what we’ve learned from sno - kansas state universityneutrino.phys.ksu.edu/mand-sim/mand-sim...
TRANSCRIPT
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What We’ve Learned from SNO
History, Goals, and Overview SNOMAN specifics Performance What we’d do differently
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Sudbury Neutrino Observatory (SNO)
1700 tons InnerShielding H2O
1000 tonnes D2O
5300 tons Outer Shield H2O
12 m Diameter Acrylic Vessel
Support Structure for 9500 PMTs, 60% coverage
Urylon Liner andRadon Seal
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ν Detection in D2O
ES
CC
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ν Detection in D2O
NC
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Fit data in derived observables (R3, cosθsun, and E)
Solar ν OscillationsSNO: Signal Separation in Pure D2O
RadialDistribution(R3, RAV=1)
EnergyDistribution
SolarDirection
Distribution
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SNO Salt Phase Advantages of NaCl: Event Isotropy
How to characterize?Mean Pair Angle (θij) Legendre Terms (β14)
Two-pt Correlation Fcn Tests
γ multiplicity means PMT hit pattern for neutron events moreisotropic than for single Cerenkov electrons
simplestBetter separation+
Ease of systematic characterization Best separation
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Fit data in derived observables (R3, cosθsun, and E)
Solar ν OscillationsSNO: Signal Separation in Pure D2O
RadialDistribution(R3, RAV=1)
EnergyDistribution
SolarDirection
Distribution
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Fit data in derived observables (R3, cosθsun, and E)
Solar ν OscillationsSNO: Signal Separation in Pure D2O
RadialDistribution(R3, RAV=1)
EnergyDistribution
SolarDirection
Distribution
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SNO Neutrinos
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SNO NeutrinosHigh Energy Atmospheric ν
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– Diffusing laser source (optics/timing)– 16N 6.13 MeV γ’s– p,T 19.8 MeV γ’s– Neutrons 6.25 MeV γ’s– 8Li β’s, E<14 MeV– Encapsulated U and Th sources
Manipulator system allowsflexible source placement
Wide variety of sources forsystematic studies
SNO Calibration System
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SNO Monte Carlo and ANalysis Package(SNOMAN)
• Began as Oxford PMT simulation• Developed in parallel with 2 other SNO sims• Managed by Nick West; driven by Dave Wark• Inputs from almost entire SNO collaboration• Integrated MC and analysis tasks (e.g. fitting)• Collab. decides ca. 1990 to use FORTRAN(77)• Today, roughly ~0.5x106 lines of code• One and only simulation and analysis code
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The Iron Fist (Code Management)
Every line of code sent to and inspected by N. West cvs archive restricted to privileged developers Rules for global (public) variable names Code without external documentation is rejected Code which violates programmer’s manual is rejected Development releases ~monthly Full releases relatively rare now (2/year?)
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SNOMAN Design Goals• Testbed for detector and analysis development• Processing and reconstruction of data• Generation of pdfs for signal decomposition
•Some acceptance calculations
RadialDistribution(R3, RAV=1)
SolarDirection
Distribution
EnergyDistribution
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Simulation Philosophy: How Complete?
1. The physical model is perfect.• Cross sections and branching ratios• Energy loss• Multiple scattering• Cerenkov light generation• …
2. Bulk properties determine most of the detector response.• Extinction lengths• Scattering lengths• Reflection coefficients• …
3. Microscopic detector model preferred over phenomenological or parameterized models.• Full detector geometry (ropes, acrylic tiles, pipes)• `3D’ PMT model• Electronics efficiencies• …
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The Processor Model
Monte Carlo Reconstruction Analysis
Data Structure
Data structure is only publicly available processor data Data structure holds everything of interest (eg. All vertices) Everything within a processor is private Processors should not depend on each other except for data In some cases, can instantiate multiple processor `objects’
(Zebra used to `fake’ dynamic memory management of data structure).
Database
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Simulation Outline
Data AcquisitionTrigger and storage of data and events in detector metrics.
GenerationCreation of γ’s, e’s, and n’s from radioactive decays or ν interactions
EvolutionA. Propagation of γ’s, e’s,
and n’s and associated secondaries
B. Propagation of Cerenkov photons until detected or killed
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Geometry
Full internal and external geometry of calibration sources also modeled.
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• 8B and hep ν spectra (but not arbitrary spectra)
•Full differential ν cross sections ΩddT)(Ed
e
νσ
Generation
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propagation of e’s and γ’s using EGS4• EGS4 step size tuned to reproduce low E data• Compton scattering• bremstrahlung• pair production• Multiple coulomb scattering• Collisional losses for electrons• Bhabha and Møller• Coherent Rayleigh• photoelectric effect• annihilation
Evolution: Particle Propagation
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Cerenkov light generation
Evolution: Particle Propagation
Photon yield for a straight track:
•Interference between segments negligible
• Poisson smearing added to each
• cosθc applied to local direction• segment directions interpolated
Note: λ-dependence of thresholdonly recently added.
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• neutron propagation using MCNP (< 20 MeV)
• hadron propagation using Fluka
Evolution: Particle Propagation
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Optics: Photons propagated to next boundary• Extinction lengths of all media• Rayleigh scattering via Einstein-Smoluchowski formula:
• Fresnel reflection and refraction, including total internal• Diffuse reflection• Photon polarization effects, including QM superpositions• Energy-dependent indices of refraction• Complex indices for media-dependent speed of light
Evolution: Photon PropagationNote: `photon’ refers only to optical light
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PMT Model: Full 3D
Evolution: Photon Detection
Photon propagated until:• hit is scored• leaves the `bucket’• photon absorbed• photon is lost (approx. models)
Photon propagation includes• fresnel refraction and reflection• geometry inside PMT• photon polarization effects• Photocathode thickness
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PMT Model: Full 3D
Evolution: Photon Detection
Complex and complicated!hit probability =(1-reflect-bounce-absorb-lost)*ε(λ,cosθ)
This is hard to • measure• test• monitor• adjust pmt-by-pmt• deconvolve from other effects
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PMT Model: Full 3D
Evolution: Photon Detection
Other available models:• Black disk• Grey disk
Easy(-ier) to• measure• check• monitor and adjust
BUT! Not microscopic enough.
Optical part of PMT-by-PMT response only recently added
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t
e+ n
Data Acquisition
Convert photon hits to charges (Q), times (t), and events
At the end of Evolution stage, we have a time series of PMT hits, including all particles in event:
t
e+ n
Multiple hits on a PMT are treated separately here. Noise is then added:
t
e+ n
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Data Acquisition
Convert photon hits to charges (Q), times (t), and events
And then realistic charges and times are generated.
Q (photoelectrons) Variable gains are now simulated, but not individual efficiencies
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Data Acquisition
Convert photon hits to charges (Q), times (t), and events
Multiple hits on a PMT are then grouped:
t
e+ n
t
e+ n
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Triggering
Data Acquisition
SNO has multiple trigger types:• 100 ns `NHIT’ coincidence• 20 ns `NHIT’ coincidence• Hi and Lo Gain `Energy’ (Q) Sums• Random (5 Hz pulsed) trigger
All are (now) simulated together
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Triggering
Data Acquisition
•Triggers are analog sums over all 9500 channels.
• fully simulated: rise and fall times baseline shifts pulse amplitude variations
Time (ns)
Puls
e am
plit
ude
(mV)
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Triggering
Simulation Steps: Data Acquisition
Time (ns)
Puls
e am
plit
ude
(mV)
threshold•After threshold crossing
• hits within gate saved• trigger info latched• search for next trigger
Event Gate
A single MC `event’ can produce multiple detected events
NOTE: No digitization of data is done!
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Input from CalibrationsThe fantasy: Measure bulk optical properties +global PMT efficiency measured at (0,0,0) Physics does the rest
Attenuation using `Laserball’
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Input from Calibrations Laser source intensity not normalized:
Cannot break acrylic/H2O attenuation covariance
λatten outside D2O region is therefore phenomenological
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Input from Calibrations
3D PMT model has ~no tunable parameters!:Nothing to change if response differs from expectation
Grey disk model used for measurements3D model phenomenologically tuned to agree(Have since devised full 3D fit)
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Input from Calibrations
3D PMT model has ~no tunable parameters!:Nothing to change if response differs from expectation
Grey disk model used for measurements3D model phenomenologically tuned to agree(Have since devised full 3D fit)
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Input from Calibrations
Global Q.E. set by volume-weighted measured scale
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Performance: Energy Scale and Resolution
Number of hits for 16N γ rays MC-Data Teff
Nhit R3
On Day 1, dead-reckoning was within 3%!
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Performance: Vertex `Shift’ and Resolution
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Performance: Angular Resolution
Angular Resolution
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Performance: Isotropy
Initial tests showed MC and data means differed by ~1%
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Performance: Isotropy
Gasp! The physical model was not perfect!
Mott scattering not included correctly in EGS4
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Performance: Isotropy
Today:
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What We Could Have Done Better
Sociologically: Included non-standard code sooner? Given out development projects across collaboration? Used something simpler than Zebra or simplified it more A different language? (C++, JAVA?)
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What We Could Have Done Better
Simulation Philosophy: Better forethought about calibrations Looked more carefully at external code supplying physical models! Adhered better to microscopic philosophy (resist the urge to say “It’s small” or “It can’t matter”)
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What We Could Have Done Better
Simulation Itself: Modelled instrumental problems more completely (or cut them) Included pmt-to-pmt and channel-to-channel variations sooner Thought about speed a little more More calibrations! More user control!
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What We Did Right
• Strong code management• Documentation• Microscopic modeling when possible• Relying primarily on in-situ calibrations• Documentation• Home-brewed structure• FORTRAN within well-designed structure• Home-brewed detector components
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