What We’ve Learned from SNO

History, Goals, and Overview SNOMAN specifics Performance What we’d do differently

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

ν Detection in D2O

ES

CC

ν Detection in D2O

NC

Fit data in derived observables (R3, cosθsun, and E)

Solar ν OscillationsSNO: Signal Separation in Pure D2O

RadialDistribution(R3, RAV=1)

EnergyDistribution

SolarDirection

Distribution

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

Fit data in derived observables (R3, cosθsun, and E)

Solar ν OscillationsSNO: Signal Separation in Pure D2O

RadialDistribution(R3, RAV=1)

EnergyDistribution

SolarDirection

Distribution

Fit data in derived observables (R3, cosθsun, and E)

Solar ν OscillationsSNO: Signal Separation in Pure D2O

RadialDistribution(R3, RAV=1)

EnergyDistribution

SolarDirection

Distribution

SNO Neutrinos

SNO NeutrinosHigh Energy Atmospheric ν

– 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

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

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?)

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

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• …

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

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

Geometry

Full internal and external geometry of calibration sources also modeled.

• 8B and hep ν spectra (but not arbitrary spectra)

•Full differential ν cross sections ΩddT)(Ed

e

νσ

Generation

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

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.

• neutron propagation using MCNP (< 20 MeV)

• hadron propagation using Fluka

Evolution: Particle Propagation

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

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

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

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

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

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

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

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

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)

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!

Input from CalibrationsThe fantasy: Measure bulk optical properties +global PMT efficiency measured at (0,0,0) Physics does the rest

Attenuation using `Laserball’

Input from Calibrations Laser source intensity not normalized:

Cannot break acrylic/H2O attenuation covariance

λatten outside D2O region is therefore phenomenological

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)

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)

Input from Calibrations

Global Q.E. set by volume-weighted measured scale

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%!

Performance: Vertex `Shift’ and Resolution

Performance: Angular Resolution

Angular Resolution

Performance: Isotropy

Initial tests showed MC and data means differed by ~1%

Performance: Isotropy

Gasp! The physical model was not perfect!

Mott scattering not included correctly in EGS4

Performance: Isotropy

Today:

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?)

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”)

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!

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