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Introduction
History
Utilization
Physics
Grid
Mass
Future
PAN 2012
NUCLEAR THEORY
Theoretical / Computational
Nuclear
& Nuclear
Energy
 History
of
&
HistoryPhysics
of physics
physics
& computing
computing
 Use
Use of
of computers
computers in
in the
the physical
physical
Wolfgang Bauer
sciences
sciences
Department of Physics and Astronomy
 Examples
from
Examples
from physics
physics
National Superconducting Cyclotron Laboratory
 Computers
at
interface
of
ComputersInstitute
at the
the for
interface
of
Cyber-Enabled
Research
biology,
and physics
physics
biology, chemistry,
chemistry
and
Michigan
State
University
 Use
Use of
of the
the Internet
Internet for
for computing
computing
Teaching with the Computer
 Teaching
“The Road
Road Ahead”
 “The
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Introduction
History
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Grid
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Nuclei are REALLY small
 Atoms are really small
• Typical atomic size: ~10-10 m
• Put 10,000,000 atoms in a row: thickness of your fingernail
• Best (scanning tunneling) microscopes are just good enough
to resolve individual atoms
 Nuclei are another factor
100,000 smaller
• Typical nuclear size:
~10-15 m
• Nucleus inside an atom is
like a golf ball in a football
stadium (but contains
almost all of the mass!)
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Introduction
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How do we learn about nuclei?
 We hit the nuclei (with other nuclei or elementary
particles or gamma rays) and watch what happens.
 Nuclear processes require high energy (> 1 MeV)
• More than 100,000 times the energy of chemical processes
 Nuclear processes last a very short time (<10-21 s)
• A billionth of a billionth of a millisecond!
• Once the nuclear reaction products are detected, the
reaction has long been over
• Direct observation of nuclei and nuclear processes is
impossible
 We need theory to understand and model the
experimental results and thus learn about nuclei!
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Introduction
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Nuclear Models
 Liquid drop model
• Explains basic nuclear masses
 Fermi gas model
• Independent particle motion mainly governed by Pauli
principle
 Shell model
• Similar to atomic shell model
 Nuclear physics requires quantum mechanics,
thermodynamics, fluid dynamics, transport theory,
theory of phase transitions, complexity and chaos
theory, …, and COMPUTERS
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Introduction
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Complexity in Many-Body Systems
Mesoscopic Systems: Computers
become essential
logC
Np
¥
2
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Introduction
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(very abbreviated) History of Physics
1700s: Newton invents calculus
to describe mechanics
1800s: Faraday et al. study
electricity&magnetism in experiments
1900s: Theoretical
physics (Planck,
Einstein) explores
the quantum world
2000s: Computational physics emerges
as third branch of physics (von Neumann)
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time
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History of Computers (Moore’s Law)
Computer speed doubles every 18 months (Moore’s Law)
Data storage doubles every 12 months
Network speed doubles every 9 months
Improvement in last 20 years
• Computers: x
2,500
• Storage:
x 130,000
• Networks:
x 6,600,000
 Inventions in last two decades:
GPS, cell phones, laptops, iPods,
DVD, BluRay, HDTV, 3d-TV, …
Moore’s Law vs. storage improvements vs. optical
 Physics limits not to be
improvements. Graph from Scientific American (Janreached for another decade
2001) by Cleo Vilett, source Vined, Khoslan, Kleiner,
Caufield and Perkins.
or more




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History of Computers (Speed Record)
“Kei” (Jp.)
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= 1016
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Introduction
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Physics
History of Computers
1946: ENIAC
Grid
Future
Driven by demand from and
inventions by physical scientists!
2000: 100 million
1947: Transistor (Bardeen, transistors in each PC chip
Brattain, Shockley)
1989: WWW, Berners-Lee, CERN
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Mass
W. Bauer
2004: BlueGene
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Use of Computers: Email & Office Software
For all of us:
 significant fraction of our workday
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Use of Computers: Programming
Languages:
 FORTRAN
 C(++)
 Java
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Use of Computers: Symbolic Manipulation
Programs:
 Mathematica
 Maple
 MathLab
Real Mathematics
Research:
e.g. Kepler Conjecture
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Use of Computers: Data Collection
Programs:
 Mathematica
 Maple
 MathLab
Real Mathematics
Research:
e.g. Kepler Conjecture
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Use of Computers: Visualization -> Insight
Collection: D. Dean, ORNL
Accelerator
design
Multiscale
model of
HIV
Type IA supernova explosion
(BIG SPLASH)
Atmospheric models
Fusion Stellarator
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Materials: Quantum Corral
W. Bauer
Nuclear Physics: STAR event (G.D. Westfall)
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Introduction
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Use of Computers: Enabling Science
Three high-tech buzzwords:
Progress in
relies on advances in
And both are dependent on
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Computational Nano-Science
 Prediction of materials’ structures
and properties
 Ab initio calculations of quantum
forces between atoms
 Density functional theory
 Example 1: Carbon pea-pod
memory
•
U.S. Patent 6,473,351
 Example 2: Time dependence of
buckyball fusion
 Calculations done with Earth
Simulator
David Tomanek, MSU-PA
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Computational Nuclear Physics
 Big questions:
• How are the heaviest elements
made in the universe?
• What is the equation of state of
nuclear matter?
 Experimental Facilities
• NSCL, (future) FRIB
 Computational
Tools
• Transport
Theory
• Reaction
Networks
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Computational Astrophysics
 Astrophysics has to answer questions
without any chance of doing
experiments
 Running computer simulations and
comparing their output to static
observations is only path to progress
E. Brown, with Flash Center, Chicago
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Computing for Data Reduction
ATLAS
Data rate: 40 MHz, 40 TB/s
Level 1 - Special hardware
75 kHz, 75 GB/s
Level 2 - embedded processors
5 kHz, 5 GB/s
Level 3 - dedicated PCs
100 Hz, 100 MB/s
Large Hadron Collider
@ CERN (>2008)
Data storage and offline analysis
ATLAS: ~10 PetaByte/year
(~100,000 PC hard drives of 100 GB)
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Introduction
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Data Streams for Different Experiments
Event Rate (Hz)
High Level-1 Trigger
Data Rates for
(1 MHz)
High Energy
106
Physics
LHCB
ATLAS
Experiments
CMS
PHENIX
5
Future
Current
MSU
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High No. Channels
High Bandwidth
(500 Gbit/s)
10
104
KLOE HERA-B
TeV II
STAR
CDF/D0
103
102
104
H1
ZEUS
High Data Archive
(PetaByte)
ALICE
NA49
UA1
105
106
107
Parts from:
Hans Hoffman
DOE/NSF
Review, Nov 00
Event Size (bytes)
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Computing with the Internet: Grid
MSU/UM ATLAS Tier2 facility
Graphic: http://rtm.hep.ph.ic.ac.uk/webstart.php
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 Nuclei consist of Z protons
and N neutrons
 Mass number
A=Z+N
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Source: Bauer & Westfall 2010
One example: Nuclear Mass
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One example: Nuclear Mass
 Energy and mass are related
 Nuclei weigh less
than the sum of
the masses of the
protons and
neutrons in them.
 Nuclear binding!
 Lets do a little bit
of nuclear theory
and see how far
it gets us …
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Al, here is how I see it:
E=mc2
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One example: Nuclear Mass
experimentally
measured binding
energies
Source: Bauer & Westfall 2010
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One example: Nuclear Mass
Fission - Example
Energy release due to difference in binding energies
Chain reaction!
Source: Wikipedia
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Introduction
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One example: Nuclear Mass
Source: Wikipedia
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One example: Nuclear Mass
Alternative
Hydro
Nuclear
Fossil
14
12
Source: Bauer & Westfall 2013 (!)
Data Sourec: US EIA
World Power Production (TW)
16
10
8
6
4
2
0
1970
1980
1990
2000
Year
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One example: Nuclear Mass
March 11, 2011: Fukushima Daiichi
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One example: Nuclear Mass
Fusion - Example
Energy release due to
difference in binding energies
Source: SOHO spacecraft, NASA
Source: ITER, Bauer & Westfall 2010
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Introduction
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One example: Nuclear Mass
 Can we understand this?
 Strong force is short-ranged; nucleons only interact
with nearest neighbors
 No neighbors at the surface
 Add Coulomb repulsion between protons (+ …)
B( A,Z) = Bv ( A,Z) + Bs ( A,Z) + Bc ( A,Z) + Ba ( A,Z) + Bp ( A,Z)
Z
= av A - as A - ac 1/3 - aa
A
2
2/3
-1/3
B( A,Z)/A = av - as A
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( Z - A)
1
2
A
N
-1
+
(-1)
( )
Z
2
- ap
æ Z 1ö
(
Z
- ac 4/3 - aa ç - 2 ÷ - a p
èA ø
A
2
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A
Z
-1 + (-1) N
)
A3/2
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One example: Nuclear Mass
Source: Bauer & Westfall 2010
 Yep, it works!
N
-1
+
(-1)
æ
ö
(
)
Z
Z
B( A,Z)/A = av - as A-1/3 - ac 4/3 - aa ç - 12 ÷ - a p
èA ø
A
A3/2
2
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2
Z
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Introduction
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Grid
Mass
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One example: Nuclear Mass
 Modern picture: on closer examination differences to
mass formula are revealed
Phys. Rev. C 69, 037304 (2004): Hirsch et al.
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One example: Nuclear Mass
 Huge national effort to
find better energy density
functional
 Leader: George Bertsch,
UW
 MSU has big part in this
multi-million $ effort
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Future
Predictions
 Predictions are hard …
• “Prediction is very difficult, especially about the future”
(Niels Bohr)
 But still useful …
• Predictions are like Austrian train schedules. Austrian trains are always
late. So why do the Austrians bother to print train schedules? How else
would they know by how much their trains are late?
(Viktor Weisskopf, paraphrased)
 So here we go …
• Moore’s Law will continue for at least another 2 decades
• Network bandwidth will become infinitesimally cheap and eventually
(~2 decades) saturate the human input bandwidth
• Caution 1: “Software is a gas” (Nathan Myhrvold)
• Caution 2: Growth in content will only be linear, not exponential
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Summary
 Physical science has provided technological
innovation to advance computer science and will
continue to do so
 Computers have enabled a third branch of physical
science (besides theory and experiment)
 Nuclear theory is now very dependent on large-scale
computer use
 Nuclear theory provides the only link between
experiments and understanding in nuclear physics
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