The ability to identify jets containing b-hadrons is important for the high-pT physics program of a general-purpose experiment at the LHC such as ATLAS. This is in particular useful to select very pure top samples, to search and/or study Standard Model or supersymmetric Higgs bosons which couple preferably to heavy objects or are produced in association with heavy quarks, to veto the large dominant ttbar background for several physics channels and finally to search for new physics: SUSY decay chains, heavy gauge bosons, etc. After a review of the algorithms used to identify b-jets, their anticipated performance is discussed as well as the impact of various critical ingredients such as the residual misalignments in the tracker. The prospects to measure the b-tagging performance in the first few hundreds pb-1 of data with di-jet events and ttbar events are then discussed. Finally three different use cases are discussed: the top mass measurement, the search for a low-mass Higgs boson produced in ssociation with a top quark pair and decaying to bbbar, and the specific challenges of tagging very high-pT (TeV) jets for exotics search

Goepfert, Th

The ability to identify jets containing b-hadrons is important for the high-pT physics program of a general-purpose experiment at the LHC such as ATLAS. This is in particular useful to select very pure top quark samples or for studying Standard Model or supersymmetric Higgs bosons which couple preferably to heavy objects. After a review of the algorithms used to identify b-jets, their anticipated performance is discussed as well as the impact of various critical ingredients such as the residual misalignments in the tracker. The prospects to measure the b-tagging performance in the first few hundreds of pb−1 of data with di-jet and ttbar events are presented. Finally two different physics use cases of b-tagging are summarised

Goepfert, T

CERN Document Server

ATL-PHYS-PROC-2010-01402February2010Tagging b-jets in ATLASThomas Go¨pfert, on behalf of the ATLAS collaborationTechnische Universita¨t Dresden, 01062 Dresden, GermanyThe ability to identify jets containing b-hadrons is important for the high-pT physics pro-gram of the ATLAS experiment at the LHC. This is in particular useful to select verypure top quark samples or for studying Standard Model or supersymmetric Higgs bosonswhich couple preferably to heavy objects. After a review of the algorithms used to identifyb-jets, their anticipated performance is discussed as well as the impact of various criticalingredients such as the residual misalignments in the tracker. The prospects to measurethe b-tagging performance in the first few hundreds of pb−1 of data with di-jet and tt¯events are presented. Finally two different physics use cases of b-tagging are summarised.1 b-tagging algorithms in ATLASBottom jets possess several characteristic properties that can be utilised to separate them fromjets coming from the hadronisation of lighter quarks. The most important property is the rela-tively long lifetime of b-hadrons of about 1.5 ps. This leads to a measurable flight length of a fewmillimeters before their subsequent decay. The decay of the b-hadrons at a displaced secondaryvertex can be identified inclusively by measuring the impact parameters (IP) of tracks comingfrom the decay, that is the distance from the point of closest approach of the track to the inter-action vertex. The IP is a signed quantity, which is positive if the point of closest approach liesupstream with respect to the jet direction and negative in the other case. Apart from that, asecondary vertex can also be reconstructed explicitly. The various tagging methods studied inATLAS can be divided into two main classes: the spatial taggers comprise methods that utiliselifetime information like impact parameters and decay vertices; the soft-lepton taggers are basedon the reconstruction of the lepton in case the b-hadron decays semi-leptonically. These leptonshave a sizable tranverse momentum as well as a large transverse momentum relative to the jetaxis (prelT). Detailed information on the presented results and b-tagging in ATLAS can be foundin the chapter on b-tagging in [1] and references therein.Apart from a few simple algorithms all tagging methods rely on a likelihood ratio to build adiscriminating variable, called jet weight, for the separation of b-jets, c-jets and other jets. Inthe following only the separation between b-jets and light-jets is considered for simplicity. Alljets having a jet weight above a certain cut value are then tagged as b-jets. This cut valuedetermines the b-tagging efficiency ǫb, defined as the fraction of true b-jets that are tagged asb-jets. It also determines the rejection rate of light-jets Ru, defined as the inverse of the fractionof true light-jets that are falsely tagged. For a given cut on the weight the rejection of light jetsas well as the efficiency, in general, strongly depend on η and pT of the jet (c.f. Fig. 1).A brief description of some of the ATLAS b-tagging algorithms follows. The JetProb algorithmuses the negative side of the transverse IP significance distribution as obtained from prompttracks to calculate the probability of compatibility of the tracks with the primary vertex. More1|η|0 0.5 1 1.5 2 2.50.30.350.40.450.50.550.60.650.7b-jet efficiencyATLAS (GeV)Tp50 100 150 200 250 3000.30.350.40.450.50.550.60.650.7b-jet efficiencyATLASb-jet efficiency0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Light jet rejection110210310410IP2DIP3DIP3D+SV1JetProbIP3D+JetFitterATLASFigure 1: Left: b-tagging efficiency obtained with the IP3D+SV1 algorithm operating at a fixedcut on the b-tagging weight for tt¯ events versus jet |η| and pT respectively [1]. Right: Rejectionof light jets versus b-jet efficiency for tt¯ events and for various tagging algorithms [1].sophisticated tagging algorithms utilise the distribution of the IP significance as calculatedin the transverse plane (IP2D) and in addition in the longitudinal projection (IP3D). Onesecondary-vertex tagger (SV1/2 ) fits inclusive secondary vertices and builds the jet weightfrom several one or more-dimensional variable distributions like e.g. the vertex mass. The tag-ger with the best single performance (JetFitter) fits the decay chain of b-hadrons, i.e. it fits acommon b/c-hadron flight direction along with the position of additional vertices on it. The jetweight is calculated similarly to SV1/2, but taking different decay topologies into account.Two soft-lepton tagger approaches are pursued in ATLAS. One uses soft muons and one or twodimensional reference histograms of the muons pT and the muons prelT. The other uses electronsand relies on the challenging identification of soft electrons inside jets.2 Performance in Monte Carlo simulationsThe performance of the tagging methods is estimated on Monte Carlo simulated events. Forthe following results tt¯ events were used. A snapshot of the expected light jet rejection as afunction of the efficiency can be found in Fig. 1 for the spatial taggers. One can expect Ru ∼ 30for the JetProb tagger and up to Ru ∼ 200 for the sophisticated JetFitter algorithm at a typicalb-tagging efficiency of ǫb = 60%. The soft muon tagger for example gives Ru ∼ 300 at ǫb = 10%,where ǫb includes semi-leptonic branching fractions.An effort has been made to reach a realistic understanding of critical aspects of b-tagging. Thestudies summarised above were done assuming a perfect knowledge of all misalignments. Morerealistic studies take residual misalignments into account as well as the process of realignmentincluding systematic uncertainties. Recent studies indicate a possible degradation of the lightjet rejection of up to ∼ 30% at most for fixed ǫb. Further degradations in rejection are seenin studies including pile-up events (∼ 5 minimum bias events are expected at an instantaneousluminosity of 2 · 1033 cm−2s−1), where in a few percent of the cases a wrong primary vertexis reconstructed leading, among other things, to an artificial shift in the longitudinal IP andfinally to a loss in rejection of ∼ 30 − 40% for IP3D and IP3D+SV1. The taggers that takeonly the transverse impact parameter into account are minimally affected by pile-up however.23 Prospects for performance measurementsIt will be necessary to calibrate the b-tagging methods on data. This means that one has tomeasure the tagging efficiency as well as the rejection rate. Due to the dependence on pT andη it is desirable to perform the calibration in bins of those variables. In addition one wouldalso like to measure the reference histograms with data. For the efficiency measurement severalmethods have been studied in ATLAS that make use of either di-jet or tt¯ events.The prelTmethod is one approach that uses events with jets that include non-isolated muons.Templates of the muon prelTas obtained from simulated and reconstructed b-, c- and light-jetspassing basic selection criteria are fitted to the measured distribution before and after applyingthe respective tagger, which preferably is a spatial tagger. By counting the number of muonjets before and after the tagging, the efficiency ǫb can be estimated. It was shown that anintegrated luminosity of ∼ 50 pb−1 is sufficient to derive detailed calibration curves in pT or ηwith a relative precision on ǫb of about 6%.There are several methods that make use of tt¯ events. One is an event counting method,that measures the average b-tagging efficiency and the cross section of tt¯ production in thelepton+jets or the dilepton channel at the same time, by counting the number of events with 1, 2or 3 tagged jets. With ∼ 100 pb−1 of data a relative precision on ǫb of ∼ (2.7(stat.)±3.4(sys.))%can be reached in the lepton+jets channel.4 Physics use casesThere are various examples of use cases where b-tagging is a critical ingredient. One is thesearch for a Standard Model Higgs boson in the tt¯H(H → bb¯) channel. Here b-tagging can beused to reduce or even eliminate large backgrounds like tt¯jj¯ or W+jets. For example the tt¯jj¯background is reduced by two orders of magnitude by using b-tagging. Another example is thetop quark mass measurement. There the highest precision can be reached in the tt¯ lepton+jetschannel by requiring two b-tagged jets using the hadronically decaying top as the mass estimator.Assuming a jet energy scale uncertainty of the order of one percent, a precision of 1GeV canbe reached with an integrated luminosity of 1 fb−1. A complementary approach relying on b-tagging infers the top quark mass from the mean transverse decay length of b-hadrons comingfrom the top decays. Here the uncertainty due to the jet energy scale is negligible.5 ConclusionVarious algorithms for tagging b-jets have been studied in detail in ATLAS. The spectrum coverssimple, robust taggers as well as sophisticated taggers, that make use of as much information aspossible from the b-hadron decay chain. Several approaches for calibrating b-tagging algorithmswith data were shown to be realisable with a few 100 pb−1 of data. b-tagging is essential formany physics analyses, like Higgs boson searches or top quark mass measurements.References[1] The ATLAS Collaboration, Expected Performance of the ATLAS Experiment, Detector, Trigger andPhysics, CERN-OPEN-2008-020, Geneva, 2008.3

Tagging b-jets in ATLAS

ATL-PHYS-SLIDE-2009-22726August2009  Strategies  to measure  the zzzTagging b­jets in ATLASThomas Goepfert (Technische Universitaet Dresden) on behalf of the ATLAS collaborationTagging algorithmsTagging algorith sTrackCounting:  simple  cut  on  the  ordered  IP significance of the n­th track (n=2, 3) in a jetJetProb:  calculates  the  probability  that  a  track originates from the primary vertex by comparing the  IP  significance  to  a  resolution  function  R prompt tracksIPxD  (x=1,2,3):  Likelihood  Ratio  (LR)  approach using  the  IP  significance distribution S of  tracks stemming  from  b  and  light  (udsg)  jets  in  x  dim xxxxxxxxPerformance  gain  by  grouping  the  tracks  into categories (e.g. pT) with dedicated pdf'sImpact parameter (IP) basedI pact para eter (IP) basedExploiting B­hadron properties:● relatively high mass (~5GeV)● charged track multiplicity (~5)● long lifetime (cτ ~ 450 μm) results in displaced decay vertexExploiting B­hadron properties:● relatively high mass (~5GeV)● charged track multiplicity (~5)● long lifetime (cτ ~ 450 μm) results in displaced decay vertexInclusive SV: reconstruction of two­track vertices inside  a  jet,  rejecting  V0  and  conversions  and fitting  to  a  common  SV,  cut  on  decay  length significance  or  LR  approach  based  on  one  or more  dimensional  distributions  of  vertex  mass, energy fraction and number of two­track verticesJetFitter:  uses  Kalman  filter  to  find  a  common line of PV and B/D­hadron decay products which also  includes  incomplete  topologies,  then  using dddCombination of jet weights from IP and SV based taggers  or  multivariate  techniques  (BDT)  for better performanceSecondary vertex (SV) basedSecondary vertex (SV) basedSoft  muon:  jet  weight from  1  dim  LR  method using  the  muon  relative transv.  momentum  pTrel wrt the jet axisSoft electron: LR  approach  based  on several variables from the inner  detector,  the calorimeter  and  both  in combinationSoft lepton taggersSoft lepton taggersThe performance of b­tagging algorithms is described by the tagging  efficiency  and  the rejection rate R of light/charm and  tau  jets,  which  is  the inverse  of  the  mis­tag  rate. Those quantities are obtained from  MC  samples  by  cutting on the discriminating variable of  the  respective  tagging algorithm, e.g. the jet weight.The expected rejection rate at 60% b­tag efficiency is ~30 for the simplest taggers (JetProb  or  TrackCounting)  which  are suitable  for start­up and   about 200  for high­end taggers like JetFitter.● Soft muon: R~300 at 10% (including BR of b → μ X)● Soft electron: R~100 at 7%● High level trigger (IP3D): R~10 at 70%At  a  given  b­tag  efficiency the  performance  of  the algorithms  depends  on  the jet  transverse  momentum and  rapidity  and  thus  is physics process dependent. Further rejection ratesFurther rejection ratesTransverse IP resolution ~35 μm for a central track with pT=5GeVSome critical ingredientsSo e critical ingredientsPrimary  Vertex  reconstruction:  At  LHC  the  beamspot  size  is σxy=15 μm and σz=5.6 cm. With and without pile­up  (~5 min bias events  at  L=2x1033  cm­2s­1)  the  PV  resolution  is  about  the  same (σxy=12  μm,  σz=40  μm)  but  up  to  5%  are  misidentified  in  the presence  of  additional  vertices.  This  leads  to  artificially  big longitudinal IP and to a degradation of the performance of ~30% for  IP3D  and  ~40%  for  IP3D+SV1  whereas  b­tagging  algorithms using only transverse IP are nearly not effected. Residual misalignment: The impact on b­tag  performance  is  studied with  different misalignment sets with shifted (10­30 μm) and  rotated  (~0.3  mrad)  Pixel  modules and with a  more   realistic  case (similar to the  misalignment  we  see  after  aligning with  cosmics)  of  actual  realignment systematic errors leading to a degradation of about 30%.Ptrack =Z ¡jIP=¾(IP )j¡1R(x)dxPerformancePerfor anceCalibrationCalibrationtagging and mis­tagging efficiency directly in data. Two main approaches to measure b­tag  efficiency  at  ATLAS  (results  for 14 TeV MC). tt eventstt eventsEvent  counting:  count  number  of  events with 1,2,3 b­tags (lepton+jets) or 1,2 b­tags (dilepton  channel),  likelihood  fit  gives  b­tag  efficiency  and  tt  cross  section  at  the same time as well as the c­tag efficiency in the lepton+jets channelWith 100 pb  ­1 the relative precision at 60% b­tag efficiency is ±2.7(stat.)±3.4(syst.)% in the  lepton+jets  channel  (slightly  worse  in the dilepton channel). Event reconstruction: obtaining a pure sample of identified b­jets by full reconstruction of a semileptonic tt decay chain, studying  the  output  distribution  of  the  relevant  tagging algorithms. Three similar techniques have been explored:For  the  topological  method, 200 pb  ­1  of  data  are  needed and allow a relative precision of ±2.7(stat.)±3.4(syst.)%.Topological selection with top mass fitLikelihood selectionKinematic fitdijet eventsdijet events pTrel  method:  uses  jets  with  an associated  muon,  fitting templates  of  muon  pT relative to  the  jet+muon  axis  for  b/c­ and light jets before and after b­tagging System  8  method:  uses two samples of jets with different  b­quark content  and  two uncorrelated  tagging algorithms  (e.g.  soft muon  and  spacial tagger) to form a system of  8  equations  and  8 unknowns, one of which is  the  b­tagging efficiencyBoth methods give results binned in pT and η. With 50 pb  ­1 a relative precision of about 6% can be reached.²b =N tag¹¡jetFtagbN¹¡jetFbUse cases for b­taggingse cases for b­taggingTop mass measurementTop  ass  easure entSearch for ttH (H→bb) in the SMSearch for tt  ( bb) in the S High pT b­tagging for BSM/Exoticigh pT b­tagging for BS /ExoticThe  best  top  quark  mass determination  is  achieved with    two  b­tagged  semi­leptonic  tt  events  and  a  top mass  estimator  taken  as  the aaaaaainvari nt  mass  of  the  three  jets  from  hadronically decaying  top  quark.  The  uncertainty  on  the  top  quark mass will be dominated by systematics and relies mainly on  the  jet  energy  scale  uncertainty.  A  precision  of 1(3.5) GeV  is  reachable  with  1 fb    ­1,  assuming  a  jet energy scale uncertainty of 1(5)%.LR  approach  similar  to inclusive  SV  but  taking different  decay  topologies into accountA  complementary  approach  relying  on b­tagging  infers the top quark mass from the mean transv. decay  length of  B­hadrons  coming  from  the  top  decays,  here  the uncertainty due to the jet energy scale is negligible.B­tagging  helps  to  reduce or  even  to  eliminate  large backgrounds  like  tt+jets, W+jets,  tW.  The  b­tagging weight  is used as a cut and as  input  variable  to  a likelihood  selection  for  jet assignment  and  signal  to background separation.Jets  with  pT above  500 GeV  appear  in  many  BSM models,  e.g.  Z'→Zh  (h→bb). They are very challenging for tracking and b­tagging.Performance  is  worse  for such  jets  due  to  several effects.  The  fraction  of fragmentation  tracks increases with parton pT,   xxxxxxxxwhile the jet is collimated into a narrower cone. The high track density in such high pT jets challenges the patter­recognition ability of the tracking software.XXIV International Symposium on Lepton Photon Interactions at High Energies jet weight =NtracksXi=1lnb(Si)u(Si)for  prompt  tracks (obtained  from tracks  with  IP<0) xxxxxxto  calculate  a  jet weightincluding systematic errors lea ing to a degradation of about 30%.

Tagging b-jets in ATLAS

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