January 21, 2005January 21, 2005 Milagro Collaboration MeetingMilagro Collaboration Meeting

MARS StuffMARS Stuff

Curtis LansdellCurtis LansdellUniversity of MarylandUniversity of Maryland

OutlineOutline

““Standard” DiscriminationStandard” Discrimination

MARS, Neural Networks, and x2MARS, Neural Networks, and x2

MARS Distributions and Q-factorsMARS Distributions and Q-factors

MARS and the CrabMARS and the Crab

GEANT 4GEANT 4

Eyeball DiscriminationEyeball Discriminationx2p

proton gamma

AS+ORCOMAS+ORCOM

Old way to differentiate – x2 parameterOld way to differentiate – x2 parameterx2 x2 nb2/mxPE nb2/mxPE

x2 > 2.5 yields Q ~ 1.7 using just AS layer (and ORCOM)x2 > 2.5 yields Q ~ 1.7 using just AS layer (and ORCOM)

Retains 51%Retains 51% and 8.5% hadron using 50 PMT trigger and 8.5% hadron using 50 PMT trigger

R. Atkins et al., ApJ 595, 803 (2003).

Some Checks on MARSSome Checks on MARS

Neural Networks, MARS should get the same or higher Neural Networks, MARS should get the same or higher Q-factors than x2Q-factors than x2– ANN (run by Xianwu) and ROOT NN package had problems ANN (run by Xianwu) and ROOT NN package had problems

recreating Q-factor from single variable (x2)recreating Q-factor from single variable (x2)– ANN and ROOT NN also had problems getting Q-factors as high ANN and ROOT NN also had problems getting Q-factors as high

as MARS for multiple variables.as MARS for multiple variables.– MARS gives Qs closer to x2MARS gives Qs closer to x2

ROOT NN MARS

Multilayer+Gaussian FittersMultilayer+Gaussian Fitters

MARSMARS11 (Multivariate Adaptive Regression Splines) (Multivariate Adaptive Regression Splines)– Should be able to determine the best parametersShould be able to determine the best parameters– Provides probability of being signal: ln[P(Provides probability of being signal: ln[P()/P(p)])/P(p)]

More positive means more More positive means more -like-like

1J. Friedman, “Multivariate Adaptive Regression Splines”, Annals of Statistics 19 (1991).

Relative Variable Importance

corecore x2x2 mxPEmxPE ∑∑PEPEtt ∑∑PEPEbb ∑∑PEPEoo nb2nb2 nb8nb8 par2dpar2d hitashitas hitmuhitmu hitorhitor pchi2pchi2 grPEgrPEtt grPEgrPEbb latPElatPEtt latPElatPEbb

OnOn 41.7341.73 2.5652.565 0.0000.000 0.0000.000 6.8396.839 0.0000.000 3.6043.604 45.0645.06 61.6661.66 0.0000.000 100.0100.0 39.1839.18 0.0000.000 0.0000.000 58.0758.07 44.4844.48

OffOff 100.0100.0 79.2679.26 98.2498.24 0.0000.000 7.7717.771 78.3678.36 80.3780.37 6.5996.599 51.8851.88 0.0000.000 4.4834.483 66.5266.52 8.0748.074 0.0000.000 71.7171.71 0.0000.000

AllAll 25.5825.58 0.0000.000 0.0000.000 0.0000.000 79.6279.62 0.0000.000 12.7112.71 11.8611.86 67.8867.88 5.0775.077 3.0633.063 59.2459.24 0.0000.000 0.0000.000 100.0100.0 7.6807.680

on-pond

off-pond

not in Crab REC data

Event EfficienciesEvent Efficiencies16 par model 12 par model (for Crab)

on-pond on-pond

off-pond off-pond

Crab ResultsCrab ResultsElapsed time = 416 daysElapsed time = 416 daysBin size = 1.3Bin size = 1.3°°nAS > 55, nFit > 80 (multi-layer fits)nAS > 55, nFit > 80 (multi-layer fits)MARS used 12 par model (dAngle < 0.7 for gammas)MARS used 12 par model (dAngle < 0.7 for gammas)

No extra cutsNo extra cuts x2x2 MARS (on=1, off=2.5)MARS (on=1, off=2.5) MARS (1, 0.6)MARS (1, 0.6) MARS (0.8, 0.8)MARS (0.8, 0.8)

SignificanceSignificance 2.652.65 4.564.56 2.662.66 3.623.62 3.273.27

On SourceOn Source 22128412212841 240608240608 2468124681 6910069100 6176361763

Off SourceOff Source 2208997.252208997.25 238434.3238434.3 24275.4124275.41 68177.8468177.84 60973.6960973.69

ExcessExcess 3843.753843.75 2173.72173.7 405.59405.59 922.16922.16 789.31789.31

GEANT 4GEANT 4

Energies from 30 GeV to 100 TeVEnergies from 30 GeV to 100 TeV

Thrown out to 1 km and flat in radiusThrown out to 1 km and flat in radius

~70 M proton events created so far~70 M proton events created so far– 4.4 k events trigger with nFit > 80 (7.8 k for nFit > 5)4.4 k events trigger with nFit > 80 (7.8 k for nFit > 5)

Distributions still don’t match data wellDistributions still don’t match data well– Even with correct quantum efficienciesEven with correct quantum efficiencies

Continuing Onward…Continuing Onward…

MARS in conjunction with multilayer fitter appears to MARS in conjunction with multilayer fitter appears to result in better discrimination in MC events.result in better discrimination in MC events.– Compared with neural networkCompared with neural network

Used ROOT NN package, but saw worse discrimination – maybe Used ROOT NN package, but saw worse discrimination – maybe more tuning of hidden layers/nodes will helpmore tuning of hidden layers/nodes will help

– Used 12 parameter MARS value cuts on Crab dataUsed 12 parameter MARS value cuts on Crab dataWorse significance than x2 > 2.5 (nFit > 80, multi-layer fits)Worse significance than x2 > 2.5 (nFit > 80, multi-layer fits)Oops, forgot to do energy weighting (used 2.4)Oops, forgot to do energy weighting (used 2.4)

GEANT 4 events being producedGEANT 4 events being produced– A few strange numbers need to be examined from the outputA few strange numbers need to be examined from the output– Will check more MC-data agreementWill check more MC-data agreement– Run MARS with new MC!Run MARS with new MC!

Need more proton triggers and start creating gamma eventsNeed more proton triggers and start creating gamma events