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Outline of the lecture

Outline of the lecture. Lecture 2: Sources of background and their rejection Efficiencies & Contaminations Absolute fluxes Conclusions. Lecture 2: Sources of background and their rejection. Antiprotons. High-energy antiproton analysis. Antiproton/proton identification :

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Outline of the lecture

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  1. Outline of the lecture • Lecture 2: • Sources of background and their rejection • Efficiencies & Contaminations • Absolute fluxes • Conclusions R. Sparvoli – MAPS 2009 - Perugia

  2. Lecture 2:Sources of background and their rejection R. Sparvoli – MAPS 2009 - Perugia

  3. Antiprotons

  4. High-energy antiproton analysis • Antiproton/proton identification: • rigidity (R)  SPE • |Z|=1 (dE/dx vs R)  SPE&ToF • b vs R consistent with MpToF • p-bar/p separation (charge sign)  SPE • p-bar/e- (and p/e+ ) separation CALO • Dominant background spillover protons: • finite deflection resolution of the SPE  wrong assignment of charge-sign @ high energy • proton spectrum harder than antiproton  p/p-bar increase for increasing energy (103 @1GV, 104 @100GV) •  Required strong TRK selection R. Sparvoli – MAPS 2009 - Perugia

  5. Low-energy antiprotons R. Sparvoli – MAPS 2009 - Perugia

  6. e- e+ p α p GV-1 e- e+ p p GV-1 p p R. Sparvoli – MAPS 2009 - Perugia GV-1

  7. 1 GV 5 GV Antiproton identification -1  Z  +1 p (+ e+) p e-(+ p-bar) proton-consistency cuts (dE/dx vs R and b vs R) “spillover” p p-bar electron-rejection cuts based on calorimeter-pattern topology R. Sparvoli – MAPS 2009 - Perugia

  8. MDR = 1/sh (evaluated event-by-event by the fitting routine) p p-bar “spillover” p 10 GV 50 GV • MDR depends on: • number and distribution of fitted points along the trajectory • spatial resolution of the single position measurements • magnetic field intensity along the trajectory Proton-spillover background R. Sparvoli – MAPS 2009 - Perugia

  9. Proton-spillover background Minimal track requirements MDR > 850GV • Strong track requirements: • strict constraints on c2 (~75% efficiency) • rejected tracks with low-resolution clusters along the trajectory • - faulty strips (high noise) • - d-rays (high signal and multiplicity) R. Sparvoli – MAPS 2009 - Perugia

  10. MDR = 1/sh (evaluated event-by-event by the fitting routine) p p-bar “spillover” p R < MDR/10 10 GV 50 GV Proton-spillover background R. Sparvoli – MAPS 2009 - Perugia

  11. Spillover as a limit to the maximum energy limit • The antiproton measurements are limited by the existence of the spillover effect; • There is need of very stringeng tracking cuts (chi-square of the track, MDR, quality..) to separate spillover protons from antiprotons; • the maximum energy achieved by an instrument is defined as the energy where the signal is well separated from the spillover; • For PAMELA, this limit is set to ~ 200 GeV. R. Sparvoli – MAPS 2009 - Perugia

  12. PAMELA: pbar/pratio PRL 102, 051101 (2009) R. Sparvoli – MAPS 2009 - Perugia

  13. PAMELA: pbar/pratio In PRL article published data acquired till February 2008 New data reduction: data till end of 2008 and improved tracking algorithm → ~50% more events above 10 GeV, e.g 14 p against 6 p above 50 GeV R. Sparvoli – MAPS 2009 - Perugia

  14. Positrons

  15. S1 CARD CAT S2 TOF SPE CAS S3 CALO S4 ND High-energy positron analysis • Electron/positron identification: • rigidity (R)  SPE • |Z|=1 (dE/dx=MIP)  SPE&ToF • b=1 ToF • e-/e+ separation (charge sign)  SPE • e+/p (and e-/p-bar) separation CALO • Dominant background interacting protons: • fluctuations in hadronic shower development  p0 ggmight mimic pure em showers • proton spectrum harder than positron  p/e+ increase for increasing energy (103 @1GV 104 @100GV) •  Required strong CALO selection R. Sparvoli – MAPS 2009 - Perugia

  16. 51 GV positron 80 GV proton Positron identification with CALO • Identification based on: • Shower topology (lateral and longitudinal profile, shower starting point) • Total detected energy (energy-rigidity match) • Analysis key points: • Tuning/check of selection criteria with: • test-beam data • simulation • flight data  dE/dx from SPE & neutron yield from ND • Selection of pure proton sample from flight data (“pre-sampler” method): • Background-suppression method • Background-estimation method R. Sparvoli – MAPS 2009 - Perugia

  17. Z=-1 e- Rigidity: 20-30 GV p-bar (non-int) p-bar (int) NB! Z=+1 0.6 RM p (non-int) LEFT HIT RIGHT planes (e+) p (int) strips Background suppression Fraction of charge released along the calorimeter track R. Sparvoli – MAPS 2009 - Perugia

  18. Z=-1 Z=-1 e- e- Rigidity: 20-30 GV Rigidity: 20-30 GV + Constraints on: p-bar (non-int) p-bar (int) p-bar Energy-momentum match NB! Z=+1 Z=+1 p (non-int) e+ (e+) p (int) p Fraction of charge released along the calorimeter track R. Sparvoli – MAPS 2009 - Perugia

  19. Z=-1 Z=-1 e- e- Rigidity: 20-30 GV Rigidity: 20-30 GV p-bar Shower starting-point Z=+1 Z=+1 Longitudinal profile Lateral profile e+ e+ p BK-suppression method p Fraction of charge released along the calorimeter track + Constraints on: Energy-momentum match R. Sparvoli – MAPS 2009 - Perugia

  20. Check of calorimeter selection Flight data Rigidity: 20-30 GV Test beam data Momentum: 50GeV/c Fraction of charge released along the calorimeter track e- e- + Constraints on: Energy-momentum match e+ p Shower starting-point p R. Sparvoli – MAPS 2009 - Perugia

  21. Check of calorimeter selection Flight data Rigidity: 20-30 GV Flight data Neutron yield in ND Fraction of charge released along the calorimeter track e- e- + Constraints on: Energy-momentum match e+ e+ Shower starting-point p p R. Sparvoli – MAPS 2009 - Perugia

  22. Check of calorimeter selection Flight data Rigidity: 42-65 GV Flight data Neutron yield in ND Fraction of charge released along the calorimeter track e- e- + Constraints on: Energy-momentum match e+ e+ Shower starting-point p p R. Sparvoli – MAPS 2009 - Perugia

  23. Energy loss in silicon tracker detectors: • Top: positive (mostly p) and negative events (mostly e-) • Bottom: positive events identified as p and e+ by trasversal profile method Relativistic rise Check of calorimeter selection Rigidity: 10-15 GV Rigidity: 15-20 GV neg (e-) pos (p) neg (e-) pos (p) p e+ p e+ R. Sparvoli – MAPS 2009 - Perugia

  24. Background-estimation • To reach a higher level of confidence that the residual background has been eliminated, one can decide to go for a background-estimation method rather than for a full background-suppression method. • Strategy of this method is to construct a sample of the background, estimate the fraction of contamination into the signal, and subtract this fraction to the signal. R. Sparvoli – MAPS 2009 - Perugia

  25. The “pre-sampler” method Selection of a pure sample of protons from flight data CALORIMETER: 22 W planes: 16.3 X0 2 W planes: ≈1.5 X0 20 W planes: ≈15 X0 Only 2% of electrons and positrons do not interact in the first 2 CALO planes R. Sparvoli – MAPS 2009 - Perugia

  26. The “pre-sampler” method POSITRON SELECTION 20 W planes: ≈15 X0 2 W planes: ≈1.5 X0 PROTON SELECTION 2 W planes: ≈1.5 X0 20 W planes: ≈15 X0 R. Sparvoli – MAPS 2009 - Perugia

  27. Positron selection with “pre-sampler” (~RM) Selections on total detected energy, starting point of shower e- e+ p - (p) - R. Sparvoli – MAPS 2009 - Perugia +

  28. Rigidity: 20-28 GV e- Fraction of charge released along the calorimeter track (left, hit, right) + Constraints on: p (pre-sempler) Energy-momentum match Shower starting-point e+ p Proton background evaluation R. Sparvoli – MAPS 2009 - Perugia

  29. Rigidity: 28-42 GV e- Fraction of charge released along the calorimeter track (left, hit, right) + Constraints on: p (pre-sempler) Energy-momentum match Shower starting-point e+ p Proton background evaluation R. Sparvoli – MAPS 2009 - Perugia

  30. e+ background estimation from data Data till end of 2008.Rigidity: 20-28 GV e- e+ p ‘presampler’ p R. Sparvoli – MAPS 2009 - Perugia

  31. Procedure e+ background estimation from data R. Sparvoli – MAPS 2009 - Perugia

  32. Test Beam Data Efficiency and contamination with pre-sampler protons

  33. PAMELA: Positron fraction NATURE 458, 697, 2009 • (Moskalenko & Strong 1998) • GALPROP code • Plain diffusion model • Interstellar spectra Solar modulation effects Anomalous increasing ? R. Sparvoli – MAPS 2009 - Perugia

  34. PAMELA: Positron fraction NATURE 458, 697, 2009 R. Sparvoli – MAPS 2009 - Perugia

  35. Different statistical approaches for the bkg Data: July 2006  December 2008 R. Sparvoli – MAPS 2009 - Perugia

  36. Additional background sources • Space:pions produced by interaction of CR protons with the satellite body or the instrument itself; helped by simulations, bkg removed by cuts on the AC and TOF; • Balloons: in addition to this, also pions and muons coming from CR interactions in atmosphere. This background is very important. R. Sparvoli – MAPS 2009 - Perugia

  37. Lecture 2:Efficiencies & Contaminations R. Sparvoli – MAPS 2009 - Perugia

  38. Efficiency of selection cuts • We have seen how different information from detectors bring to particle identification. All selection criteria have to be combined together to select a specific particle type. • To be able to compute particle ratios and fluxes, we must know the efficiency of every selection cut, namely the probability that a good event passes that selection cut. • The efficiencies will be combined together, and much attention must be put into correlations between selection cuts. R. Sparvoli – MAPS 2009 - Perugia

  39. Efficiency samples • Efficiency samples can be obtained by: • Simulations • Test beams • Flight data • The first two are very appealing! But: • in-flight conditions might vary with time, bringing to time-dependent efficiencies; • Still hadronic interactions a problem; • Beam test data do not arrive isotropically! R. Sparvoli – MAPS 2009 - Perugia

  40. Flight data samples • Samples of efficiency are selected by independent detectors with respect to the one we are measuring the efficiency. • Big danger of this procedure: systematics induced by correlations between detectors ! No recipee: when possible, several methods should be cross-checked against each other, until a consistent result is found ! R. Sparvoli – MAPS 2009 - Perugia

  41. Example: tracker efficiency • The tracking efficiency (probability of good events to be tracked) is energy dependent (for many different experimental reasons); • One needs to know the energy of the incoming particles by another detector: • TOF is capable of providing particle energy until few GeV; • CALO is capable of providing particle energy only for e.m. particles. •  Need to tune the simulation with the real data, and then compute the tracking efficiency by simulation ! R. Sparvoli – MAPS 2009 - Perugia

  42. Efficiency fits R. Sparvoli – MAPS 2009 - Perugia

  43. Contaminations • The procedure is the same as for efficiency, but one has to select a sample of the background ! • BEWARE: the level of contamination acceptable must be always put in relation to the flux in flight compared to the “signal”. • Es: a contamination of 0.1% of protons in the positron signal is NOT acceptable, because at 100 GV the p/e+ ratio is 104, so the bck/signal ratio would be 10 ! R. Sparvoli – MAPS 2009 - Perugia

  44. Lecture 2:Determination of fluxes R. Sparvoli – MAPS 2009 - Perugia

  45. Final steps • Once the number of selected events has been obtained, and efficiencies of the selection cuts calculated, the final steps are: • derive the final number of events by correction for the selection efficiencies; • include live time and geometrical factor in the calculation; • propagate the flux at the top of the instrument, including energy loss in dead materials; • eventually (balloon flights) propagate the flux at the top of atmosphere. R. Sparvoli – MAPS 2009 - Perugia

  46. Correction for efficiency • The selected events are distributed in energy bins, fixed as a compromise between statistics and energy resolution of the instrument (pointless to have them much smaller than it!). • Since efficiencies are energy-dependent, the efficiency correction will be done bin per bin: • If e does not vary significantly inside the bin, the correction will be easy and done at the center of gravity of the energy distribution of the events; • If e varies significantly inside the bin, one has to obtain an “average efficiency value” inside the bin, by means of a weighting technique. R. Sparvoli – MAPS 2009 - Perugia

  47. Particle fluxes • J (E) = 1xN (E) • TlivexG xDE • whereN (E)is the number of selected event per bin,Tliveis the livetime, G is the geometrical factor andDEis the width of the energy bin. R. Sparvoli – MAPS 2009 - Perugia

  48. ANALITIC METHOD No physics(except the magnetic field) Simplified geometry(TOF and tracker planes approximated as rectangles) Geometrical cuts SIMULATION Particles interactions with materialsmust be switched off(except the magnetic field) Complex geometry(dead volumes: to be included in efficiencies and not GF) Cuts on interaction points Geometrical factor R. Sparvoli – MAPS 2009 - Perugia

  49. Analytical method • The geometric factor is uniquely determined once given the particle rigidity: or, in other words: • the momentum modulus; • the charge q (with sign). • Integration in the 4-dim. space of track parameters x, y, θ, φ (taken in the reference plane S). • Elements considered in the calculation: • Magnet (+ Al external layers). • Tracker planes T1 and T6 (outside magnet). • 6 TOF planes. • Magnetic field (real map). • No interactions.

  50. PAMELA GF results 2160 Good agreement between SIMULATION selection and ANALITIC METHOD’s one R. Sparvoli – MAPS 2009 - Perugia

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