Nuovi algoritmi di b tagging Nuovi algoritmi di b tagging Alessia Tricomi Università and INFN Catania TISB – Firenze Gennaio 2003

Alcune considerazioni sullo stato dei tools Cosa cè 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 – Cè 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 –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 |d 0 |/ (d 0 ) (take better in account experimental resolutions) dodo PV Linearised track Jet Helix PaPa

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) Confidence level that a track with IP significance S D 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

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 S D 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 S i is evaluated The ratio of the significance probability distribution functions for b and u-jets is computed: r i = f b (S i )/ f u (S i ) A jet weight is constructed from the sum of logarithms of the ratio: W= log r i 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 – | | intervals considered: | | < < | | < < | | < < | | < 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 K S 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 p < 5 p > < 1.4 p < 10 p > < 1.4 p < 15 p > < 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 E T =100 GeV 0.7 p < 5 p > < 1.4 p < 10 p > 10

First step: resolution function calibrations bb E T =100 GeV 2.0 < 2.4 p < 20 p > p < 15 p > 15

First step: resolution function calibrations cc E T =100 GeV 0.7 p < 5 p > < 1.4 p < 10 p > 10

First step: resolution function calibrations cc E T =100 GeV 2.0 < 2.4 p < 20 p > p < 15 p > 15

First step: resolution function calibrations uds E T =100 GeV 0.7 p < 5 p > < 1.4 p < 10 p > 10

First step: resolution function calibrations uds E T =100 GeV 2.0 < 2.4 p < 20 p > p < 15 p > 15

Likelihood ratio: distribuzione W jet ORCA 6.1.1

Likelihood ratio: performances Mistag = 5% b 70%, b 65% * Mistag = 10% b 80%, b 72% * Mistag = 20% b 85%, b 78% * * DAQTDR Preliminary : calibration to optimize

Likelihood ratio: performances x 2.4

Likelihood ratio: performances x 1.4

Likelihood ratio: performances x 2.4

Likelihood ratio: performances x 1.4

Likelihood ratio: performances x 2.4

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 Lefficienza 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 p T ) 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 allinterno 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 luso 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 dellalgoritmo dovrebbe essere abbastanza rapida. Uno schema di algoritmo è già pronto

Conclusioni Ne parliamo dopo…