Presentation is loading. Please wait.

Presentation is loading. Please wait.

Track Fitting (Kalman Filter). Least Squares Fitting Generally accepted solution: Kalman filter – at Gaussian level optimal correction of multiple scattering.

Similar presentations


Presentation on theme: "Track Fitting (Kalman Filter). Least Squares Fitting Generally accepted solution: Kalman filter – at Gaussian level optimal correction of multiple scattering."— Presentation transcript:

1 Track Fitting (Kalman Filter)

2 Least Squares Fitting Generally accepted solution: Kalman filter – at Gaussian level optimal correction of multiple scattering (“process noise”) – energy loss can be incorporated similarly – with “smoother”, full information at every point of trajectory convenient for matching with other components

3 What the Kalman filter is A progressive way of performing a least- squares fit Mathematically equivalent to the latter What it is not: a pattern recognition method (though it can be efficiently used within one) a “robust” fitting method

4 Information Flow in the Track Fit Effects influencing the amount of information contained in the measurements Effects influencing the amount of information contained in the measurements  Information that the fit has to take into account Increase of informationDilution of information Origin

5 Kalman Filter The Kalman filter process is a successive approximation scheme to estimate parameters Simple Example: 2 parameters - intercept and slope: x = x 0 + S x * z; P = (x 0, S x ) Errors on parameters x 0 & S x (covariance matrix): C = Cx-x Cx-s Cs-x Cs-s Cx-x = In general C = Propagation: x(k+1) = x(k)+Sx(k)*(z(k+1)-z(k)) Pm(k+1) = F(  z) * P(k) where F(  z) = 1 z(k+1)-z(k) 0 1 Cm(k+1) = F(  z) *C(k) * F(  z) T + Q(k) k k+1 Noise: Q(k) (Multiple Scattering) P(k) Pm(k+1)

6 Kalman Filter Form the weighted average of the k+1 measurement and the propagated track model: Weights given by inverse of Error Matrix: C -1 Hit: X(k+1) with errors V(k+1) P(k+1) = Cm -1 (k+1)*Pm(k+1)+ V -1 (k+1)*X(k+1) Cm -1 (k+1) + V -1 (k+1) k k+1 Noise (Multiple Scattering) and C(k+1) = (Cm -1 (k+1) + V -1 (k+1)) -1 Now its repeated for the k+2 planes and so - on. This is called FILTERING - each successive step incorporates the knowledge of previous steps as allowed for by the NOISE and the aggregate sum of the previous hits. Pm(k+1)

7 Kalman Filter We start the FILTER process at the conversion point BUT… We want the best estimate of the track parameters at the conversion point. Must propagate the influence of all the subsequent Hits backwards to the beginning of the track - Essentially running the FILTER in reverse. This is call the SMOOTHER & the linear algebra is similar. Residuals &  2 : Residuals: r(k) = X(k) - Pm(k) Covariance of r(k): Cr(k) = V(k) - C(k) Then:  2 = r(k) T Cr(k) -1 r(k) for the k th step

8 point k-1 How the Kalman Filter Works - - details 1.Trajectory until point (k-1)

9 point k-1 Prediction How the Kalman Filter Works 1.Trajectory until point (k-1) 2.Prediction (without process noise)

10 point k-1 Prediction Filtering of k-th point 1.Trajectory until point (k-1) 2.Prediction (with process noise = mult. scattering) 3.Filter How the Kalman Filter Works

11 point k-1 PredictionMultiple scattering 1.Trajectory until point (k-1) 2.Prediction (with process noise = mult. scattering) How the Kalman Filter Works

12 point k-1 Prediction Filtering of k-th point Multiple scattering 1.Trajectory until point (k-1) 2.Prediction (with process noise = mult. scattering) 3.Filter How the Kalman Filter Works

13 Some Math: Prediction Parameters & covariance matrix at (k-1) Prediction Transport matrix Process noise Prediction equations Transports the information up to the (k-1)-th hit to the location of the k-th hitTransports the information up to the (k-1)-th hit to the location of the k-th hit Process noise takes random perturbations into account (e.g. multiple scattering, radiation)Process noise takes random perturbations into account (e.g. multiple scattering, radiation)

14 Prediction vs. Measurement Measurement & covariance matrix at (k) Residual Projection matrix Projection matrix H k connects parameter vector (e.g. 5D) and the actual measurement (e.g. 1D)Projection matrix H k connects parameter vector (e.g. 5D) and the actual measurement (e.g. 1D) Measurement equations

15 Some Math: Filter Filtered parameters & covariance matrix at (k) “Gain matrix” Filter equations In this formulation (“gain matrix formalism”), the matrix that needs to be inverted has only the dimension of the measurement (here: 1) In this formulation (“gain matrix formalism”), the matrix that needs to be inverted has only the dimension of the measurement (here: 1)

16 Along the Trajectory Traditionally, the Kalman filter proceeds in the direction opposite to the particle’s flight – parameter estimate near point of origin contains information of all hits & is most precise production vertex direction of flight   direction of filter production vertex

17 Along the Trajectory (cont’d) If precise parameters at both ends are needed, two filters in opposite directions can be combined production vertex direction of filter 1   direction of filter 2 production vertex

18 Along the Trajectory (cont’d) The orthodox method of propagating the full information to all points of the trajectory is the “Kalman smoother” Excellent, but computing intensive – one parameter vector size matrix to invert per step production vertex direction of flight   direction of filter direction of smoother  production vertex

19 Process Noise & How to Calculate It Important: multiple scattering model evaluate contribution to covariance matrix – depends on track model (example is for t x = tan  x, t y = tan  y )  angular elements of Q (t = thickness in terms of radiation length)

20 Extended (“thick”) Scatterers In this case, also the spatial components of the process noise matrix Q are non-zero (l = thickness in terms of radiation length, D=direction)

21 28-Jul-2004R. Mankel, Kalman Filter Techniques21 Nonlinear fit With non-linear transport or measurement equation, generalizations are necessary Optimal properties are retained if linear expansion is made in the right places – in general, this requires iteration

22 28-Jul-2004R. Mankel, Kalman Filter Techniques22 Nonlinear fit (cont’d) Knowledge of derivatives important – for helical tracks, calculate analytically – for a parameterized inhomogeneous field, transport & calculation of derivatives are usually done numerically e.g. embedded Runge-Kutta method (adaptive step size) see T. Oest, HERA-B notes and , and A. Spiridonov, HERA-B note In case derivatives depend on parameters, iteration may be needed

23 28-Jul-2004R. Mankel, Kalman Filter Techniques23 Outlier Removal In least squares fitting, outlier hits have bad influence on the parameter estimate – outliers should be removed The traditional method of removing outliers is based on the  2 contribution of the hit to the fit – in Kalman filter language: smoothed  2 s Problems: – good hits can have a worse  2 than bad hits nearby that are causing the problem – “digital” decisions may result in bad convergence

24 28-Jul-2004R. Mankel, Kalman Filter Techniques24 Robust Estimation Least squares fitting (& thereby Kalman filtering) reaches its limits when underlying statistics are far from Gaussian – typical example:  2 distributions in presence of multiple scattering This problem is more pressing in electron fitting with plenty of material  radiation – for general treatment, see Stampfer et al, Comp.Phys.Comm. 79, 157

25 28-Jul-2004R. Mankel, Kalman Filter Techniques25 Kalman Filter & Pattern Recognition Kalman filter can be used very efficiently at the core of track following methods – “Concurrent track evolution” – “Combinatorial Kalman filter” within & without magnetic field see for example Nucl. Instr. Meth. A395, 169; Nucl. Instr. Meth. A426, 268 will not be discussed in detail here will not be discussed in detail here

26 28-Jul-2004R. Mankel, Kalman Filter Techniques26 Further Reading Many excellent papers exist, which unfortunately cannot be done justice by listing them all here A review of tracking methods with many references to the original literature can be found in R. Mankel, Rep. Prog. Phys. 67 (2004) 553—622 (online at

27 Cosmic Rays

28 History 1785 Charles Coulomb, 1900 Elster and Geitel Charged body in air becomes discharged – there are ions in the atmosphere 1912 Victor Hess Discovery of “Cosmic Radiation” in 5350m balloon flight, 1936 Nobel Prize 1902 Rutherford, McLennan, Burton: air is traversed by extremely penetrating radiation (  rays excluded later) 1933 Sir Arthur Compton Radiation intensity depends on magnetic latitude 1933 Anderson Discovery of the positron in CRs – shared 1936 Nobel Prize with Hess 1938 Pierre Auger and Roland Maze Rays in detectors separated by 20 m (later 200m) arrive simultaneously 1985 Sekido and Elliot “Somewhat” open question today: where do they come from ? 1937 Street and Stevenson Discovery of the muon in CRs (207 times heavier than electron) First correct explanation: very energetic ions impinging on top of atmosphere

29 Victor Hess, return from his decisive flight 1912 (reached 5350 m !) radiation increase > 2500m

30

31 Satellite observations of primaries Primaries: energetic ions of all stable isotopes: ~85% protons, ~12%  particles Similar to solar elemental abundance distribution but differences due to spallation during travel through space (smoothed pattern) Major source of 6 Li, 9 Be, 10 B in the Universe (some 7 Li, 11 B) Cosmic Ray p or  C,N, or O (He in early universe) Li, Be, or B

32 NSCL Experiment for Li, Be, and B production by  +  collisions Mercer et al. PRC 63 (2000) MeV Measure cross section: how many nuclei are made per incident  particle Identify and count Li,Be,B particles

33 Cosmic Ray (Ion, for example proton) Atmospheric Nucleus oo --   ee ee  ee Electromagnetic Shower oo --  (mainly  -rays)   (~4 GeV, ~150/s/cm 2 )  Hadronic Shower (on earth mainly muons and neutrinos) (about 50 secondaries after first collision) Ground based observations Space Earth’s atmosphere Plus some: Neutrons 14 C (1965 Libby)

34 Cosmic ray muons on earth Lifetime: 2.2  s – then decay into electron and neutrino Travel time from production in atmosphere (~15 km): ~50  s why do we see them ? Average energy: ~4 GeV (remember: 1 eV = 1.6e-19 J) Typical intensity: 150 per square meter and second Modulation of intensity with sun activity and atmospheric pressure ~0.1%

35 Ground based observations Advantage: Can build larger detectors can therefore see rarer cosmic rays Disadvantage: Difficult to learn about primary Observation methods: 1) Particle detectors on earth surface Large area arrays to detect all particles in shower 2) Use Air as detector (Nitrogen fluorescence  UV light) Observe fluorescence with telescopes Particles detectable across ~6 km Intensity drops by factor of 10 ~500m away from core

36 electrons  -rays muons Ground array measures lateral distribution Primary energy proportional to density 600m from shower core Fly’s Eye technique measures fluorescence emission The shower maximum is given by X max ~ X 0 + X 1 log E p where X 0 depends on primary type for given energy E p Atmospheric Showers and their Detection

37 Air Shower Physics The actors: Nuclei composed of nucleons N (p,n) Pions: π +, π -, π 0 Muons: μ +, μ - Electrons, positrons: e +, e - Gamma rays [photons]: γ The actions: N + N  lots of hadronic particles and anti-particles (mostly pions, equal mix of π+,π -,π 0 ) π ± + N  lots of hadronic particles and anti-particles (mostly pions, equal mix of π +,π -,π 0 ) π ±  μ ± + ν (decay lifetime is 1/100 muon lifetime) π 0  γ + γ immediate decay ( sec) γ  e + + e - (and recoiling nucleus) [“pair production”] e ±  e ± + γ (and recoiling nucleus) [“bremsstrahlung” or “brake radiation”]

38 Air shower building block: The electromagnetic cascade Pair production and bremsstrahlung In this simplified picture, the particle number doubles in each generation. Each generation takes one radiation length (37 g/cm 2 in air). The cascade continues to grow until the average energy per particle is less than an electron loses to ionization in one radiation length (81 MeV). It is then at its maximum “size,” and the number of particles then decreases. γ e+e+ e-e- e+e+ γ γ e-e- e+e+ e+e+ γ γ e-e- e-e- e-e- e+e+ Each π 0 decay produces two photons (γ’s), which transfers energy from the “hadronic cascade” to the “electromagnetic cascade.”

39 Particle detector arrays Largest, prior to Pierre Auger project: AGASA (Japan) 111 scintillation detectors over 100 km 2 Other example: Casa Mia, Utah:

40 Air Scintillation detector 1981 – 1992: Fly’s Eye, Utah : HiRes, same site 2 detector systems for stereo view 42 and 22 mirrors a 2m diameter each mirror reflects light into 256 photomultipliers see’s showers up to km height

41

42 Fly’s eye

43 Fly’s Eye

44 Fly’s eye principle

45 Pierre Auger Project Combination of both techniques Site: Argentina + ?. Construction started, 18 nations involved Largest detector ever: 3000 km 2, 1600 detectors 40 out of 1600 particle detectors setup (30 run) 2 out of 26 fluorescence telescopes run

46 Other planned next generation observatories OWL (NASA) (Orbiting Wide Angle Light Collectors) Idea: observe fluorescence from space to use larger detector volume EUSO (ESA for ISS) (Extreme Universe Space Observatory)

47 Energies of primary cosmic rays ~E -2.7 ~E -3.0 ~E -2.7 Observable by satellite Man made accelerators ~E -3.3 Lower energies do not reach earth (but might get collected) 40 events > 4e19 eV 7 events > 1e20 eV Record: October 15, 1991 Fly’s Eye: 3e20 eV UHECR’s: many Joules in one particle!

48 Origin of cosmic rays with E < eV Direction cannot be determined because of deflection in galactic magnetic field Galactic magnetic field M83 spiral galaxy

49

50 Precollapse structure of massive star Iron core collapses and triggers supernova explosion

51 Supernova 1987A by Hubble Space Telescope Jan 1997

52 Supernova 1987A seen by Chandra X-ray observatory, 2000 Shock wave hits inner ring of material and creates intense X-ray radiation

53 Cosmic ray acceleration in supernova shockfronts No direct evidence but model works up to eV: acceleration up to eV in one explosion, eV multiple remnants correct spectral index, knee can be explained by leakage of light particles out of Galaxy (but: hint of index discrepancy for H,He ???) some evidence that acceleration takes place from radio and X-ray observations explains galactic origin that is observed (less cosmic rays in SMC)

54 Ultra high energy cosmic rays (UHECR) E > 5 x eV Record event: 3 x eV 1991 with Fly’s eye About 14 events with E > known Spectrum seems to continue – limited by event rate, no energy cutoff Good news: sufficiently energetic so that source direction can be reconstructed (true ?) Isotropic, not correlated with mass of galaxy or local super cluster

55 The Mystery Isotropy implies UHECR’s come from very far away But – UHECR’s cannot come from far away because collisions with the cosmic microwave background radiation would slow down or destroy them (most should come from closer than 20 MPc or so – otherwise cutoff at eV (FOR PROTONS…!) Other problem: we don’t know of any place in the cosmos that could accelerate particles to such energies (means: no working model) Speculations include: Colliding Galaxies Rapidly spinning giant black holes Highly magnetized, spinning neutron stars New, unknown particles that do not interact with cosmic microwave background Related to gamma ray bursts ? Easy explanations: the highest energy UHECRs might not be protons, but rather heavier nuclei (heavier nuclei have a somewhat higher cutoff). Systematics on energy scale measurements from AGASA…

56 Possible Solutions to the Puzzle AGASA Data HIRES Data 1. Maybe the non-observation of the GZK cutoff is an artefact ? 2. Maybe intergalactic magnetic fields as high as ~micro Gauss cutoff seen ? then even UHECR from nearby galaxies would appear isotropic problem with systematic errors in energy determination ?

57 The structure of the spectrum and scenarios of its origin supernova remnantspulsars, galactic windAGN, top-down ?? knee ankletoe ?


Download ppt "Track Fitting (Kalman Filter). Least Squares Fitting Generally accepted solution: Kalman filter – at Gaussian level optimal correction of multiple scattering."

Similar presentations


Ads by Google