ppt - HEP

Nuovi algoritmi di b tagging
Alessia Tricomi
Università and INFN Catania
TISB – Firenze 15-16 Gennaio 2003
Alcune 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?


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 idea

b-jet tagging made exploiting long lifetime of b:
– the algorithm relies on tracks with large impact parameter

PV
Helix
Pa

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|/s(d0) (take better in
account experimental resolutions)
– Both transverse and 3D IP have been used
– A probabilistic approach is used
Probabilistic 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)
d
s (d )
Confidence level that a track with
IP significance SD originates from
the primary vertex given by:
R(| S |) : S 


PT ( S D ) 

 R( x)dx
S
By construction the DC.L. is flat for
tracks coming from primary vertex
while is peaked at small values for
tracks coming from
displaced
vertices
Probabilistic 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 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=Slog 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 Selection

Monte Carlo samples:
– bb, cc and uu events
– Jet Transverse Energies: 50, 100 and 200 GeV
– |h| intervals considered:





|h| < 0.7
0.7 < |h| < 1.4
1.4 < |h| < 2.0
2.0 < |h| < 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 DR<0.4 cone size
– At least 8 hits per track
– At least 3 hits in the pixel
– To reject g conversions, L and KS decays, Transverse IP < 2 mm
Track quality classes

IP measurements depend on momentum and number of hits
in the different kind of detectors
8 Quality track classes defined:
Nhit  8
 h0.7
 p < 5
 p > 5
 0.7 < h 1.4
 p < 10
 p > 10
 0.7 < h 1.4
 p < 15
 p > 15
 0.7 < h 1.4
 p < 20
 p > 20
First 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 ET=100 GeV
h0.7
p<5
p>5
0.7 < h 1.4
p < 10
p > 10
First step: resolution function calibrations
bb ET=100 GeV
1.4 < h 2.0
p < 15
p > 15
2.0 < h 2.4
p < 20
p > 20
First step: resolution function calibrations
cc ET=100 GeV
h0.7
p<5
p>5
0.7 < h 1.4
p < 10
p > 10
First step: resolution function calibrations
cc ET=100 GeV
1.4 < h 2.0
p < 15
p > 15
2.0 < h 2.4
p < 20
p > 20
First step: resolution function calibrations
uds ET=100 GeV
h0.7
p<5
p>5
0.7 < h 1.4
p < 10
p > 10
First step: resolution function calibrations
uds ET=100 GeV
1.4 < h 2.0
p < 15
p > 15
2.0 < h 2.4
p < 20
p > 20
Likelihood ratio: distribuzione Wjet
ORCA 6.1.1
Likelihood ratio: performances
Preliminary: calibration to optimize
Mistag = 5%
eb70%, eb65%*
Mistag = 10%
eb80%, eb72%*
Mistag = 20%
eb85%, eb78%*
* DAQTDR
Likelihood ratio: performances
1.4 < h 2.4
ex
Likelihood ratio: performances
|h| 1.4
ex
Likelihood ratio: performances
1.4 < |h| 2.4
ex
Likelihood ratio: performances
|h| 1.4
ex
Likelihood ratio: performances
1.4 < |h| 2.4
ex
Conclusions

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: 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/y : b  J/y l
– Decadimenti di adroni b in t e poi in l: b  t l
Electroni di fondo:
– Conversioni g
– Decadimenti Dalitz di p0
– Decadimenti semileptonici in cascate di adroni
Muoni di fondo:
– Muoni da decadimenti di K e p
– 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: 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
Conclusioni
Ne parliamo dopo…