Summary of downstream PID MICE collaboration meeting Fermilab Rikard 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 = k 0 /β 2 + k 1 / β + k 2 + k 3 β + k 4 β 2 +ordo(β 3 ) 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 2  y 2 -  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, p x 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.