BID Status Report part I

1. BID Status Report part I S₫bastien Greder, Lorenzo Feligioni On behalf of bid group Taggers Description -CSIP, JLIP, SVX, SLT - definition positive/negative tag Taggability V0 removal Mistag Rate - neg.tag rate in Data and Monte-Carlo corrections B-tagging efficiency - Methods - B-tagging efficiency in Data - B-tagging efficiency in MC - Data/MC Scale factor Available Tools

2. Tagger Description : Introduction - The b quark has a long lifetime : ~1.5 ps - Can fly over 1-2 mm. Lifetime based taggers : CSIP, JLIP, SVX based either on tracks impact parameter or vertex decay length -10% b decay produce a muon : Muon based tagger : SLT m

3. Counting Signed Impact Parameter : CSIP (2) L. Chabalina, R.Demina, A. Khanov, F. Rizatdinova Based on impact parameter significance S(IP) : (sometimes named distance of closest approach (dca) significance) S(IP) = IP/s(IP) Note : IP is a signed quantity w.r.t to jet axis (so is S(IP)) : - positive if q < p/2 - negative if q > p/2 Jet axis Track q I.P Primary vertex

4. Counting Signed Impact Parameter : CSIP (2) Requirements to tag a jet positively : - at least 2 tracks with S(IP) > 3 - or at least 3 tracks with S(IP) > 2 Requirements to tag a jet negatively : - at least 2 tracks with S(IP) < -3 - or at least 3 tracks with S(IP) < -2

5. Jet Lifetime Impact Parameter : JLIP (1) D. Bloch, B. Clement, D. Gele, S.Greder, I. Ripp-Baudot Fit a resolution function (R) on the negative S(IP) distribution. Assume these tracks to originate from primary vertex. Use then R to define a probability for a track to originate from the primary vertex :

6. Jet Lifetime Impact Parameter : JLIP (2) Combine then each track's probability to compute the probability for N tracks to originate from primary vertex : Pjet the jet lifetime probability (tracks are required to be in dR = 0.5 cone around jet axis) Pjet(+-) = p(+-) 5 ₢j=1Ntrk (-logp)(j-1)/(j-1)! With : p(+-) =Õj=1Ntrk Ptrk(sig<0>0) Requirements to tag a jet positively : - Pjet (+) < cut Requirements to tag a jet negatively : - Pjet (-) < cut

7. Secondary vertex tagger : SVX (1) L. Feligioni, M. Narain, A. Schwartzman, P. Schieferdecker -Build up track-based jets and fit their tracks to a secondary vertex -Select tracks with high IP to build secondary vertices -Tracks with high IP will form vertices with high decay length significance : S(Lxy) = Lxy/s(Lxy). (Like S(IP), S(Lxy) is a signed quantity) Track-based jet Z axis

8. Secondary vertex tagger : SVX (2) Jet are then tagged by requiring a dR < 0.5 matching between the jet axis and the secondary vertex. Requirements to tag a jet positively : - match a secondary vertex with S(Lxy) > cut Requirements to tag a jet negatively : - match a secondary vertex with S(Lxy) < -cut

9. Soft lepton tagging : SLT K. Hanagaki, J. Kasper, J. Butler - Defined "a la RunI" : require a muon to be matched within dR < 0.5 to the jet axis no Ptrel cut is required. At this step try to mesure muon reconstruction and track-match efficiencies by looking into J/Psi-> mumu signal in CSG sample. Muon are required to have Pt > 4GeV, |h| < 2.

10. Taggability A jet is required to be taggable before tagging ; it ensures a "minimum" quality requirement before tagging procedure Definition : - build-up track-based jets, dR = 0.5, track Pt > 0.5 GeV - require tracks to have at least 1 smt hit (ladder+Fdisk) - at least 1 track with Pt >1GeV - track dca(x-y) < 0.2 cm - track dca(z) < 0.4 cm A calorimeter jet is taggable if if it matched to a track-jet within a dR< 0.5 cone. Efficiencies, mistags in data and Monte-Carlo are calculated w.r.t taggable jets.

11. V0 Removal procedure Decays in flight like Kos , L, g conversions do contribute into signal for impact parameter based taggers V0 removal algorithm is included into CSIP package It makes use of all the tracks in the event and flag them No-V0 flagged tracks are then filtered out to feed the taggers

12. V0 Removal procedure Fractions of V0 candidates without V0 removal in jet trigger data normalized to taggable jet (left) and tagged jets (right) All Kos g conversions L

13. Mistags : Definition and origin in MC Mistag is defined as the light quark tagging efficiency The origin of tracks in light jets has been scrutinized (Sasha Khanov) in W+light jets events The main contributions come from tracks originating from the primary vertex and fakes (defined as reconstructed track not matched with a mc track within 10s of all tracks' parameters) Negative tags Positive tags

14. Mistags : how to get them in data ? Heavy flavour is most likely to be positively tagged : forget about using positive tags ! On the other hand light flavour has a nearly symmetric distribution That's nice, take negative tag rate in data as mistags ! BUT : unfortunately reality is far from being as simple as Monte-Carlo because if(jet->GetFlavour() == "light") doesn't work in data ! WHY: - Heavy flavour contaminates also negative tags. - Long lived particles contributes in positive tags and are thus missing in negative tags

15. Mistags : corrections factors Need to correct data negative tag rates to get the estimated light tagging efficiency in data : Scaling factor to correct for the heavy flavour contamination in negative tags Missing contribution from long lived particles Both scaling factors are evaluated in Monte-Carlo

16. Mistags : examples plots SVX SFhf SFll JLIP

17. Mistags : estimated light tagging efficiency Shown here for CSIP as predicted from EMQcd sample for 4 working points This prediction parametrizations have to be used to reweight Monte-Carlo light jets