Particle Identification

Particle identification: an important task for nuclear and particle physics

Usually it requires the combination of informations from several detectorsExample: a charged particle in a magnetic field Under suitable conditions, the radius R of the trajectory is related to the momentum and charge R =p/zThe velocity may be obtained from a time-of-flight measurement, since it is proportional to 1/The energy loss E of a charged particle may be described by the Bethe-Block formula, where dE/dx is proportional to z2

All these informations may be combined to identify low energy particles

At low energies there is need to identify and discriminate ● particles with different Z (protons, He, Li, light nuclei,…) ● different isotopes of the same Z (p/d/t, 3He/4He,…)

This is usually accomplished by combined dE/E and TOF techniques

At high energies particles have all Z=1 and most of them have relativistic speed There is need to identify and discriminate ● electrons/muons/pions/kaons/protons ● electrons/photons/neutral pions

This is usually accomplished by different techniques tuned to the different momentum ranges:dE/dx, TOF, Cherenkov, TRD,...

Particle identification techniques and detector geometries have to take into account the usual penetration capabilities of the different species.

Large experiments wish to detect and identify as much as possible all the particles produced in each event. To this aim, a combination of different detectors and tehniques is used, to have good reconstruction efficiency in a large momentum range (0.1 – few GeV/c).For instance, in ALICE, a combination of the information from ITS (silicon), TPC, TOF, TRD and RHIC detectors is used.When the track is identified in more than one detector, the combined information is taken into acount.

Particle identification in the silicon detectors

The measurement of energy loss in thin silicon layers may be used for PID in the non-relativistic region.

Previous equation only gives the average value of the energy loss. However, the energy loss is a statistical process with fluctuations.

Two cases:a) Thick absorbers (large number of collisions):

Gaussian distributionb) Thin absorber (small number of collisions):

extremely difficult to evaluate. Approximation: Landau distribution

Landau distribution

Long tail in the high energy side, asymmetric distribution

Most probable energy loss

Mean energy loss

Simulation of dE/dx in keV and MIP units for pions of 830-930 MeV/c in a silicon detector

The truncated mean approach: in a multi-layer detector, evaluate the mean excluding one or more values, especially the largest ones which may come from the high energy tail.

ΔE1 ΔE2ΔE3 ΔE4

ΔE5 ΔE6

ΔE-p plot for pions, kaons and protons in the ALICE ITS (6 layers).The truncated mean approach is used.

How to identify

such particles?

Distribution of truncated mean energy loss in the ALICE ITS for pions, kaons and protons of 400 MeV/c

Separation between particle species is made, for each given momentum interval, by the PID probability for each type of particle.

(PID probability)i = gi/Σgi

gi = gaussian value for each species(centroid from Bethe-Bloch function, with a σ

The Bayesian approach

The probability to be a particle of type i if a signal s is observed - w(i | s) – depends on the conditional probability - r(s | i) - to observe a signal s if a particle i crosses the detector and on the “a priori” probabilities Ci (how often such particle species are detected).A priori probabilities may be estimated from simulations through event generators or from other detectors.

W(i | s) = PID weightsIdentify the particle according to the largest PID weight (with some threshold?)

PID results

DefinePID Efficiency = Ncorr/Ntrue

PID Contamination = Nincorr/(Nincorr+Ncorr)

Ncorr = No. of correctly identified particlesNincorr = No. of misidentified particlesNtrue = True number of particles of that species

Momentum dependence of PID efficiency and contamination for pions, kaons and protons in the ALICE ITS.

Particle identification with TPC

Again, the Bethe-Bloch function is used to describe the energy loss of particles as a function of their momentum, especially in the relativistic rise region.

The mean value (truncated mean) of the energy loss is gaussian, with a standard deviation determined by the detector properties.

dE/dx distribution for various particles of p=0.5 GeV/c

The general way to quantify the separation power between particles A and B is to consider the difference in energy loss compared to standard deviation

Typical examples of separation power as a function of momentum

Particle identification with TRD and RHIC

Such detectors are used for

TRD: Improve electron/pion separation by a large factor (pion rejection of 100 for large momenta)

RHIC: Improve the overall PID at very large momenta

(See specific lectures on the working principle of such devices)

TRD may contribute also to pion, kaon and proton PID

Particle identification with the TOF (Time-Of-Flight)

The time-of-flight technique may be used to separate particles with intermediate momenta (a few GeV/c), depending on the time resolution.In ALICE, the TOF system is built with MRPC (Multigap-Resistive-Plate-Chambers), with a time resolution around 80 ps.

In such conditions, a pion/kaon separation better than 3 σ is achieved up to 2.5 GeV/c, and kaon/proton up to 4 GeV/c.

The combined PID

The method can be applied to combine PID information from several detectorsConsider the whole detector of N different detectors:

Vector of PID signals in N detectors

If the PID measurements are uncorrelated over the different detectors,

The PID weights combined over the whole system of detectors will be

-If a detector is not able to provide PID, its PID weights are set equal, and its contribution cancels out in the product-When several detectors may provide PID, they contribute together, improving the overall information

Items not covered in detail:

Neutral particle identification: especially neutral pions and photons

Principal-Component-Analysis (PCA) of showers

A shower in a calorimeter may be characterized by different parameters:Lateral dispersion, two ellipse axes, sphericity parameter, core energy, largest fraction of energy deposited in a single crystal, …

Instead of working in a multidimensional space to find the cuts (which is not an easy task), the principal components may be obtained by diagonalization of the covariance matrix of the original parameters.The two most significant components (largest eigenvalues) allow to work in a 2-D space to find selection criteria.

Principal-Component-Analysis of showers in a photon detector

Neural-network approach

Several applications exist for particle identification with neural network algorithmsJust one example: π0 / γ discrimination in ALICE

Neural network structure:3 Layers: 1 input (N nodes = features vector) 1 hidden (2 N + 1 nodes) 1 output (1 node: 0 or 1)

Training with 2 event samples:1) Clusters with single photons2) Clusters with overlapped photons (from π0 decay)

Typical results in ALICE with the PHOS (Photon Spectrometer)

Probability of true photon identification

Probability of misidentification of a π0 as a photon