Nuovi algoritmi di b taggingNuovi algoritmi di b tagging

Alessia TricomiAlessia Tricomi

Università and INFN CataniaUniversità and INFN Catania

TISB – Firenze 15-16 Gennaio 2003TISB – Firenze 15-16 Gennaio 2003

Alcune considerazioni sullo stato dei toolsAlcune considerazioni sullo stato dei tools

Cosa c’è di pronto– Track counting e Probabilistico a “la ALEPH” già inseriti da Gabriele

nel suo framework Nuovi algoritmi “stand alone”

– Likelihood ratio Implementazione Primi risultati mostrati durante la CMS Week di Dicembre

– Leptonico Idea Implementazione Problemi

Cosa serve– C’è un framework sviluppato da Gabriele che integra già alcuni

algoritmi (vedi presentazione di Gabriele) in cui però bisogna integrare i nuovi algoritmi ed eventualmente renderlo più flessibile

– Occorre un pacchetto semplice e user friendly per la calibrazione degli algoritmi probabilistici

Cosa vi mostro ora?Cosa vi mostro ora?

Un riassunto del Likelihood Ratio con i primi risultati Uno schema del btagging con leptoni

– Una discussione sui problemi

Rimandiamo la discussione su cosa serve e come farlo alla fine delle presentazioni…

The main ideaThe main idea

b-jet tagging made exploiting long lifetime of b:– the algorithm relies on tracks with large impact parameter

– Both transverse and 3D IP have been used

– A probabilistic approach is used

Sign is positive if track appears to originate in front of PV

Negative if it appears to originate from behind

Significance of IP is defined as the ratio of the signed IP to its total error: sign x |d0|/(d0) (take better in account experimental resolutions)

d oPV

Linearised track

JetJet

HelixHelixPa

Probabilistic algorithmsProbabilistic algorithms

Tracks from b hadron decays have usually LARGE and POSITIVE IP

Tracks coming from the PV and badly reconstructed tracks have a 50% chance to be assigned a negative significance

Tracks with negative IP d used however to measure the intrinsic resolution (due to limited resolution on track reconstruction, primary vertex finding, b-jet reconstruction)

Confidence level that a track with IP significance SD originates from the primary vertex given by:

By construction the C.L. is flat for tracks coming from primary vertex while is peaked at small values for tracks coming from displaced vertices

)(:|)(|

d

dSSR

DS

DT dxxRSP )()(

Probabilistic algorithmsProbabilistic algorithms

Starting from the C.L. the probability that a set of tracks is coming from the PV can be evaluated

In the Likelihood ratio method however both bkg and signal information are used:– b-jet to bkg-jet ratio: builds a global likelihood ratio based

on the distributions of SD expected for b-jets and bkg (gluon or uds) jets. The sum over all selected tracks of lg(ratio) provides discrimination between jets wich contain long-lived particles and those which do not.

Likelihood ratio: the methodLikelihood ratio: the method

Main step of the method: For each track i in a jet the significance Si is evaluated The ratio of the significance probability distribution

functions for b and u-jets is computed: ri= fb(Si)/ fu(Si) A jet weight is constructed from the sum of logarithms of

the ratio: W=log ri

By keeping jets above some value of W, the efficiency for different jet samples can be obtained

The rejection will have to be optimised for each specific bkg under study

Several track quality cuts applied

Samples & Track SelectionSamples & Track Selection

Monte Carlo samples:– bb, cc and uu events– Jet Transverse Energies: 50, 100 and 200 GeV– || intervals considered:

|| < 0.7 0.7 < || < 1.4 1.4 < || < 2.0 2.0 < || < 2.4

Track selection (ORCA_6_1_1):– Forward Kalman Filter used for track reconstruction– Tracks with p > 1 GeV– Tracks inside the jet within a R<0.4 cone size– At least 8 hits per track – At least 3 hits in the pixel– To reject conversions, and KS decays, Transverse IP < 2 mm

Track quality classesTrack quality classes

IP measurements depend on momentum and number of hits in the different kind of detectors

8 Quality track classes defined:Nhit 8 0.7

p < 5 p > 5

0.7 < 1.4 p < 10 p > 10

0.7 < 1.4 p < 15 p > 15

0.7 < 1.4 p < 20 p > 20

First step: resolution function calibrationsFirst step: resolution function calibrations

Resolution function dominated by Gaussian term+exponential terms (effects not taken into account in the error estimate, secondary interactions with the material and lifetime)

bb Ebb ETT=100 GeV=100 GeV

0.7

p < 5

p > 5

0.7 < 1.4

p < 10

p > 10

First step: resolution function calibrationsFirst step: resolution function calibrations

bb Ebb ETT=100 GeV=100 GeV

2.0 < 2.4

p < 20

p > 20

2.0

p < 15

p > 15

First step: resolution function calibrationsFirst step: resolution function calibrations

cc Ecc ETT=100 GeV=100 GeV

0.7

p < 5

p > 5

0.7 < 1.4

p < 10

p > 10

First step: resolution function calibrationsFirst step: resolution function calibrations

cc Ecc ETT=100 GeV=100 GeV

2.0 < 2.4

p < 20

p > 20

2.0

p < 15

p > 15

First step: resolution function calibrationsFirst step: resolution function calibrations

uds Euds ETT=100 GeV=100 GeV

0.7

p < 5

p > 5

0.7 < 1.4

p < 10

p > 10

First step: resolution function calibrationsFirst step: resolution function calibrations

uds Euds ETT=100 GeV=100 GeV

2.0 < 2.4

p < 20

p > 20

2.0

p < 15

p > 15

Likelihood ratio: distribuzione WLikelihood ratio: distribuzione Wjetjet

ORCA 6.1.1ORCA 6.1.1

Likelihood ratio: performancesLikelihood ratio: performances

Mistag = 5% Mistag = 5% bb70%,70%, bb65%65%**

Mistag = 10% Mistag = 10% bb80%,80%, bb72%72%**

Mistag = 20% Mistag = 20% bb85%,85%, bb78%78%**

* * DAQTDR DAQTDR

PreliminaryPreliminary: calibration to optimize: calibration to optimize

Likelihood ratio: performancesLikelihood ratio: performances

xx

2.4

Likelihood ratio: performancesLikelihood ratio: performances

xx

1.4

Likelihood ratio: performancesLikelihood ratio: performances

xx

2.4

Likelihood ratio: performancesLikelihood ratio: performances

xx

1.4

Likelihood ratio: performancesLikelihood ratio: performances

xx

2.4

ConclusionsConclusions

Performances seem interesting– Results are very preliminary– 3D performances still need to be studied– More statistics needed for calibration– Staged scenario need to be studied– New ORCA should be used

Rejection need to be optimized for each specific bkg under study

b tagging con i leptoni: ideab tagging con i leptoni: idea

La presenza di leptoni soft provenienti dai decadimenti semileptonici dei mesoni B può essere utilizzata per taggare i jet di b

L’efficienza del soft lepton tagging è limitata dalla frazione di decadimenti semileptonici dei B ( 17%)

Leptoni di segnale in b-jets:– Decadimenti diretti: b l– Decadimenti in cascata: b c l– Decadimenti leptonici della J/ : b J/ l– Decadimenti di adroni b in e poi in l: b l

Electroni di fondo:– Conversioni – Decadimenti Dalitz di 0 – Decadimenti semileptonici in cascate di adroni

Muoni di fondo: – Muoni da decadimenti di K e – Particelle misidentified in jet contenenti muoni reali – (muoni di basso pT ) particelle estrapolate alle camere per muoni

con depositi di energia compatibili con muoni

b tagging con i leptoni: realizzazioneb tagging con i leptoni: realizzazione

Primo approccio: per ora solo con i muoni– Ricostruire i jet, guardare alle tracce del jet, ricostruire i muoni

di L3 e fare un match traccia-muone Secondo approccio:

– Ricostruire tutti i muoni di L2– Per i muoni di L2 all’interno del cono del jet ricostruire i muoni

di L3 in questo modo si ricostruiscono solo le tracce compatibili con i muoni

Ma… PROBLEMA!!!– La ricostruzione dei muoni e l’uso della libreria bTauJetTools

sembrano incompatibili! Basta includere questa libreria perché la RecCollection dei muoni risulti vuota!

Il prossimo step: – innanzitutto occorre risolvere il problema muoni-bTauJet– Fatto questo la realizzazione dell’algoritmo dovrebbe essere

abbastanza rapida. Uno schema di algoritmo è già pronto

ConclusioniConclusioni

Ne parliamo dopo…Ne parliamo dopo…