coupp project internship using bubble chambers for dark matter detection using bubble chambers for...
DESCRIPTION
Introduction to Dark Matter Detection What is dark matter? Why do we think it exists? How can we “see” dark matter? What are the current leading experiments? What is dark matter? Why do we think it exists? How can we “see” dark matter? What are the current leading experiments?TRANSCRIPT
COUPP Project Internship
Using Bubble Chambers for Dark Matter Detection
Summer 2007
Overview
• Introduction to Dark Matter Detection
• Introduction to COUPP• Chicagoland Observatory for Underground Particle Physics (COUPP)
• Data Analysis Review Project
Introduction to Dark Matter
DetectionWhat is dark matter?Why do we think it
exists?How can we “see” dark
matter?What are the current leading experiments?
What is Dark Matter?
About 95% of the Universe’s mass and energy is invisible to us
DM ≈ one third
Dark Matter is matter that does not emit or reflect electromagnetic radiation
Therefore, except for gravitational effects, it is functionally invisible
Dark Matter is one hypothesis explaining several cosmological challenges
Why do we think DM exists?
Galactic Dynamics:For stars to move with velocities they have, there must be far more mass (to keep in orbits)[Vera Rubin, Fritz Zwicky]
Weak Lensing:Photons are deflected by a gravitational field, so clumps of matter will cause distortions in the appearance of galaxies
Cosmological Structure:Slow-moving dark matter appears necessary to generate galaxies and large-scale structures (need fluctuations)
Universe’s Expansion:Inflation demands Universe have critical density, but visible mass accounts for considerably less than this
Why do we think DM exists?
The Bullet Cluster:One idea is simply to modify gravity at large scale
Why not just modify gravity?
Collision between two galaxy clusters with hot gas
Hot gas (red) slowed by drag force, while dark matter (blue) not slowed by impact
If hot gas most massive component (per alternative gravity theory), this would not occur
*
*
*
*Separation of dark matter
and the gas
How can we detect dark matter?
Direct DetectionIndirect DetectionWe know WIMPs can collide with each other, producing neutrinos or gamma rays
Gamma rays produced as a factor of density squared -- look for high density of DM (center of galaxy)
So look for neutrinos and gamma rays -- because WIMPs really high energy, look for GeV energy gamma rays and neutrinos
WIMPs can also collide with target nuclei
Set up experiment to watch for WIMP collisions with target nuclei
What are the current leading experiments?
CDMS Xenon 10
(Direct Detection)
Cryogenic Dark Matter SearchTo reduce noise :
Very coldBottom of Soudan Mine(neutrons produced from atmospheric muons)
Phonons -- tiny increase in heat in single cold Germanium crystal
Phototubes in liquid Xenon
Surpassed CDMS early 2007
Ionization
Look for ratio of:
Look for ratio of ionization to scintillation signal
Introduction to COUPPMethods of Detection
DesignAdvantages over
Competitors
Methods of Detection
Heavy
heavy target nucleus
Dark matter particle from galatic halo
nuclear recoilEnergy 1-100 keV
Design
Liquid, temperature and pressure tuned so
that WIMP must provide majority of
energy to form bubble
Advantages of Bubble Chambers
• Low cost• Easily reaches large sizes
• Low energy thresholds for nuclear recoils
• Backgrounds ( and ) easily suppressed [run at low pressure]
fairly convention pressure vesselcommercial partsprimary cost associated with maintaining cleanness
Heat is tuned (low enough) to not allow bubble formation by gamma or beta particlesAlso why runs for extended period of time
Because sufficient degree of superheat
Advantages of Bubble Chambers
•Variety of target nuclei
• CF3Br• CF3I• C3F8• Xe• etc.
•Neutron backgrounds can be measured by multiple bubble events
Neutrons bounce Some fraction produce more than one bubbleSource of neutrons included to simulate neutron Important b/c lack of muon shielding (except eriks)
Different kinds of dark matter interact differently with different target atoms/nuclei
Data Analysis Review Project
ProblemMethod of Assessment
Results
Problem
With what accuracy does the current method of data analysis report the radon levels in the bubble
chamber?
Method of Assessment1) Develop Monte Carlo to simulate the real data2) Analyze MC data using the project’s data analysis
methods (Maximum Likelihood method of fit)3) Determine the fraction of bubble counts that data
analysis would attribute to Radon4) Compare the data analysis fraction to the fraction
actually input into the Monte Carlo
The bubble chamber is contaminated with Radon. This results in a significant background count.
Monte Carlo Simulation
• Must mimic Radon decay chain as well as “other” (suspected Dark Matter) component
• Bubble chamber will not detect any bubble formation within 30 seconds of a previous bubble
• Amount of “other” component relative to Radon must be easily adjusted (to be looped)
• Run quickly (very large time loops) to mimic actual week long data runs
Constraints
What occurs in the bubble chamber?
1. Radon enters, probably through “O-rings”, moves around, even through plastic, in liquids, etc.
2. Beta decays invisible, but alpha decays produce bubbles..
3. Alpha particle emission for:Radon 222 to Polonium 218
Polonium 218 to Lead 214
Polonium 214 to Lead 210
Unless tens of years, only these relevant
COUPP’s Data Analysis MethodMaximum Likelihood Method
for a sum of exponentials• We suspect that there are two
primary components to the data• Radon• Other -- (simply not Radon, may include dark matter)
• Radon has a known half life that is short enough to be highly visible in bubble chamber data
• Fit two exponentials• one is the Radon component (known exponential decay)
• the other component has decay given by the fit
• Three free parameters• two coefficients, one exponential power
Time difference in seconds
Num
ber o
f Eve
nts
Time difference in seconds
Num
ber o
f Eve
nts
Run data vs. Monte Carlo
Time difference in seconds
Num
ber o
f Eve
nts/
0.5(
min
)
Monte Carlo Simulation for COUPP Bubble Chamber data
Check Minimization of the Likelihood Function
Visual check (approximate) of minimization of likelihood function(for one free parameter)
Radon Fraction• Can determine Radon
component because of known 3.1 minute half-life
• Expect Radon to decay with known exponential curve
• To determine number of decays, integrate under curve
• For each Radon decays, two decays will later occur
• So for each bubble we label as 3-minute-Radon, we expect two additional triggers
3 x 3-minute-Radon
Total Number of Triggers
Radon Fraction =
Comparison of Data Analysis and Actual
Radon Fraction
Create a loop, inputting a variety of Radon Fraction values
• Percent error between the given Radon fraction and the calculated value
• Deviation within each fraction calculation
Goal:
Output:
alpha = 0omega = 15gap = 1for z = alpha:gap:omega Z = z+1a = Fraction_Repeat_PDM_Loop(z)% Now we fit to a Gaussianxmin = 0;xmax = 1.5;ymin = 0;ymax = 40;bingapP = .025;gauss_data = a;bin_sizeP= 0.5:bingapP:xmax;n_elementsP = histc(gauss_data,bin_sizeP);nmax = n_elementsP(n_elementsP>(n_elementsP-(.1*z)))del_TP = xmax/bingapP;mu(Z) = mean(a);sigma(Z) = std(a);j = 0:.01:1.25chi = (1/((sigma(Z))*((2*pi)^(1/2))))*…
(exp(-((j-(mu(Z))).^2)./(2*((sigma(Z))2))))%FIGURE 2figurehist(a)hold onplot(j,chi)
Results
Mean valueMean value of Radon fraction calculated by the analysis is fairly accuratefairly accurate for high Radon fractions -- for low values, problematic
VarianceVariance, however, can be quite largequite large, with values often at seven to eight percent of the actual Radon fraction (for high Rn fractions)
Analysis underestimates for lowunderestimates for low Radon fraction values and overestimates for higheroverestimates for higher Radon fraction values
Radon Fraction
Estimated Radon Fraction Estimated Radon Fraction
Estimated Radon Fraction Estimated Radon Fraction
= 0.0359 = 0.0441
= 0.0422 = 0.0349
Radon Fraction
Estimated Radon Fraction
Num
ber o
f run
s est
imat
ing
a gi
ven
Rad
on fr
actio
n
= 0.0422
Actual vs Estimated Radon Fraction
Actual Radon FractionEstimated Radon Fraction
Bias
BiasOne interesting aspect to note in the representation of the bias is that the data analysis underestimates the Radon fraction at low values and overestimates the Radon fraction at high values.
Variance
Variance
VarianceIncluding low Radon
fraction
VarianceExcluding low Radon
fraction
Conclusions
Analysis method has sufficient accuracy, but is dependent on the Radon fraction
Considerable variance in individual runs from the mean, so experiment must conduct many runs, to ensure that an accurate mean is determined
Low Rn fraction unlikely, given actual data, so accuracy good
Questions