Transcript Document 7447664

Geant4 Milagro
Simulation
Vlasios Vasileiou
U. Maryland 5/5/2006
Simulation components
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The software
Corsika – Air shower simulation
 Geant4 – Simulation of the detector’s response to
the EAS particles reaching the ground
 Milinda – analysis software (among others: adds
noise, performs PMT corrections, add time jitter and
smears number of pes)
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Corsika
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Simulates the air showers
Variety of hadronic physics models available
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We use
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Low energy hadronic model
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Fluka 2005 (previously used Gheisha)
High energy hadronic model
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Nexus 3 (started using it in the latest Corsika v6.500 with g4sim v2.0). Previously
used Venus
neXus (NEXt generation of Unied Scattering approach) is a common effort of the authors of
VENUS and QGSJET with extensions enabling a safe extrapolation up to higher energies, using the
universality hypothesis to treat the high energy interactions . It handles nucleus-nucleus collisions
with an up to date theoretical approach.
Corsika
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Curved atmosphere (started using it after g4sim v1.2)
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In the default version of corsika a plane atmosphere is used and the thickness
increases as 1/cos(θ). The accuracy of this approximation decreases with θ
(for θ=90 deg the thickness of the planar atmosphere reaches infinity).
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Corsika doesn’t allow using θ>70 deg unless a curved atmosphere is selected.
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How it works: In the curved version the Cartesian coordinate system is kept
but the horizontal step size is limited to <20 km. Longer transport distances
are divided into appropriate segments to be treated in a local flat atmosphere.
After each traversed segment the particle coordinates are transferred into the
next local Cartesian coordinate system with its vertical axis pointing to the
middle of Earth. Thus the curved Earth's surface is approximated piece by piece by flat
segments with limited horizontal extensions.
Corsika settings
Particle
Max θ
Spectral index
Energy Range
Available
nShowers
(mil)
p
70
-2.75
30GeV to 100 TeV
~500
He
70
-2.75
30GeV to 100 TeV
~250
γ
45
-2.4
30GeV to 100 TeV
~315
Geant4
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Geant4 is a simulation toolkit from CERN
created for the simulation needs of the LHC.
Biggest advantages
Written in C++ and thus easy to expand & debug.
 Wide international collaboration
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Far greater functionality and number of physics models
than geant3
 New and more accurate physics models
 Fewer bugs
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g4sim
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Milagro simulation code based on Geant4
Main code written by V. Vasileiou (it’s all my fault...)
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C. Lansdell and A. Smith helped with debugging
First beta version out in Summer 2004.
The detector model in the first version of g4sim was
almost identical to the one in the g3 version.
Slowly, I rewrote all the code from scratch
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Using my own sources for the properties of the detector
elements (instead of the numbers in g3).
Rechecking the sizes of the detector elements
Using a different PMT model
g4sim: Geometry
The pond
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Source of dimensions a pond
schematic from Peter Nemethy.
Support for water over the cover &
air under the cover.
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Currently using 1cm of water over
the cover and no air under the
cover.
The layer of air under the cover is
uniform all over the water  not
a good approximation.
In the g3 sim there is no physical
cover included in the simulation
g4sim: Geometry
The pond
Cover & pond liner
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Polypropylene
Diffuse reflector with incidence angle dependent reflectivity.
Calculated from Fresnel’s equations by Geant4
Have to provide refractive index: 1.49
Increased reflectivity from the white pipes at the bottom of the pond not included in the simulation.
Reflectivity of a flat Water Polypropylene
interface for non polarized light.
g4sim: Geometry
The pond cover
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Possible problem: Reflectivity from Fresnel’s equations and the results from ’94 David
Schmidt’s Milagro memo disagree. UV reflectivity of polypropylene is very low.
More reflections from the cover  We trigger more easily by weaker showers. Nhit, npe, mxpe,
nfit distributions slightly move to lower values.
Reflectivity of polypropylene (by D. Schmidt) versus
the reflectivity of a material assumed to be a perfect
reflector. Measurements correspond to some
unknown incidence angle.
Reflectivity of a flat Air Polypropylene
interface for non polarized light from Fresnel’s
equations.
g4sim: Geometry
The baffles
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Baffle dimensions taken from a schematic from M. Schneider
Bug in g4sim v1.2
 Inside part of baffles had the properties of tyvek
 True inside part of baffles is made out of the of unknown composition material
called “Liner” in D. Schmidt’s memo.
Currently in g4sim v2.0
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Liner is a diffuse reflector with the reflectivity measured by D. Schmidt.
Outside part of baffles: Diffuse reflector with the properties of polypropylene
g4sim: Geometry
The outriggers
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Dimensions of outriggers taken from a
schematic by Tony Shoup.
They are not all on the same plane (in g3 they
are)
They don’t all contain the same amount of
water.
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In g4 sim their water height takes random values
around a mean value.
76cm ± 10cm
(Thanks to Scott for measuring the water levels of
several outriggers.)
g4sim: Geometry
The outriggers
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Inside part of outriggers lined with tyvek
Reflections from tyvek a mixture of specular and diffuse
The values of Geant4’s parameters that describe the reflections
from Tyvek (sigma_alpha and specular lobe constant) were taken
from Auger notes.
Tyvek reflectivity taken from D. Schmidt’s memo
Water properties
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Water absorption and scattering lengths
 In g3 sim (v3.2 pro?) 18m Absorption length and no scattering
 In g4 sim (v0.99 – v1.3), 18m abs length and Rayleigh scattering were mainly
used. Simulated other abs lengths too but 18m was always the pro.
 g4sim v2.0
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Has both Mie and Rayleigh scattering
Two configurations were mainly used:
 Absorption Length 27.4m and Scattering Length 56.8m (Att. Length 19m).
Source was an email from Don Coyne to the milagro mailing list dated
5/4/2004.
 Absorption length 30m and Scattering length 50m.
Water properties
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Rayleigh Scattering – caused be scattering centres smaller than λ/20
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Rayleigh scattering length increases with λ4 and scattering angular distribution
goes like ~(1+cosθ2).
Forward and backward scattering probability equal.
Rayleigh scattering very long
Geant4 has code to simulate Rayleigh scattering. Calculates the lengths just for
water using the Einstein-Smoluchowski formula
Water properties
Mie scattering
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Mie Scattering
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Geant4 doesn’t include code for Mie scattering, so I wrote some code to simulate this process
Mie’s angular distribution function is hard to solve
If your scattering centers have similar properties (rindex and size) or if your scattering is
dominated by a single species of scatterers 
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you can approximate the Mie complicated functions that give the scattering angle distribution with a
simple function: the Henyey-Greenstein function
Needs only one parameter; the asymmetry factor (g=<cosθ>)
Mie scattering in g4sim v2.0 MC
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In the March 2004 Los
Alamos meeting Don Coyne
presented results of his water
scattering measurements.
Showed a plot of relative
intensity vs scattering angle
His results would agree with
a Henyey-Greenstein function of
<cosθ>=0.9999. Extremely
forward scattering.
For the g4sim v2.0 MC, I used
<cosθ>=0.99
Differential (top) and integral (bottom) plots of the Mie scattering angle distributions for
different asymmetry factors.
Water properties
Mie scattering
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The asymmetry factor can be calculated if the refractive index and the size of the
scatterers is known.
There is a program called MiePlot that performs these calculations (if only we
knew what scatters our light in the pond).
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My impression
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Air bubbles, Al2O3, other stuff ?
We will never know what exactly is in our water
For now, just use a conventional very forward asymmetry factor and if any one
performs a reliable measurement of the water scattering angular distribution in the
future, then try to change things.
For this version of the Mie scattering code
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There is no energy dependence of the asymmetry factor
No back-scattering (easy to add, thought about it after I generated all this data)...
PMT properties
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Geometry
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g4sim v>1.2 uses a full optical model of the PMT
Taken from GLG4SIM generic Geant4 application (G.
Horton, D. Motta et al) and modified and bug fixed for
Milagro.
PMT model
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The PMT model in the G4 Milagro simulation
tracks the photons until they are converted to
photoelectrons or until they are absorbed upon
incidence on a surface or until they exit the
PMT.
It simulates reection/refraction/absorption at
the glass, photocathode, dynodes and silvered
surface of the PMT.
The absorption probability of a photon
transversing the photocathode material is
calculated using its complex refractive index and
thickness.
The probability of a photon detection is a
product of the probabilities of the following
steps:
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The absorption of the photon in the
photocathode material and the resulting creation
of a photoelectron
The liberation of the photoelectron in the PMT
vacuum
The collection of the photoelectron at the 1st
dynode
PMT model
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The absorption probability is be calculated from the complex
refractive index of the photocathode material and depends on
the energy and the incidence angle.
Some of the photoelectrons produced will be liberated into the
vacuum and later collected by the 1st dynode.
The probability for a liberated into the vacuum photoelectron to
be properly collected by the 1st dynode is called the Collection
Efficiency of a PMT (CE).
For the PMT model in the simulation the CE is 1; all liberated in
the vacuum photoelectrons are detected. And collection
efficiency effects are applied later in milinda.
PMT model
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Let the liberation probability of a photoelectron
into the vacuum be called LP.
The Quantum Efficiency (QE) of a
photocathode is defined as the ratio of liberated
into the vacuum photoelectrons over the
incident on the photocathode photons.
If the probability of a photon being absorbed in
the photocathode is called A then
QE = A*LP (1)
PMT model
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The LP is only energy dependent, while the A and QE are both
energy and incidence angle dependent.
The QE data from the spec sheets corresponds to normal
incidence, let's call it QEn(E).
The absorption probability at normal incidence, An(E), can be
calculated using the complex refractive index and the thickness
of the photocathode material.
So the LB(E) can be calculated as
LB(E) = An(E)/QEn(E) (2)
Deriving LB(E) from (2) and by calculating A(E,θ) we can derive,
using (2), the angular dependence of the quantum efficiency.
PMT tests
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I tested some Milagro PMTs
Mainly, made pulse height distributions for
different conditions and measured the relative
detection efficiency on the surface of the PMT
PMT gain and detection efficiency decrease as
we illuminate points far from the top of the
photocathode.
I sent a memo out (5/1/2006) about these tests.
PMT tests
Pulse height distribution
for illumination at
different positions of the
photocathode .
PMT #1024, HV 1800V,
PMT vertical
PMT tests
PMT tests
PMT tests
Magnetic field effects
PMT illuminated at the top
PMT tests
Relative detection
efficiency
Normal illumination at
different points of the
photocathode.
PMT #1024, 1800V,
vertical position
PMT tests
Relative efficiency vs incidence angle. Illumination of the top of the photocathode under
different angles
Milinda’s initial processing of the MC event
Noise addition
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The MC event is read by DataRead_MCASCII5
AddNoise code superimposes noise to the events
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Cosmic ray noise
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Dark noise
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Simulated 5 GeV – 100 TeV , 90deg zenith angle protons thrown uniformly
on the pond with cores distributed to +-5km. Kept events with any pmts hit
and at most 10AS PMTs hit. Saved in the config_milagro/noise.dat file
Milinda adds, randomly in time, events from this file until the hit rate of the
AS tubes becomes 20KHz.
2KHz for Pond PMTs and 20KHz for Outrigger PMTs.
There maybe another source of dark noise in our pond.
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Relative rate of muons to single pe hits doesn’t seem to agree between MC
and data for these noise rates.
Muon Peak
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Number of pes a muon produces on the MU layer PMTs
Muon peak from data
Move the time window of the edge-finder off time,
where the pond hits come from other unrelated
showers.
Muon peak from the MC
Analyze non triggering data
Milinda’s initial processing of the MC event
PMT gain and efficiency corrections
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Milinda code was written to apply the PMT efficiency
and gain corrections.
In g4sim v2.0 the position (distance from PMT axis) of
each pe detection is saved in the output file.
Milinda’s code GetPEAmp.c
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Discards some of the MC pes based on the position
dependent PMT efficiency
Assigns a pulse height to the surviving pes using the pulse
height distributions as a PDF. (Does the same for the dark
noise hits, using a dark noise pulse height spectrum)
Milinda’s initial processing of the MC event
Some notes
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If g4sim v1.2 or earlier is used
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Can’t have PMT corrections applied. No positions of the pe
detections are saved in the MC file.
If you were using code with the eventdata bug
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Using Milinda.Reco.Event.EventData->CalData instead of
Milinda.Reco.Event->CalData
You bypass the calibration process
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For g3 and g4sim v1.2-v1.3, you get pe smearing, time jittering with the
old g3 method. No noise or calibration on the events
In g4sim v2.0 I stopped saving all the pieces of information that milinda
would calculate (time jitter and pe smearing info).
So for g4sim v2.0, you would get no time jittering, no pe smearing
(integer number of pes – nb2&X2 definitely wrong), no noise, no PMT
corrections, no calibrations, no 2.6 spectrum on the crab
Further reading
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“Results from the Geant4 Milagro Simulation”, Milagro Memo, V. Vasileiou,
04/15/2006
“PMT Tests at UMD”, Milagro Memo, V. Vasileiou, 05/1/2006
“An electronics simulation and improved noise model for Milagro”, Milagro Memo, A.
Smith, 2/4/2004
g4sim webpage
http://umdgrb.umd.edu/vlasisva/g4sim
Milinda manual
http://umdgrb.umd.edu/milinda/doc/manual/MilindaManual.html