Summary of downstream PID

MICE collaboration meetingFermilab

2006-06-10Rikard Sandström

Outline

• Modular analysis approach• Fits• PID variables• PID performance• PID and emittance• Which events are miss identified?• New useful tool?• Summary

Modular analysis

• All fits used in analysis has been updated.– Wider range in all phase space coordinates

• Robustness!

• Fits are using a modular approach.– For example, given (x,y,z,t,px,py,pz,E) at exit of

tracker, return expected track parameters after TOF2.

– The same thing is then repeated for track after layer 0 of calorimeter, but using the track that was expected coming out of TOF2.

– If one object changes, or fits are deemed poor, only that module needs refitting!

• Robustness!

Energy loss fits

• Bethe-Bloch:

• Difficult for the fitter, Taylor expand!– dE/dx = k0/β2 + k1/ β + k2 + k3 β + k4 β2 +ordo(β3)

• Path length correction by cos()

(β = p/E)

Energy loss in tracker

Energy loss in TOF2

Energy loss in EMCal layer 0

Range in EMCal layer 1-10

• Range is well parameterized by βγ.• Due to projection onto z axis, correct with a factor

cos().

Visible energy EMCal layer 0

Visible energy EMCal layer 1-10

• This kinetic energy after layer 0.• Note how the calorimeter starts leaking energy at high E.

Reconstructed E vs MC Truth range

• Distribution of ADC counts in layers gives information on Bragg peak -> muon range & momentum

• For muons punching through Bragg peak is often not found.

Fits -> Useful PID variables

• Given the fits and information in a detector, the response in of another variable can be anticipated. – If the expected value is wrong the particle could be

background.

• Discrepancy variables:– D = 1-expected/measured– D = 0 indicates signal event (muon)

• Used in Neural Net analysis– Barycenter disc, total ADC disc, tof, tof disc, range disc,

tdc peaks, holes/range, high threshold adc/ low threshold adc, adc layer0/ total adc, adc layer0/ adc layer1.

– Would need a better tool for evaluating their real impact on PID.

• Some variables are correlated.

Signal - background separation

Same again, but log scale

Efficiency

• Notice the steepness of the curve.– 99.9% efficiency is at very sensitive region.

PID performance achieved

• Scenario: – Aug’05 with 7.6mm diffuser– RF turned off, empty absorbers

• With same statistics as in Osaka, – Efficiency 99.90% -> rejecting 97.7%

• Input purity 99.58% ->final purity 99.99%

– Efficiency 99.87% -> rejecting 99.5%

• Curiously, exactly same data but fewer events gave– Efficiency 99.90% -> rejecting 99.4%

• These results are even better than what I presented 2 days ago.– Due to longer NN training.

Steepness!

Impact on emittance

• Is 99.9% efficiency, 99.8% purity enough?

• I used no field approximation (thanks Chris)– ε = sqrt(x

2y2-xy

2)/m

– Exit of TOF2, probably worst difference between signal & background.

• x direction– ε = 10.3 pi mm rad– dε/ ε = 0.89 ppm

• y direction– ε = 9.9 pi mm rad– dε/ ε = 0.58 ppm

Impact of wrong badness tagging

• Assuming PID is perfect, what is the effect of poor tagging as good/bad event?– x, px gave 0.22 ppm difference at exit of TOF2.

• Effect is lower than PID, but at the same scale.

• Cause exclusively by tracker and tof resolutions.– Miss tagging straight tracks and tracks close to

active volume edges.

• Note: emittance is using reconstructed tracks (smeared) for both MC truth badness and reconstructed badness.

What signal events are miss IDed?

• For w<0.2, – 8% of muons are stopped in TOF2.

• Will be worse a lower momentum.

– 8% are leaving TOF2 with very large angle and misses calorimeter.

• Will be worse a lower momentum.• Move calorimeter even closer to TOF2.

– 8% decay between TOF2 and calorimeter.• Move calorimeter even closer to TOF2.

– 60% are muons decaying in ADC gate and too close in time to its own track that only one TDC peak is registered.

• Tweaking TDC threshold could help.• Harmless!

New useful tool?

• Stumbled upon a ROOT package, T Multi Variable Analysis (TMVA) which is made for evaluating different methods of separating signal from background.

• Yesterday, I installed it and ran it.– To my big surprise it worked

without problem.

• We could use this tool to see if a better way than using my neural net exists.– Can use the ROOT trees I

have already prepared for comparison.

Summary

• New modular approach– increases performance.– makes analysis more robust to future changes.

• Calorimeter close to TOF2 -> better PID.• No show stopper

– Failed PID events does not prevent us to reach our emittance measurement target resolution.

– Still room for tweaks and improvements.