison
parallel
matrix
parallel
matrix