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

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

Antiprotons

R. Sparvoli – MAPS Perugia High-energy antiproton analysis Antiproton/proton identification: rigidity (R)  SPE |Z|=1 (dE/dx vs R)  SPE&ToF  vs R consistent with M p  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 high energy proton spectrum harder than antiproton  p/p-bar increase for increasing energy (10 10  Required strong TRK selection

R. Sparvoli – MAPS Perugia Low-energy antiprotons

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

R. Sparvoli – MAPS Perugia Antiproton identification e - (+ p-bar) p-bar p -1  Z  +1 “spillover” p p (+ e + ) proton-consistency cuts ( dE/dx vs R and  vs R ) electron-rejection cuts based on calorimeter-pattern topology 1 GV5 GV

R. Sparvoli – MAPS Perugia Proton-spillover background 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 “spillover” p p-bar p 10 GV50 GV MDR = 1/   (evaluated event-by-event by the fitting routine)

R. Sparvoli – MAPS Perugia MDR > 850 GV Minimal track requirements Strong track requirements: strict constraints on  2 (~75% efficiency) rejected tracks with low-resolution clusters along the trajectory - faulty strips (high noise) -  -rays (high signal and multiplicity) Proton-spillover background

R. Sparvoli – MAPS Perugia Proton-spillover background p-bar p “spillover” p 10 GV50 GV MDR = 1/   (evaluated event-by-event by the fitting routine) R < MDR/10

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

R. Sparvoli – MAPS Perugia 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

Positrons

R. Sparvoli – MAPS Perugia High-energy positron analysis Electron/positron identification: rigidity (R)  SPE |Z|=1 (dE/dx=MIP)  SPE&ToF  =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     might mimic pure em showers proton spectrum harder than positron  p/e + increase for increasing energy (10 10  Required strong CALO selection S1 S2 CALO S4 CARD CAS CAT TOF SPE S3 ND

R. Sparvoli – MAPS Perugia 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 51 GV positron 80 GV proton

R. Sparvoli – MAPS Perugia Background suppression Fraction of charge released along the calorimeter track (e + ) p (non-int) p (int) NB! p-bar (int) e-e- p-bar (non-int) Z=-1 Z=+1 Rigidity: GV LEFTHITRIGHT strips planes 0.6 R M

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

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

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

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

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

R. Sparvoli – MAPS Perugia Check of calorimeter selection Rigidity: GVRigidity: GV neg (e - ) e+e+ e+e+ p pos (p) p neg (e - ) pos (p) 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

R. Sparvoli – MAPS Perugia 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 Perugia 2 W planes: ≈1.5 X 0 20 W planes: ≈15 X 0 CALORIMETER: 22 W planes: 16.3 X 0 The “pre-sampler” method Selection of a pure sample of protons from flight data Only 2% of electrons and positrons do not interact in the first 2 CALO planes

R. Sparvoli – MAPS Perugia The “pre-sampler” method POSITRON SELECTION PROTON SELECTION 2 W planes: ≈1.5 X 0 20 W planes: ≈15 X 0 2 W planes: ≈1.5 X 0

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

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

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

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

R. Sparvoli – MAPS Perugia Procedure e + background estimation from data Electrons sample (1)Protons sample (2) Positrons + Background sample (3) Fit with : Beta distribution Wavelets Spline mean, standard-deviation beta distribution parameters, wavelets coefficients, splines parameters Fit sample (3) with finite mixture distributions got by (1) and (2) Estimation of weight “p” by means of Maximum Likelihood Estimation. p Estimation positrons number Estimation positrons fraction: Bootstrap resampling of positives sample and estimation of mean values of positrons number and positrons fraction positrons number 68 th percentile of mean values of positrons number and positrons fraction repeat for 1000 times Fit with : Beta distribution Wavelets Spline

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

Efficiency and contamination with pre-sampler protons Test Beam Data

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

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

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

R. Sparvoli – MAPS Perugia Additional background sources Space: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:Balloons: in addition to this, also pions and muons coming from CR interactions in atmosphere. This background is very important.

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

R. Sparvoli – MAPS Perugia 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 Perugia 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 Perugia 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 Perugia 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: 1.TOF is capable of providing particle energy until few GeV; 2.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 Perugia Efficiency fits

R. Sparvoli – MAPS Perugia 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 10 4, so the bck/signal ratio would be 10 !

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

R. Sparvoli – MAPS Perugia Final steps Once the number of selected events has been obtained, and efficiencies of the selection cuts calculated, the final steps are: 1. derive the final number of events by correction for the selection efficiencies; 2. include live time and geometrical factor in the calculation; 3. propagate the flux at the top of the instrument, including energy loss in dead materials; 4. eventually (balloon flights) propagate the flux at the top of atmosphere.

R. Sparvoli – MAPS Perugia 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  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  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 Perugia Particle fluxes J (E) = 1 x N (E) T live x G x  E where N (E) is the number of selected event per bin, T live is the livetime, G is the geometrical factor and  E is the width of the energy bin.

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

 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. Analytical method

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

R. Sparvoli – MAPS Perugia Spectra at top of the payload Particles crossing the material above the tracking system lose energy by ionization and bremsstrahlung processes. This changes the energy distribution of these particles; the spectra determined as in previous slide need to be extrapolated to the corresponding spectra at the top of the payload. In this extrapolation all the processes of energy loss have to be taken into account.

R. Sparvoli – MAPS Perugia This is done with an iterative procedure: the spectra in the spectrometer are used as input spectra of a program which integrates, with a RungeKutta technique, the particle propagation equation for a depth t of radiation lengths equal to the material above the tracking system. The resulting spectra are compared with the ones found in the spectrometer and the differences are used to rescale the input spectra. This iterative procedure goes on until the spectra propagated into the spectrometer coincide - in agreement within a few percent - with the experimental ones. This technique is cross checked using the simulation of the payload. Spectra at top of the payload

R. Sparvoli – MAPS Perugia PAMELA: Antiproton flux preliminary PAMELA

R. Sparvoli – MAPS Perugia PAMELA electron flux preliminary

R. Sparvoli – MAPS Perugia PAMELA electron flux preliminary

R. Sparvoli – MAPS Perugia Galactic protons and Helium p He preliminary

R. Sparvoli – MAPS Perugia Galactic protons and Helium preliminary

R. Sparvoli – MAPS Perugia Proton flux compared to prev. exp’s

R. Sparvoli – MAPS Perugia Conclusions Cosmic ray research in space is living a very exciting time: many instruments in flight (PAMELA, AMS, AGILE, FERMI,..) are providing – and will provide – excellent data to answer important questions. For the first time measurements of cosmic rays can be performed with the precision, statistics and temporal evolution needed to clarify many of the open problems in cosmology, astrophysics and solar terrestrial environment. Especially the search for rare particles has – from a theoretical point of view – the potentiality for discovering new physics. Task of the experimentalist is to provide high-quality data: the particle-physics technologies and methodologies used in space transformed the “once-pioneeristic” CR physics to a science of precision.

R. Sparvoli – MAPS Perugia Courtesy of Neil Weiner