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5/15/06H. Ray : Pheno 06 MiniBooNE Results worth waiting for Heather Ray Los Alamos National Laboratory.

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Presentation on theme: "5/15/06H. Ray : Pheno 06 MiniBooNE Results worth waiting for Heather Ray Los Alamos National Laboratory."— Presentation transcript:

1 5/15/06H. Ray : Pheno 06 MiniBooNE Results worth waiting for Heather Ray Los Alamos National Laboratory

2 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

3 5/15/06H. Ray : Pheno 06 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

4 5/15/06H. Ray : Pheno 06 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

5 5/15/06H. Ray : Pheno 06 Why the Wait?  The oscillation signal is expected to be small  Probability for LSND oscillations = 0.264%!  Need to know backgrounds, detector response very precisely  Requires 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?”

6 5/15/06H. Ray : Pheno 06 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 : H 2 0, no fluor/scint, all Cerenkov  LSND : all scintillation (swamped fluorescence), some Cerenkov  MB : in the middle, need to untangle various components

7 5/15/06H. Ray : Pheno 06 1 st Hurdle The Optical Model

8 5/15/06H. Ray : Pheno 06 The Optical Model  Full battery of external measurements to provide complete picture of OM  Problem! 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 correlations  We do not tune on any samples which may bias the oscillation analysis

9 5/15/06H. Ray : Pheno 06 External Measurements  Variety of stand- alone tests which characterize separate components of mineral oil

10 5/15/06H. Ray : Pheno 06 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

11 5/15/06H. Ray : Pheno 06 The Optical Model Chain External 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.

12 5/15/06H. Ray : Pheno 06 Recent Improvements Improvements to OM greatly improve Michel electron E as a function of location in our detector

13 5/15/06H. Ray : Pheno 06 Impact of Improved OM Scintillation light in 1st gamma in pi0 fitter Distance between pi0 vertex and 1st gamma conversion point

14 5/15/06H. Ray : Pheno 06 2 nd Hurdle Identifying Mis-IDs

15 5/15/06H. Ray : Pheno 06 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 +

16 5/15/06H. Ray : Pheno 06 3 rd Hurdle Particle ID

17 5/15/06H. Ray : Pheno 06 Sensitivity Estimate  Good sensitivity requires PID  Remove  99.9% of  CC interactions  Remove  99% of all NC  0 producing interactions  Maintain  30-60% efficiency for e interactions LSND best fit sin 2 2  =  m 2 = 1.2 ev 2

18 5/15/06H. Ray : Pheno 06 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) Nuc.Inst.Meth.A 555 (2005)

19 5/15/06H. Ray : Pheno 06 PID Inputs Calibration Sample Signal-like Events Primary Background Mean = 1.80, RMS = 1.47 Mean = 1.19, RMS = 0.76 Mean = 20.83, RMS = Mean = 3.48, RMS = 3.17 Mean = 16.02, RMS = Mean = 3.24, RMS = 2.94

20 5/15/06H. Ray : Pheno 06 Summary  We are moving forward in leaps and bounds!  Past 6 months have brought phenomenal improvement in our Optical Model  Agreement in PID potential inputs vastly improved  New pion fitter offers better resolution of single  0 events, reductions in mis-id backgrounds  These 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

21 5/15/06H. Ray : Pheno 06 BACKUP INFO

22 5/15/06H. Ray : Pheno 06 NN vs Tree The Elements of Statistical Learning, Hastie, Tibshirani, Friedman, Springer (2003) Neural NetsTrees Natural handling of data of “mixed” type BadGood Handling of missing values BadGood Robustness to outliers in input space BadGood Insensitive to monotonic transformations of inputs BadGood Computational scalability (large N) BadGood Ability to deal with irrelevant inputs BadGood Ability to extract linear combinations of features GoodBad Interpretability BadFair Predictive power GoodBad

23 5/15/06H. Ray : Pheno 06 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 ProsCons

24 5/15/06H. Ray : Pheno 06 Why Boost a Tree?  You can boost anything - tree, neural net, etc.  Boosting combines weak classifiers to produce a powerful committee  Classifiers are combined through a weighted majority vote to produce the final output

25 5/15/06H. Ray : Pheno 06 Boosted Trees  Inherits pros of single trees  Dramatic performance improvement  Low bias, low variance  Less susceptible to overtraining  More of a black box  Increases sensitivity to outliers and noisy data ConsPros

26 5/15/06H. Ray : Pheno 06 Boosted Tree Falsehoods  Boosted trees are NOT robust against data to MC disagreement  We 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

27 5/15/06H. Ray : Pheno 06 Osc e MisID  e from  + e from K + e from K 0 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 POT High energy e data  Events below ~1.5 GeV still in closed box (blind analysis) Determining Backgrounds with MiniBooNE data

28 5/15/06H. Ray : Pheno 06 Why the Wait?  We don’t have 2nd detector so we can’t do flux cancellation  We need to know the neutrino production mechanisms much more precisely than past expts have needed  Rely on data from external expts : Harp thin target results recently added to MiniBooNE MC (April ‘06)

29 5/15/06H. Ray : Pheno 06 Checking PID with NuMI Events  Because of the off-axis angle, the beam at MiniBooNE from NuMI is significantly enhanced in e s from K +  Enables a powerful check on the Particle ID

30 5/15/06H. Ray : Pheno 06 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

31 5/15/06H. Ray : Pheno 06 Beams  Nomad = 450 GeV p + Be  Chorus = 450 GeV p + Be  Karmen = 800 MeV p + heavy H20  LSND = 800 MeV p + heavy H20  NoVa = 120 GeV p +  DoNUT = 800 GeV p + Tungsten


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