Tesis energy flowandclusteringalgorithms

Energy Flow and Clustering algorithms for the reconstruction

of physics objects in ATLAS

Tesis DoctoralDpto. Física Atómica, Molecular y Nuclear

Carmen Iglesias Escudero

OUTLINE LHC and ATLAS ATLAS Calorimetry Jet Physics in ATLASI. Energy Flow algorithm in ATLFAST

Underlying Events, Minimum Bias & Pile Up

II. Clustering Algorithms for VLE particles (simulated)

III. Clustering Algorithms for VLE data of Combined TB

LHC and ATLAS

LHC Physics LHC Setup LHC Experiments ATLAS Particle detection

LHC Physics

The physical origin of electroweak symmetry breaking and the origin of mass Higgs boson

The physical origin of CP violation Unitary triangle

Searches beyond the standard model supersimmetry, new gauge bosons, compositeness,…

Precision measurements of Standard Model parameters Top. Beauty, tau, QCD,…

The physics of strongly interacting matter at extreme energy densities quark-gluon plasma

The LHC will allow to explore the structure of matter at energy frontier andat the energy density frontier.

LHC Setup

ATLASALICE

CMS

LHCb

Over 1000 superconductive 8.36 Tesla (at 1.9 Kelvin) dipoles are needed to bend the 7 TeV protons in the 27 Km LHC circumference

ATLAS

General purpose detectors which will be Focused in the study of the p-p interactions.They will be used in further test of SM (HiggsBoson search) and in new physics search (supersimmetries, extra dimensions…).

Detector dedicated to the study ofheavy ions.

Detector dedicated to the study of B-physics (CP violation)

The design considerations for ATLAS detector are: good EM-calorimetry for e, γ identification and measurement. Hermetic jet and Emiss calorimetry. Efficient tracking at high luminosity for lepton measurements, b-quark tagging

and e, γ identification. τ and heavy flavour vertexing and reconstruction capability of some B decays.

ATLAS (A Toroidal LHC Apparatus)

Particle Detection

Only one detector can not measure the Energy/momentum of all particles

Each layer identifie and measure the energy non defined in the previous one

The photons and electrons deposit almost all their energy in EM CalorimeterThe hadrons deposit their energy in HAD calorimeterThe muons as has little interaction with the matter, arrive until the spectrometerThe moment from charged particles is measured from the curvature of the tracks in the inner detector

ATLAS Calorimetry

Electromagnetic shower Hadronic cascades EM calorimeter Hadronic Calorimeter: TileCal Physics issues for Calorimetry Energy resolution

Electromagnetic showers A high energy e or γ initiates a cascade of e

and γ’s via bremsstrahlung and pair production

until they fall below critical energy Ec

Characteristic length X0≡ radiation length

Shower can be fully measured or sampled.

Needs a depth of > 25 X0to contain a high energy em shower

The lateral development is governs by theMoliere Radius (average lateral deflection of critical energy electrons after 1 X0).

Mean distance in the absorver over wich a high-energye- reduces its energy by a factor 1/e only due to bremst.

RM = X0/EC

Hadronic cascades Similar to em shower but with

strong interaction responsible for cascading effect : Multi-particle production (π0,

π±, K etc..) nuclear break up until π

production threshold

Characteristic length λ≡nuclear interaction length

About 10λ necessary to contain 99% of energy of 200 GeV pion

High pt quarks/gluons hadronize giving narrow JETS

Mean distance between inelastic collision of hadrons with nuclei

EM Calorimeter

Accordion geometry benefits: No cracks in ϕ

The detection element is liquid Argon. The EM shower emit electrons in theArgon which are collected and register.

Provide a very precise energy reconstruction of e- and γPowerful tool for the particle identification due to its high granularity

Hadronic Calorimeter: TileCal

Barrelmodules

Extended Barrelmodules

64 modules

Sampling calorimeter:- Scintillators (active mat.) - Iron (absorber mat)

The tiles are placed in the perpendicular plane to the beam axis and the read out is performed by optical fibres and routing them to the PMTs.

Hadronic calorimeter• Rapidity coverage up to |η|=5.• Energy resolution:

• Linearity better than 2% up to 4TeV.• Granularity• ∆ηx∆φ =0.1x0.1 for |η|<3 ∆ηx∆φ =0.2x0.2 for 3<|η|<5• Jet tagging efficiency > 90%• Tolerance to radiation

Physics issues for Calorimetry

Electromagnetic calorimeter• Dynamic range: From few MeVs to TeVs • Good energy resolution:

• Good electron/jet and γ/jet separation • High granularity :– At least ∆ηx∆φ=0.03x0.03 for |η|<2.5– Longitudinal segmentation for particule ID• Tolerance to radiation

ATLAS calorimetry: Crucial role at the LHC:Detectors are required to measure the energy and direction of:

photons and electronsisolated hadrons and jets,the missing transverse energy (ET).

Energy Resolution

Jet Physics in ATLAS

Jet definition Fragmentation Initial parton to jet Hard scattering and Underlying Events Jet measurement

Jet : Group of energetic particles which are emitted spatially collimated.

Jets are manifestations of scattered sub-nuclear 'partons' (quarks & gluons)so due to partons cannot be isoleted, jets gives information about them.

A jet constains mainly hadrons: tens of neutral and charged pions, a lesser extent of kaons and very few light baryons(such protons and neutrons)

JET definition

Fragmentation Hard Scattering Elementary hard process: p-p interaction produces fundamental objets: quarks

and gluons (they can be seen as free particles). Parton shower: primary partons generate a shower

of partons because color forces will organize them into colorless hadrons involving the creation of many quark-antiquarks pairs.

Hadronization Hadronization: parton shower is transformed into the observed set of short-life

hadrons. Phenomenological models are used. Decay of unstable primary particles into stable hadrons and leptons according

to the lifetimes and braching ratios for each unstable particle.

Initial Parton to JetThe definition of a jet is not unique and the corresponence between parton energy and direction and measured jet characteristic is influenced by many factors: parton fragmentation, FSR, Underlying Events, detector response and by the jet algorithm

Hard Scattering & Underlying Events The 'Hard Scattering' components consists of

the outcoming two 'jets‘ which come from a hard 2 parton scattering which interact at short distance with large pT transfer.

The ‘underlying events’ is everything except the 2 hard scattered jets and consist of:-the beam-beam renmants: because protons are not elementary particles bur are formed by 3 quarks.

- ISR and FSR: interaction between quark and gluonsbefore and after the hard scattering.

- multiple interaction: a second, a third parton scattering...softer than hard scattering

Finally, in high luminosity, it is possible to have several collision between beam particles in the same beam crossing, ie, pile-up events.

Jet Measurement Each jet is characterized by :

a charged fraction: mainly π± a neutral electromagnetic fraction:

mainly photons from π0γγ decays a neutral hadronic one: mainly KLand neutrons.

The calorimeter is segmented in ϕ (azimuthal ang.) and η (pseudo-rapidity).

Jets are observed as clusters of ET locatedin adjacent cells with0.1x0.1 in η-ϕ

Jets used to be reconstructed with a cone centered in the cell with max ET and a radius R= √∆η2+∆φ2 around the center (usually R=0.4-0.7)

I. Energy Flow in ATLFAST Energy Flow algorithm Overlapping Resolution in ATLFAST Jet Generation (Pythia) and Reconstruction (Atlfast) Particle composition of the jets Analysis by Cell Applying Energy Flow

Energy Flow Algorithm About 2/3 of the jet energy are carried by charged particles (p±,K±...)

However jet algorithm makes no use of tracking information Energy Flow algorithm make an optimal use of the detector information combining

the measurement of the energy deposition in calorimeter cells with the reconstructed track in the inner detector to improve jet energy resolution and ET

Miss. Introduced first by LEP experiments .

For low momentum charged particles, the tracking error is much smaller than the calorimetric energy error. In example, for the Central Barrel in ATLAS (η=0):

where pT and E are in GeV. We can see, i.e for one π± of 10 GeV E resolution is 16 %while for PT is 1.3%.Energy Flow must be applied at pT<140 GeV.

So for charged particles, their energy resolution will be sustituted by the track momentum resolution better resolution in jet ET.

Track: σpT/pT = 0.036%pT⊕1.3%

Cal: σE/E = 50%/√E⊕3%

Energy Flow: Overlapping The use of the track momentum improves the resolution only works if cluster is

isolated. If the track shares a cluster with a neutral particle, the gain in resolution from track will be limited by loss of resolution from remaining cluster.

Efficiency of algorithm is limited by the overlapping between neutral and charged particles in the cell of the calorimeter. We need to know more about this effect and its influence in the analysis

Typical multi-jet event : 64% charged energy 25% photons 11% neutral hadron

Resolution in Atlfast ATHENA: Framework of ‘offline’ Software in ATLAS Atlfast: C++ Object Oriented implementation which provides a fast particle-level

simulation of the detector response and its later reconstruction, and allow: define the 4-momentum of the particles reconstruct clusters and jets inside the calorimeters characterize the tracks

In Atlfast no detailed simulation of particle shower neither of the tracks in the inner detector

only a parametrisation of calorimeter E resolutionand a simulation of efficiency and Pt resolution in Si detector.

Parametrisations were derived from Full Simulation studies:

Effects as overlap of particles inside the cell can be studied by Atlfast,HOWEVER when the influence of the shower is relevant Full Simulation.

EM Cal resolution( γ and electrons)

HAD Cal resolution(hadrons :π± and k±)

Si Detect resolution(track of e ±, µ ± , π± )

0.245/√Pt ⊕0.007 at η<1.4 0.306((2.4- η)+0.228) /√Pt ⊕0.007 η>1.4

0.5/√Pt ⊕0.03 at η<3.21.0/√Pt ⊕0.07 at η>3.2

0.0005(1+ η10/7000)Pt ⊕0.012

Generation with PYTHIA 6.2Generate 1000 events of QCD jets, applying in Pythia the next conditions:- for differents range of PT:

20-40, 40-80 , 80-160, 160-320, 320-640 and 640-1280 (GeV)- Without include Underlying Events and Minimum Bias effects- ISR and FSR are taken into account- |ηparton| < 5.0, to use only parton insider the calorimeter coverage

Release 6.2.0 is used for the reconstruction of QCD jets:- Cone algoritm is used with different values of radius R=0.4 and 0.7- |ηjet| < 2.0, to ensure the completed containment of the cone jet

inside Inner coverage (calo+track info used later)- Minimum Pt of the jet, to prevent excessive merging of noise and energynot associated with hard scattering. Different values depending on R(multiplicity of jets still significant)

Ptmin=20GeV if R=0.7 Ptmin=15GeV if R=0.4

Jet Reconstruction with Atlfast

Particle composition of jetsTo reconstruct jet ET from particle energy into the cone,we select: only stables particles deposited in Calorimeter

mainly charged hadrons (π ± and k ± ) Similar ammount of photons (from π0γγ) a too lesser extend of neutral hadrons (kLO & n) and very few leptons (e ± ,µ± and ν)

ET>0.5GeV for charged particles |ηpartc| < 2.5, only particles inside inner coverage

Total in jet

Charged had Neutral had Photonsper jet (%) per jet (%) per jet (%)

40-80 13.2 6.2 46.6 0.9 7.1 6.0 45.580-160 17.2 8.2 47.1 1.1 6.4 7.9 45.7

160-320 20.9 10.0 47.3 1.3 6.1 9.6 45.7

Total in jet

Charged had Neutral had Photonsper jet (%) per jet (%) per jet (%)

13.4 6.4 46.6 0.9 7.0 6.0 45.5

17.7 8.4 47.1 1.1 6.3 8.2 45.7

21.7 10.3 47.3 1.3 6.1 9.9 45.7

R=0.7R=0.4

mainly charged hadrons and photons the ammount of leptons is negligible (<0.5%)Number of particle increase with the E

MultiplicityCharged had Neutral had Photons

per jet (%) per jet (%) per jet (%)

40-80 22.6 61.2 4.6 12.5 9.2 25.2

80-160 40.3 61.3 7.8 11.8 16.9 25.6

160-320 69.1 61.4 13.1 11.9 28.9 25.7

Charged had Neutral had Photons

per jet (%) per jet (%) per jet (%)

24.15 61.1 4.88 12.4 9.2 25.2

42.62 61.3 8.19 11.8 11.7 25.7

73.50 61.4 13.98 11.7 30.7 25.7

R=0.4 R=0.7

ET deposited by particles increase as the ET of jet is biggermost of ET from charged had (2/3 parts), it is ∼double that photons ETEt per jet in R=0.7 is bigger than 0.4

Et deposited by particlesSo, for charged hadron we have 2 important results:

1) Their number is ~ 47% of the total particles2) Their deposited ET is ~ 61% of the total energy

Energy Flow is applied to the charged hadrons, BUT not to all only to the charged hadrons which hit cell without sharing with neutral particles,

Analysis by Cellsa) define the calorimeter CELL that the particles hitsGrid of 81 cells with 0.1 x 0.1 granularity in η-φ plane around deposition point of jet

b) classification of the cell based on the type of particle(charged or neutral) that fell in it

CHARGED CELLS: only charged partic (π ± and k ± )NEUTRAL CELLS: only photonsMIXED CELLS: mixed charged and neutral particles in this last case it’s analyzed the overlapping between charged and neutral particles

Et jet (GeV)

Charged Cells Neutral Cells Mixed Cells

per jet (%) per jet (%) per jet (%)

40-80 35.50 16.3 45.8 6.7 18.9 12.6 35.3

80-160 65.94 21.8 33.8 8.7 13.4 35.3 54.6

160-320 94.20 23.7 25.2 9.6 10.2 60.7 64.4

This proportion decrease quickly with the jet ET, as the same time as the energy in Mixed Cell increase.

ET deposited in cells

Up to 45% of total ET, in the best case, come from charged had in Charged cells. For this ET a gain in resolution will be done by Energy Flow

So, the overlapping will be bigger with the E, and the gain in resolution applying Energy Flow will be worse.

Improvement in ET of the jet(Range 40-80GeV and DR=0.4)

Aplying HAD Cal smearingto the CHARGED CELLS:

resolution in the jet energy~8%

Aplying INNER smearing

resolution in the jet energy~4.8%

much better result than with HAD Cal

0.0005(1+ η10/7000)Pt ⊕0.012 at η<2.5

Resolution of the jet energy have been improved in ~40%

0.5/√Pt ⊕0.03 at η<3.2

Variation of gain in resolution

- Very optimistic result: high gain in resolution using Energy Flow at low Pt~40 %- The improvement decrease with E.- At few 100 GeV the overlap of particles gets higher and the gain in resolution is marginal

RMS HAD

RMS INNER

(%)

40-80 0.079 0.048 39.080-160 0.062 0.042 31.0160-320 0.051 0.039 23.6320-640 0.041 0.034 16.9640-1280 0.032 0.029 9.6

R=0.4

RMS HAD

RMS INNER

(%)

40-80 0.076 0.049 35.780-160 0.062 0.043 30.7160-320 0.049 0.039 20.4320-640 0.039 0.033 16.6640-1280 0.031 0.029 9.5

R=0.7

Underlying Events, Minimum Bias & Pile Up

Soft physics processes The Underlying Event

Multiple Scattering with Pythia Influence in the multiplicity Ocupancy and Density Applying Energy Flow

Minimum Bias Event and Pile-UpNumber & ET of particles

Soft physics processesThere is no observable high-pt signature Physically a combination of several physical processes: mainly non-diffractive inelastic double diffractiveExperimentally depends on the experiment-trigger: Collider expts usually measure non-single diffractive(NSD)

Soft physics

Minimum bias

Underlying event Associated with high PT events:

Beam remnantsISRMore difficult to define experimentally and theoretically

•Underlying event is everything

except the two outgoing hard

scattered jets.

•In a hard scattering process, the underlying event has a hard component (initial + final-state radiation and particles from the outgoing hard scattered partons) and a soft component (beam-beam remnants).

The underlying eventHigh PT scatter

Beam remnants

ISR

When we add Underlying events :- Increase the multiplicity of charged hadrons (10%)

- Increase the multiplicity of photons (14%)

Influence in the multiplicity

Mean=7.0 Mean=7.8

Mean=7.1 Mean=8.1

Occupancy and DensityOCCUPANCY: number of particles which hit in each cell with a granularity 0.1x0.1.PTcut: ET>0.5GeV for charged particles

The occupancy is more than the double when we consider UE+QCDjets

DENSITY: number of particles which hit in ∆η =1, i.e. dN/dηWhen we apply the pT cut to charged hadronsthe density decrease.

UE Density = dN/dη ∼ (38-15) = 23 Similar results than previous analysis

A. Moraes studies:ATL-PHYSICS-2003-020

Applying Energy FlowOnly QCDjets QCDjets+UnderlyingEvents

RMS(Had)=0.079RMS(Inner)=0.048Gain(%)=39

RMS(Had)=0.079RMS(Inner)=0.049Gain(%)=38

Similar results:Underlying can be negligible

Forward productionLow multiplicityLarge Enegy

Central productionHigh multiplicitySmall Energy

MB consists of 4 processes: non diffractive, single diffractive,double diffractive and elastic Most popular models takes MB Events as non-diffractic inelastic.

A minimum bias event

Minimum Bias Events~ 7 charged partc/Δη

~ 8 neutral partc/Δη

Similar results to the shown in Calorimeter Performance ofATLAS and TDR

Multiplicity of particles in Δη

Pile-Up Events in PYTHIAPile-up events are taken by PYTHIA to be of the MinimumBias type.

PYTHIA can generate several events and put one after the other in the event record, knowing the assumed luminosity per bunch crossing expressed in mb-1.

Min biasPile Up

QCDjets

QCDjets+UE

with cut ET>0.5for charged had

So if we applied Energy Flow, the influence of the Pile-up events at low luminosity can be negligible.

Number of Particles

ETof Particles

Min biasPile Up

QCDjets

QCDjets+UE

Although at occupancy level Pile Up at low luminosity is of the order of QCDjets, the ET deposited by Pile-up Events is much smaller than the come from jets

Conclusions The application of the Energy Flow algorithm at particle level in

ATLAS can potentially improve the jet energy resolution.

This improvement is better at lower pT reaching values up to ∼40%of relative gain in resolution. Nevertheless, around 100 GeV the overlap between particles is higher anf the gain in resolution of the jet energy is marginal.

Respect to the soft process, the influence of the Underlying Events and the Pile-Up events at low luminosity can be neglegible for Energy Flow resolutions.

II. Clustering Algorithmsfor VLE particles (simulated)Why clustering algorithms…? Samples used Clustering algorithms in ATLAS TopoCluster analysis: EM Noise Lower threshold for Seed and Neighbor cells

Cone algorithms Clustering comparison TopoCluster with electronic Noise

Why Clustering is useful for EFlow?

Samples used DC1 samples of pions and neutrons (the main components of jets)

at very low ET (pT =1-30 GeV). Used to generate ntuples with 1000 events at η=0.3 (central barrel) and φ=1.6 of :

π’0s, to understand the behavior of photons inside the EM calorimeter. π’+s and neutrons, to know more about the hadronic shower.First, without electronic noise applied and later with it.

π0photonselectrons

positrons

e-and q γ

π+

π0

π-

neutron

proton

The shower of the π0 has only e.m. components!!!

Shower composition

Total energy deposited For the π0’s, as there are only e.m. particles we expect

having all the ET deposited in the E.M calorimeter For π+’s and neutrons the situation is different. Although,

at high pT their ET is usually deposited only in HAD calo,at very low energy, they also deposited their ET in EM calo.This deposition decrease with the ET of the particles.

Clustering Algorithms in ATLAS Sliding Window (SW) Clustering

EGAMMA Clusters Combines Inner detector tracks informationwith calorimeter clusters (SW) using 5 x 5 cells for cluster Useful for the identification of the e.m objects(photons and electrons).

TopoCluster AlgorithmTo reconstruct hadronic shower, the ET depositions from closed cells is merged to clusters

Cluster is built around a Seed Cell which has an ETabove a certain threshold (Seedcut). The neighbours are scanned for their ET and are added to the cluster if this ET is above the neighborcut. Then the neighbors of the neighbors are scanned and so on.The cuts depend on the noise in each cell

phi

eta

Seed Cell

Neighbour Cell

Simple search for local maxima of ET deposit on a grid using a fixed-size “window” of adjacent cells in η-φ space.

Default value is 5 x 5 cells in each cluster. Another values: 3x5 cells (unconverted photons) 3x7 cells (e- and converted γ).

Clustering comparisonFirst, calculate the ET deposited in all CELLs of the calorimeter and consider it as the “reference Energy Flow”, i.e., the best resolution that could be reach for the most sophisticated algorithm taking into account the whole ET in all the calorimeter.

For π0’s, compare the resolution of “reference Energy Flow” with the resolution of: Sliding Window Cluster/EGAMMA cluster TOPOcluster in EM calrim ∆R cone around seed

For neutrons, compare the resolution of “reference Energy Flow” with : TOPOcluster in EM and Tile ∆R cone around seed

For π+’s, compare the resolution of “reference Energy Flow” with : TOPOcluster in EM and Tile ∆R cone around seed PT of TRACKS from XKalman

TopoCluster Analysis: EM NoiseCompare different ways of reconstructing TopoCluster at VLE particles, to find:the best ET resolution the largest amount of ET deposited inside the cluster. Use these thresholds:

And checking different thresholds for EM Noise: EM Noise=10 MeV (lower than realistic case, only useful for checking

VLE particles) EM Noise=70 MeV (Fix Value by default for EM cal) CaloNoiseTool=true (package with a model for the electronic noise)

•Resolution from pT of TRACKSis the best result, but it get worse as the ET of particle increases.•Respect to the calorimeter ET, the best resolution comes from the ET deposited in all calo cells.•Around 30 GeV, ET resolution get better than pT resolution limit of Energy Flow algo

π+’s resolution

neutrons resolution

The worst result is at 1 GeV: •ET very similar to the mass of neutron~940MeV.

For the TOPOclusters CaloNoiseTool is the most realistic simulation of Electronic Noise. The rest of the analysis will be done using it.

π0’s have better resolutionthan π+’s and neutrons For Sliding-Window clusters, always are obtained the same results as EGamma. Best result for all calo cells, and next for EGamma cluster.

π0’s resolution

• For all TopoClusters at 1, 3 and 5 GeV their multiplicity is very low. Results have non-sense -> ET resolution increase instead of decreasing with ET.

Lower threshold for TopoCluster Loss of ET deposited in TOPOcluster due to the low multiplicity of these clusters

It’s needed to move for lower threshold for Seed and Neighbor cells:

Seed_cut: E/σ= 30 6, 5, 4… Neigh_cut: E/σ= 3 3, 2.5, 2…

The low efficiency of TopoClusters has been practically eliminated, mainly in π0’s case. The worst results is for neutrons at 1 GeV, but it also improves with the changed cuts.

For π0’s, the resolution of TOPOclusters usingany of these new cuts is even better than the resolution of EGamma.

For π+’s and neutrons, the best resolution for TOPOcluster using Seed_cut=4 and Neigh_cut=2.

The TOPOcluster resolution is more similar to the resolution of the ET deposited by all calorimeter cells

Deposited Energy

For π+’s and neutrons,changing the Seedcut from 30 to 4, a large increase inthe deposited ET is obtained, mainly at 1-5 GeV (the ET is almost the double)

For π0’s, with the new cuts, theValues of deposited ET for Topoare very similar to the EGammaone and competitive respect tothe ET in all the cells.

Cone algorithms The ET of the clusters is reconstructed from the ET of all cells inside

a cone with a radius ∆R=√∆η2+∆φ2

Different strategies are followed for the different type of particle

In principle, it’s used a cone with ∆R<1.0 in this first contact, only it’s required to select the cone algorithm with the best resolution.

Neutral pions- Cone’s centred in η-φ coord of EGAMMA cluster- Cone’s centred in η-φ coord of TOPO cluster in EM cal- Cone’s centred in η-φ coord of TRUTH generated π0Charged pions - Cone’s centred in η-φ of TRUTH generated π±- Cone’s centred in η-φ of TRACK position at 2nd layerNeutrons- Cone’s centred in η-φ of TRUTH generated neutrons

But with ∆R<1.0 I’m taking into account more than one shower in the same cluster.It’s needed to defined a smaller ∆R, different for each type of particle

For π0’s and neutrons:- Cone’s centered in η-φ coord of TRUTH generated partcFor π±’s:- Cone’s centered in η-φ of TRACK position at 2nd layer

Defined ∆R of the cone algorithm For π0’s:

For π±’s:

For neutrons: as their shower will be as wide as the π±'s ones, the same values for ∆R will be checked:

From “Calorimeter Performance” analysis the cluster size are (for E<100GeV): Unconverted photons: 5x3 cells ∆φ= 0.0625 ∆η=0.0375 (∆R<0.073) Converted photons and electrons : 7x3cells ∆φ= 0.0875 ∆η=0.0375 (∆R<0.095)

For the reconstruction of the clusters from π0’s, will be used: ∆R <0.1 for starting, because I’m using very low ET ∆φ= 0.0875 ∆η=0.0375 : 7x3cells ∆φ= 0.0625 ∆η=0.0375 : 5x3 cells ∆R<0.0375: 3x3 cells

From LAr TestBeam analysis, the cluster size for pions: 7x7 cells (∆R<0.12), 9x7 cells (∆R<0.16), 11x11 cells (∆R<0.20)…

For the reconstruction of the clusters from π±’s: ∆R <0.4 ∆R<0.2 ∆R <0.1

∆R>0.1, ∆R<0.2 and ∆R<0.4

ET Resolution

For π±’s the best resolution for TRACK-cone with ∆R<0.4, but with ∆R<0.2. I have also a good resolution and it let me a better definition of the shower of only one π±.For neutrons: the best resolution with ∆R<0.4, but ∆R<0.2 is still very good resolution.

In both cases, ∆R<0.1 is too strict to defined hadronic particles.

The best resolution is for ∆R<1.0, but it includes more than the shower of one particle.

For π0’s: Resolution with ∆R<0.1 is the better. Clusters with 7x3 and 5x3 cells gives us goodresolution but not enough.3x3 is too strict. Theycould be useful when elect noise will be applied

The previous results from Cone algorithm are the best of all.

Anyway, the results from TOPO algorithm with Seed_cut=4 and Neigh_cut=2 are very competitive with them.

EGAMMA-cluster give worse resolution, in general, than TOPO and Truth-cone, except at 1 GeV.

Clustering Algorithms Comparison

π±’s π0’sneu

Topocluster with Electronic NoiseThe values of have increased, now the ET deposited in TopoCluster comes from the generated particles, but also from the electronic noise

Asking for a minimum value of ET in Seed Cell and Neighbor cells: Seed Cell >200MeVNeighbor cells >80MeV

a similar value of without noise is obtained.After these cuts, the size of theTopocluster is up to 14 timessmaller.This difference is more importantfor the EM calo because there thelevel of noise with respect to thesignal is bigger.

π±’s π0’sneu

The ET resolution get worse with the application of these cuts there is a loss in energy reconstruction of the clusters. WHY?Because we have applied a general threshold to the ETcell for all calorimeter, and the electronic noise contribution is different in each layer of LAr and Tile.

Seed Cell >200MeVNeighbor cells >80MeV

Conclusions WITHOUT NOISE:

The best E resolution for VLE particles is obtained with cone algorithms

TopoCluster is a very competitive algorithm but doing the changes: Using CaloNoiseTool to model the EM Noise Applying lower thresholds to Seed and Neighbor cells:

SeedCut=4 and NeighborCut =2TopoClusters is event better than EGamma cluster for π0’s.

WITH NOISE: The E resolution get worse for TopoCluster If we try to remove electronic noise, we get a loss in ET from particles

It will be needed to applied ET thresholds in each layer of LAr and Tile

III. Clustering Algorithms for VLE data of CombinedTB

Combined TestBeam Setup Physcics Samples Energy reconstruction Particle Selection

First method: using sample D as a muon vetoSecond method: Using the longitudinal profileThird method: using MDT information

Clustering info in CBT ntuples ET resolutions

The electron sample Separate pions from muons

A full slice of the ATLAS experiment has been tested with beams of different particles (π’s, µ’s, γ, electrons and protons), at different energies (1-350 GeV) and polarities.Inner Detector: 3 layers of Pixel, 4 layers of SCT and 2 modules-barrel slice of TRTBarrel EM and HAD calorimeter: 2 barrel modules of EM LAr calo and 3 barrelmodules of HAD TileCal + 3 extended barrel modules of HAD caloMuon spectrometer:

Physics samples events from 1 to 9 GeV at eta=0.35, with Calo info (LAr+Tile) and

the tracks info from TRT only (pixels have problems) 100 k events for each point Mixture of e, π and µ Reconstruction with release 9.1.1

Separate the different kind of particles Evaluate the fraction of e, π and µ Apply clustering algorithms

Ntuples were generated by Vincent with the default values of RecExTB:

castor/cern.ch/atlas/ctb/test/real_data/reconstruction/Combined/

Energy #Run 1 GeV 2101077

2 GeV 2101078

3 GeV 2101079

Energy #Run 7 GeV 2101085

8 GeV 2101048

9 GeV 2101049

Energy #Run

4 GeV 2101080

5 GeV 2101047

6 GeV 2101084

Energy Reconstruction E = Sum of cells with

|Ecell| >σpedestal

Only cells in a small volume around the beam axis

For LAr0.25 < η < 0.45-0.15< ϕ < 0.15

For TileCal0.20 ≤η≤ 0.50 -0.1< ϕ < 0.1(cells A3, A4, A5, BC3, BC4, BC5, D1, D2)

Because the hadronic shower is wider than the electronic one, and the most of the deposition comes from pions in Tile.

Particle selection Selection of good tracks

trk_nTracks==1Only 1 track trk_nTrtHits[0]≥20 More than20 hits per track

to separate e from π/µ Cherenkov2 counter cut

for electrons: sADC_C2>650 for π/µ: sADC_C2<650

high-level hits (improves theCherenkov efficiency) for π/µ: nHL>5 for π/µ: nHL≤2

The Electron sampleElectrons are selected requesting:

sADC_C2>650 Cherenkov2 counter cut nHL>5 number of high-level hitsNo energy in TileCal sample D : to remove the µ contamination

Separate pions from muonsBoth pions and muons are:

sADC_C2<650 Cherenkov2 counter cut nHL≤2 number of high-level hits

First method: using sample D as a muon vetoAssuming that only muons can reach sample D and π signal is only comingfrom pedestal, we put the cut:

ADVANTAGE: method very efficient andeasy to reproduce with MCDISADVANTAGE: we can reject pions that reach the sample D, getting a bias.

In order to avoid it, different strategies are followed depending on ET:a) below 6 GeV : using TileCal last sample as a muon veto. It is supposed that there is no ET in Sample D from pions (only pedestal)b) above 6 GeV : use another method longitudinal profile in TileCal

Second method: Using the longitudinal profile

Using the fact tha muons leave their ET uniformly in the detector(normalizing by the path lenght)

E ∝path in matter

For ET>6GeV, different conditions are applied to , and

In LArThe contamination of muons increase when E decreasesThe number of electrons and pions decrease at low energies

totalelectronsmuonspions

Clustering info in CBT ntuples Emcluster: clusters from the sliding window algorithm

Tbemclusters: clusters from an algorithm used inprevious test beam. It has been added to allow comparison.It’s a window of 3x3 cells.

Emclusters and tbemclusters use only cells from the LAr calorimeter.

Cmbclusters: sliding window clusters but they are done on towers (LAr+Tile) and not anymore on cells. It is not working for the moment because of a coordinate problem between LAr and Tile.

Topo_EM and Topo_Tile cluster: Finds a seed cell, then cluster expands by checking energy in neighboring cells. Thresholds for seed and neighbors can be changed. The default values are: seed threshold is E/σnoise>6 neighbor threshold is E/ σnoise>3

(Hadronic TopoCluster is the sum of Topo_EM and Topo_Tile)

e- in Lar: Energy distributionFor electrons at 9 GeV

For electron it seems as the cuts on TRT works good

e- in LAr: Number of Clusters#particles and #cluster is very similar #clusters is very similar between them for each ET value.

(*) There is a cut (E>2 GeV) in this algorithm by definition

#clusters is very low

#clusters defined increase with the energy.

e- in LAr: ResolutionsSW SW_TB TOPO_EM

9 GeV 7.57 8.92 10.48

8 GeV 8.51 10.04 11.64

7 GeV 7.85 6.93 8.51

6 GeV 8.83 7.81 9.62

5 GeV 13.07 15.47 17.34

4 GeV 11.04 11.47 14.78

3 GeV 9.59 (*) 14.38 20.39

2 GeV ---(*) 20.51 34.99

1 GeV ---(*) 80.75 48.38

The best resolution is for SW, but at 1-3 GeV we have bad results.

TOPO obtain the worst resolutionsmaybe it will be needed to change the thresholds for seed and neighbor cells.

In general, the E resolution is better when E increases

E resolution slightly better than it’s expected, WHY?Maybe problems in the reconstruction chain

(*) There is a cut (E>2 GeV) in this algorithm by definition

Improvement in the resolution of electronsNew release of Athena is used:

Optimal Filtering is applied in LAr signalProblems in the reconstruction chain have been solved.

Now the TopoCluster is the global cluster for Lar+Tile calo:”super3D”, as well asnew values are used for the thresholds:

There is a important improvement of the resolutionThe values are of the order that are expected for VLE particles

Results for pions and muons

Results are very difficult to interpert, because there is still a mixing of µ’s and π’sat energies above 7 GeV

New method to separate µ’s and π’sThird method: using MDT information

Using the variable nMDTdigto count the number of hitsin the different MDT stations

We can assume that events with more than 8 digits in a MDT stations are muons (because we have 8 plans tubes per station)

After applying these cuts, the correct separation of π’s from µ’s above 7 GeV it’s possible

#TopoClusters is very similar to #particles, so the clustering methodseems to works well.

The resolution from π’s is rather similar,nevertheless the most important resultsis the improvement in resolution for µ’s.

Conclusions The reconstruction of very low energy particles it’s possible with the

tools available in the reconstruction package for the Combined TB inside Athena.

For the recostruction of 1-9 GeV e-, the two Sliding Windows algo are usefull, and the Topocluster results are very competivie with them. The energy resolutions obtained are of the order that it is expected Nevertheles, it will be necessary to apply some changes in the ET thresholds of SW to

can apply them at 1-3 GeV e.m. particles

The reconstruction of π’s and µ’s, first nedeed of a very accuracy separation of them. We conclude to use the Sample D as muon veto for E<6GeV and the MDT cuts for larger energies.The values of E resolutions obtained are inside the expected ones. However, it will be interesting a tunning work to adapt the E threshold more properly to

VLE particles (as in the previous simulation analysis)