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PID simulations

PID simulations. Rikard Sandstr öm University of Geneva MICE collaboration meeting 2005-10-22 RAL. Outline. Definitions PID objective Calorimeter Comparison with KLOE data Time of flight as PID variable Performance Before PID analysis After PID analysis. Definition, signal. Signal

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PID simulations

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  1. PID simulations Rikard Sandström University of Geneva MICE collaboration meeting 2005-10-22 RAL

  2. Outline • Definitions • PID objective • Calorimeter • Comparison with KLOE data • Time of flight as PID variable • Performance • Before PID analysis • After PID analysis

  3. Definition, signal • Signal • An event which is a muon at TOF1 and at TOF2. • Background • An event which does not fulfill the Signal requirement.

  4. Definition, good event • Good event • An event which • gives hits in both trackers, TOF1 & TOF2. • is within  = 15cm in both trackers. • has a time of flight corresponding to average βz within 0.5 and 1. • has positive pz in trackers. • Bad event • An event which does not fulfill the Good event requirement • Good/bad given independently by • MC truth • Reconstructed tracks Nota bene!: Good event  Signal event

  5. PID objective • For good events, correctly assign signal/background tag. • Can be expressed in efficiency & purity • Assigning signal as background -> Low efficiency • Assigning background as signal -> Low purity • How: • Find/construct variables which separates signal from background. • Every event is then assigned a “muonness” weight. • Now done by fitting the signal variable in a neural net. • The weight is added to input file. • Can be used in emittance calculation. • Easy to compare alternative methods and cuts.

  6. Calorimeter, KLOE in G4MICE • To validate EmCal simulations, G4MICE was used to reproduce KLOE situation. • KLOE geometry • Cells: 4.4x4.4x400 cm3 • Lead thickness 0.5 mm • KLOE readout • Fibers: Kuraray SCSF-81 • Long attenuation • 3% light collection efficiency • PMT: Hamatsu R5946/01 1.5” • Gain 1M @ 2kV • Result: • amplitude/visible energy = 60.09±4.19 adc counts/MeV • amplitude  2(adcL adcR)/(adcL+adcR)

  7. Calorimeter, data vs G4MICE KLOE data KLOE in G4MICE 195<|p|<250 MeV/c

  8. TOF and PID (MICE note on this topic coming soon.) • Idea: • Time of flight given by TOF1 and TOF2. • Momentum given by trackers. • Comparing the two gives estimate of particle mass! • Practice: • Take momentum from trackers. • Assume mass = muon mass. • Calculate when the particle is expected to arrive at TOF2. • Compare with measured time.

  9. Time of flight • dt=dz/(βzc) • Hence, to first order t.o.f. depends on pz. • A rough estimate is taking only pz measured in trackers, and expected energy loss into account. • Second most important effect is momentum transfer induced by magnetic field. • Other things • Energy loss fluctuations. • RF phase.

  10. Magnetic field & TOF • Principle: • Total momentum conserved, longitudinal momentum not conserved. • Lorentz force F=qvxB • Longitudinal component Fz~ vxBy-vyBx • Field has largest transversal components at field flips • B(z=0)  k , k is a constant. • Treat classically -> Fz ~ pxy- pyx • -> sintan, as beam goes to pencil beam. • Result (most difficult case): • t.o.f. = 49.81±1.93 ns predicted to rms 0.28 ns (muons). • I.e. spread reduced to 14.6%. (Upper limit.)

  11. MC truth purity • Beam: • 6 pi mm rad mu+ beam, starting at TOF1. • 1ppm of p, K+, pi+, e+ contamination. • Starting purity at TOF1 = 99.62%. • What happens: • Particles may decay, and another particle might arrive at TOF2. • At TOF2: • Proton tracks never give good event. • 4% of K+ tracks give good event. • 68% of pi+ tracks give good event. • 0.42% of good-event mu+ of tracks has different particle ID at TOF2. • Decay! • 5.3% of mu+ tracks give bad event. -> Resulting purity at TOF2 = 99.46%.

  12. Time of flight cut • Time of flight can be predicted to < 300 ps • Worst case beam 280 ps. • Using MC truth tracker info, apply 5 ns time of flight discrepancy cut • Efficiency = 99.994%  100% • Purity = 99.68% • Background from muon decay reduced by 44%. • Positrons (starting at TOF1) reduced by 100%. • Pion background reduced by 2%. • Kaon background reduced by 40%. • Tracker reconstruction gave suspicious values. • I have the program ready to analyze using reconstructed values once all OK.

  13. Neural network performance • Using neural net to fit is more powerful than square cuts, or multidimensional Gaussian fits. • Calorimeter + MC truth tracker & TOF info: • Purity = ?@ 99.9% efficiency. • Calorimeter alone: • Purity = ?@ 99.9% efficiency.

  14. Summary • Background at TOF2 • Heavy particles get lost prior to TOF2. • Pions at TOF1 gives significant background at TOF2. • Time of flight is almost powerless. • Hard to separate from muons with present calorimeter design. • Positrons at TOF1 very easy to reject with time of flight. • Positrons from muon in-flight decays harder to reject. • Time of flight & tracker & calorimeter makes good combination. • Purity before analysis = 99.42% (default beam). • Purity after analysis with neural net = ? @ eff =99.9%.

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