5/15/06H. Ray : Pheno 06

MiniBooNEResults worth waiting for

Heather Rayhray@fnal.gov

Los Alamos National Laboratory

5/15/06H. Ray : Pheno 06

Outline LSND : MiniBooNE motivation MiniBooNE Experiment Why we’re waiting to open the box Improving the Optical Model Improving identification of mis-id 0

Particle ID Algorithm

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LSND : The Great Mystery

1st accelerator expt to observe osc signal 3.8 excess of anti-e in an anti- beam Incongruous with rest of osc results Other expt have explored LSND phase space but allowed

regions still remain

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MiniBooNE

8 GeV proton beam 1.6 s pulse, 5 Hz rate

from Booster p + Be mesons

Mesons focused by magnetic horn

Mesons DIF E ~ 500 MeV

Primary (protons) Secondary (mesons)

Tertiary (neutrinos)

800 Ton, 12 m diameter sphere

Non-doped mineral oil Two regions

Inner light-tight region, 1280 pmts (10% coverage)

Optically isolated outer veto-region, 240 pmts

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Why the Wait?The oscillation signal is expected to be

small Probability for LSND oscillations = 0.264%!Need to know backgrounds, detector response

very preciselyRequires a well-developed, sensitive Particle ID

algorithm, exact optical model, solid identification of mis-ID backgrounds

“Why not borrow the optical model from another mineral-oil based neutrino experiment?”

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Why the Wait?

No other expt uses non-doped mineral oil We’re the first to study, model, and simulate

interactions in pure mineral oil Scintillator fuzzes out rings, ruins separation SNO/Super-K : H20, no fluor/scint, all Cerenkov LSND : all scintillation (swamped fluorescence), some

Cerenkov MB : in the middle, need to untangle various components

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1st HurdleThe Optical Model

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The Optical ModelFull battery of external measurements to

provide complete picture of OMProblem! How do you set the relative

normalization from one measurement to the other? (ie ratio of fluorescence to scintillation)

Need internal calibration sources / tank data to provide correlationsWe do not tune on any samples which may

bias the oscillation analysis

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External Measurements

Variety of stand-alone tests which characterize separate components of mineral oil

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Internal Calibration Sources

Muon tracker + cubes : provides and Michel e- of known position and direction in tank, key to understanding E and reconstruction

Laser flasks (4) : used to measure tube charge, timing response

Neutral Current Elastic sample : provides neutrino sample, protons below Cerenkov threshold == isolate scintillation components, distinguish from fluorescence of detector

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The Optical Model ChainExternal Measurements and Laser Calibration

First Calibration with Michel Data

Calibration of Scintillation Light with NC Events

Final Calibration with Michel Data

Validation with Cosmic Muons, CCQE, e NuMI, etc.

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Recent Improvements

Improvements to OM greatly improve Michel electron E as a function of location in our detector

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Impact of Improved OMScintillation light in 1st gamma in pi0 fitter

Distance between pi0 vertex and 1st gamma conversion point

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2nd HurdleIdentifying Mis-IDs

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Minimizing Mis-IDs 83% of all

mis-ID backgrounds come from events with a single 0

Need sample of pure 0 to measure rate as f(momentum)

High-P region very impt. to get a handle on high-E e bgd from K+

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3rd HurdleParticle ID

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Sensitivity EstimateGood sensitivity

requires PIDRemove 99.9% of

CC interactionsRemove 99% of all

NC 0 producing interactions

Maintain 30-60% efficiency for e interactions

LSND best fit sin22 = 0.003 m2 = 1.2 ev2

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Particle ID Algorithm Using a boosted decision tree

Similar to a neural net, but better Needs to be trained on a set of variables Want vars which are powerful at distinguishing between

signal, background event types Have a large list of potential inputs Require data & MC shapes to agree for an input

to be considered for training The more vars with agreement, the larger set of

powerful vars we’ll have to draw from, thus providing a more powerful PID algo

Nuc.Inst.Meth.A 543 (2005) 557-584Nuc.Inst.Meth.A 555 (2005) 370-385

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PID Inputs

Calibration Sample

Signal-like Events

Primary Background

Mean = 1.80, RMS = 1.47Mean = 1.19, RMS = 0.76

Mean = 20.83, RMS = 25.59Mean = 3.48, RMS = 3.17

Mean = 16.02, RMS = 25.90Mean = 3.24, RMS = 2.94

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SummaryWe are moving forward in leaps and bounds!

Past 6 months have brought phenomenal improvement in our Optical Model

Agreement in PID potential inputs vastly improvedNew pion fitter offers better resolution of single 0

events, reductions in mis-id backgroundsThese improvements are vital to maximizing

our sensitivity to LSND (Remember, Probability for oscillations = 0.264%)

We are not done yet. Improvements are continuing - hope to open box this summer

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BACKUP INFO

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NN vs TreeThe Elements of Statistical Learning, Hastie, Tibshirani, Friedman, Springer (2003) Neural Nets TreesNatural handling of data of “mixed” type Bad GoodHandling of missing values Bad GoodRobustness to outliers in input space Bad GoodInsensitive to monotonic transformations of inputs

Bad GoodComputational scalability (large N) Bad GoodAbility to deal with irrelevant inputs Bad GoodAbility to extract linear combinations of features

Good BadInterpretability Bad FairPredictive power Good Bad

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Decision Trees

Unstable - large trees have high variance Mitigate this by using a

collection of trees (boosting)

Don’t capture additive structure well Use sensible choice of

input vars

Good Performance Low Bias Training is easy, does

not depend on minimization procedure

Immune to effects of outliers

Resistant to effects of inclusion of irrelevant input vars

Pros Cons

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Why Boost a Tree?You can boost anything - tree, neural

net, etc.Boosting combines weak classifiers

to produce a powerful committeeClassifiers are combined through a

weighted majority vote to produce the final output

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Boosted Trees

Inherits pros of single trees

Dramatic performance improvement

Low bias, low variance Less susceptible to

overtraining

More of a black boxIncreases

sensitivity to outliers and noisy data

ConsPros

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Boosted Tree FalsehoodsBoosted trees are NOT robust against

data to MC disagreementWe must have good data to MC

agreement for an input to be used in training

Boosted tree performance does NOT improved with the number of input variables

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Osc e

MisID

e from +

e from K+

e from K0 e from +

e from K+

Use High energy e and to normalize

Use Kaon production data for shape

Need to subtract off misIDs

Full data sample ~5.3 x 1020 POT

High energy e data Events below ~1.5

GeV still in closed box (blind analysis)

Determining Backgrounds with MiniBooNE data

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Why the Wait?We don’t have 2nd detector so we can’t do

flux cancellationWe need to know the neutrino production

mechanisms much more precisely than past expts have neededRely on data from external expts : Harp thin

target results recently added to MiniBooNE MC (April ‘06)

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Checking PID with NuMI Events Because of the off-axis angle, the beam at MiniBooNE

from NuMI is significantly enhanced in es from K+

Enables a powerful check on the Particle ID

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Optical Model MB is very unique = mineral oil with no scintillator Solar nu : Genius = Gd, Moon = liq Ar, Heron = liq He, SNO

= heavy H20, Homestake = Cl, Sage = Ga, Ge, Xe, GNO = Ga, Gallex = Ga, SuperK = H20, Borexino = mineral oil + PP0 (doped with a fluor), ICARUS = liq Ar

Reactor nu : Chooz = mineral oil + Gd, Daya Bay = ???, Diablo Canyon = doped mineral oil, Kaska = ???, Angra = mineral oil + Gd, Palo Verde = ???, Bugey = ???, Gosgen = ???

SBL Accelerator expts : Nomad = collider detector (drift chamber, etc), Chorus = emulsifying film, KARMEN = liquid scintillator, LSND = mineral oil + bPBD, NuTeV = solid calorimeter, DoNUT = emulsion sheets

LBL Accelerator expts : T2K = ???, NoVa = liquid scintillator, MINOS = solid detector, K2K = H20, Opera = emulsion sheets

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BeamsNomad = 450 GeV p + BeChorus = 450 GeV p + BeKarmen = 800 MeV p + heavy H20LSND = 800 MeV p + heavy H20NoVa = 120 GeV p + DoNUT = 800 GeV p + Tungsten