LHCb xxxxLPHE yyyyversion 1.2July 13, 2009Optimization of b jet energy measurement inLHCb

Aurelio Bay1, Cedri Potterat2Laboratory for High-Energy Physi s, Swiss Federal Institute of Te hnology, LausanneAbstra tWe present studies for the optimization of b jet energy measurement in LHCb. Animplementation is proposed, based on a neural-network.

1E-mail:Aurelio.Bay�epfl. h2E-mail:Cedri .Potterat�epfl. h

1 Introdu tion. Jet algorithmsJets of parti les are produ ed by the hadronization of hard quarks and gluons. Similartopologies are also found in the hadroni de ays of fast τ and other heavy parti les.Intermediate mass Higgs bosons de ay into two b quarks whi h hadronize into b jets.In order to pre isely re onstru t the Higgs invariant mass, the two b jets have to bemeasured with the best four-momentum resolution. The present study aims at obtainingthe best b jets energy resolution. The absolute alibration of the jets will be determinedexperimentally from hadroni Z de ays. This point is not onsidered here.Several algorithms are available for the identi� ation of jets and for the determinationof the asso iated four-momenta. We onsider here only the � one algorithm�, whi h onsistin the integration of the energy deposited inside a one of given radius Rmax de�ned in theη − φ spa e. The axis of the one (the �seed�) is given by some a priori knowledge of thejet dire tion. In the ase of b jets this dire tion an be obtained from the position of these ondary verti es found in the de ay hain of the b-hadron. The b jet �nder onsideredin this study is very lose to the one des ribed in [2℄, whi h uses se ondary verti es to tagthe b quark, and to determine the seed for the one algorithm (ou present implementationwill be dis ussed in a separate note).Jet energy measurement in LHCb have already been dis ussed in [1, 2, 3, 4℄. Anexample of algorithm performan es study in terms of di-jet invariant mass resolution isshown in �gure 1, where the two b jets are produ ed by the de ay of an Higgs boson,with a mass of 120 GeV/ 2. The rise of the urve for Rmax larger than 0.5 is due tothe in reasing pollution from the �underlying event� (UE) parti les. In the following, thedefault value will be Rmax = 0.5, lose to the optimal resolution for Higgs events.The energy �ow in the one delimited by Rmax is obtained from the tra king and alorimetri data, whi h su�er of several problems, spe i� to LHCb: in parti ular theLHCb dete tor a eptan e is limited to a forward window of about [15, 300℄ mrad, andthe alorimetri spe tros opi hannels saturate for a transverse energy deposited above∼10 GeV/ .In our implementation the jet one algorithm olle ts the information from all thesub-dete tors (ex ept RICHes) to ompute the energy. This pro edure requires some adho weighting of ea h ontribution and a global alibration, as explained at the end ofthis se tion. The energy deposited in ea h alorimeter ell is used to onstru t a pseudo-parti le (zero mass) four-momentum pointing from the primary vertex to the enter ofthe ell, and size orresponding to the energy deposited. The pseudo-parti le is dis ardedif falling outside the Rmax window. A �raw� jet four-momentum, praw (energy Eraw), is omputed from the (unweighted) ECAL, HCAL, and muons four-momenta in the one.In the present study we have used events generated by Pythia, fully simulated (onlythe study for an UE orre tion presented in se tion 6 was limited at the generator level).Besides Higgs events, we have also used events produ ed by a �jet-gun�: a quark of hosen �avor is pla ed in parti le list of the Pythia generator setup for independentfragmentation. We have produ ed several thousands of su h events, mainly with b quarks,energies 0.2 < Eb < 3 TeV, the gun spraying the whole dete tor a eptan e.Some plots obtained with the b jet-gun are shown in �gure 2. They present the energies

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Figure 1: The FWHM (normalized to the entral value) of the di-jetinvariant mass distributions versus the resolution parameter Rmax. Thestars represent the resolution when the two jets are omputed using thepartoni shower from the Higgs de ay. At the other extreme, the opentriangles show the resolution when stable parti les are onsidered, neu-trinos dis arded, and the �underlying event� (UE) in luded. Three othersituations are also presented: pure Higgs event, no UE, with neutrinosdete ted; Higgs plus UE, also with neutrinos dete ted; pure Higgs, noUE, neutrinos undete ted (data from [4℄) olle ted in the Rmax =0.5 one by the tra ker, ECAL and HCAL, and Eraw. >From thes atter plots we see that saturation a�e ts the energy resolution starting from Eb ∼1 TeV.Moreover the momenta from the magneti spe trometer are also quite dispersed beyond0.5�1 TeV. We an already guess that orre tions η and energy dependent are needed inthe pro edure, justi�ed by the observation that the probability of saturation grows withEb and de reases with η.What are the riteria for the algorithm optimization ? The �nal goal is to trans-form the raw dete tor information into a quantity suitable for physi s. In this study wehave onsidered three di�erent points of view of this problem. In the most onservativeapproa h the algorithm aims at re onstru ting pre isely the �visible� energy �ow in the one delimited by Rmax: in pra ti e the energy of referen e is inferred from the parti lefour-momenta known from Monte Carlo (MC) truth, the neutrinos are dis arded and alsoall parti les with traje tories outside the dete tor a eptan e. In a se ond, more involvedapproa h, we drop the a eptan e ut: then the algorithm is designed to orre t also forthe energy lost outside the dete tor. In other words, we have the hoi e to setup the

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Figure 2: Left 4 s atter plots: energy in the one measuredby the tra ker, ECAL, HCAL, and also the �raw� value Eraw=ECAL+HCAL+muons, vs the energy Eb of the quark. Right: Eraw vsEb, for 3 pseudorapidity ranges: low: η < 2.4, medium: 2.4 < η < 2.8,and high: η > 2.8pro edure to reprodu e as pre isely as possible the visible energy only, or to obtain thebest approximation of the total energy �ow in a one whi h an partially lie outside thea eptan e. This se ond option is our default. Be ause of the parti ular jet �nder usedhere, for seeds falling very lose to the dete tor limits, the energy outside the a eptan eis never more than ∼40% of the total.Finally, a third more radi al approa h is to ask the algorithm to guess the partoni energy. In the ase of H → bb the goal being to re onstru t the two b-quark four-momenta, in addition to dete tor e�e ts the algorithm is required to orre t for possiblehard gluon emission, neutrinos, the presen e of parti les from the underlying event, et ..2 The neural-networkGiven the omplexity of the task, we have hosen a neural-network te hnique to explorethe di�erent possibilities (for omparison, we have also tested simpler algorithms, see nextse tion).The aim is to setup a general pro edure to be alled from the analysis ode (DaVin i),individually for ea h jet. The variables are olle ted from long tra ks and alorimetry asalready explained. The following 17 variables are omputed from the set of four-momenta onstru ted from tra king, in luding muons, and from the hannels hits in the PRS,SPD, ECAL, and HCAL subdete tors (the names given in the following are the names ofvariables used in the ode):

• 6 energies from tra king and alorimetry: ETRK, EPRS, ESPD, ECAL, HCAL,EMUS4

• 6 masses : MTRK, MPRS, MSPD, MCAL, MCAL, MMUS. The goal of these�invariant masses� is an attempt to parameterize the energy spread inside the one• the pseudorapidity of praw: ETA• 2 numbers to de�ne the granularity of the region hit in the alorimeters: by extrap-olating praw to the ECAL and HCAL surfa es, we set EPOS = 0, 0.5, and 1, forinner, middle, and outer ECAL, and HPOS = 0, 1 for inner, outer HCAL• 2 numbers to measure of the energy in the saturated alorimeter ells: for ea h ellin the one found at saturation, the saturation value in rements a parameter ESATfor ECAL, and HSAT for HCAL.An example of these variables is shown in �gure 3 (from b jet andidates found in120 GeV/ 2 Higgs events). To better feed the NN, the variables are regularized in theapproximate interval [0.,1.℄, see �gure 4. The same treatment is reserved to the variableto be guessed, Emc, also shown in the two �gures.

Figure 3: Variables onsidered for feeding the neural-network, the �rstplot labeled with E is the energy onsidered for training, i.e. the valueEmc wished at the NN outputThe NN was setup with 17 inputs, two hidden layers of 18 and 12 neurons, 1 output.Train and tests are done on sets of the order of 10k events.We �rst studied the NN on the b jet events from the jet-gun, with an energy up to 3TeV. In this ase no underlying event is present, nor hard gluon radiation. In �gure 5 theplot R is the distribution of R = (Emc −Enn)/Emc. This distribution must be entered onzero, the rms providing the average energy resolution. Emc was hosen following the three

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Figure 4: Like �gure 3, after �regularization�, to better �t the preferredNN input rangeoptions dis ussed above, in in reasing order of di� ulty: 1) Emc is the visible energy inthe a eptan e, 2) idem but gathered without a eptan e uts (our default), 3) Emc =Eb, the b quark energy. As an be expe ted, we observe a degradation of the resolutionfor the more demanding setup: the rms values are 0.2276, 0.2281, and 0.2476, for the 3options. The bottom right s atter plot shows the pro�le from the s atter plot, Emc versusEnn. The 45o slope shows that the NN is behaving well, ea h sli e in Enn giving the orre tEmc on average.On the other hand, from the Enn vs Emc (or R vs Emc) plots we see that the responseof the dete tor deteriorates after ∼1.5 TeV. The e�e t is mainly due to alorimetri saturation as an be seen from the large dispersion at low η visible in the R vs η s atterplot. Considering the default option (no. 2 above) we have a rms of ∼0.30 in the regionη < 2.4 and ∼0.20 at large η. We did an attempt to split the sample in two regions of lowand high η, and pro ess them independently: no improvement was observed ompared tothe previous results, whi h indi ates that the NN handles this dependen y in a orre tway.Another study was done as a fun tion of the input energy range. The NN trained withb jets up to 3 TeV overestimates the energy in the lower 2/3 of the range: we �nd thatthe average of the R distribution is −0.12 for the 0-2 TeV region. To try to adjust theresponse under 2 TeV (this is the region of interest for Higgs sear hes), the NN trainingwas limited to Eb < 2 TeV, see �gure 6. In that ase th R distribution is well entered,the width is 0.19, but the response is somehow distorted after 1.8 TeV. In general weobserve that to obtain a more uniform energy response, it is better to in lude in the train

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Figure 5: Performan e plots for the NN. Ea h group of four gives thedistribution of R = (Emc −Enn)/Emc, and the s atter plot with (in red)the pro�le of Enn vs Emc, R vs Emc, and R vs η. The three di�erentgroups orrespond to Emc hosen as the visible energy, the visible energybut without a eptan e uts, and the b quark energy used to generatethe events respe tively. The bottom right s atter plot is Emc vs Enn,showing a pro�le at 45 degrees.

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set events with energies ∼10% higher than the wished range.

Figure 6: Performan e of a NN trained with energies up to 2 TeVOther variables have been studied, mainly to try to re over from the saturation e�e t:• the multipli ity of tra ks, and the number of hits in the alorimeter inside the one• the energy deposited around the jet one: inside a region from Rmax to Rmax + 0.3or Rmax + 0.5No useful orrelation with the true energy was found by inspe tion of s atter plots. In-serted into an extended NN these variables were found ine�e tive, and were dis arded.3 Other non NN pro eduresFor omparison we have tested other simpler algorithms. The method is shown in �gure 7(see left group of 4 plots). It onsists of �tting the pro�le of Emc vs Eraw by a polynomial,subsequently used to orre t Eraw. Also shown in the �gure (four right plots) the resultfrom a similar pro edure but assuming Eraw =ETRK+ECAL+0.7*HCAL (as suggestedin [2℄). In both ases the resolution is worse that with the NNN.4 Neural-networking the Higgs eventsThe pro edure has been tested in the ontext of an analysis for the sear h of Higgs inasso iated produ tion with Z or W. Several thousands of fully simulated events whereprodu ed with Higgs mass of 90, 110, 120, 130, and 140 GeV/c2. The event sele tionrequires a prompt and isolated lepton from W or Z, and two b jets. As previously said,the b jet �nder exploits se ondary verti es to tag the b quark and to determine the one seed. The events are reje ted if less than two seeds are found. For ea h seed, praw

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Figure 7: Four plots at the left: simple energy orre tion starting fromthe raw energy Eraw. The pro�le of Emc vs Eraw (top left �gure) is �ttedwith a polynomial of degree 7 (blue line), used to orre t Eraw (top right).R parameter and orre ted Eraw vs Emc are shown at the bottom. Fourplots at the right: idem, but Eraw =ETRK+ECAL+0.7*HCALis omputed with Rmax =0.5, and we hose the two jets whi h give the largest di-jetinvariant mass, but with an upper limit of 250 GeV/c2. The events are dis arded if Erawof one of the two jets is less than 100 GeV. After these uts the jets of the surviving eventswere divided in two sets of 10-20k jets ea h, one set was used for training, and the otherfor testing the NN. The NN was setup as seen before, but with a larger number of hiddennodes (17 input nodes, two hidden layers of 30 nodes, and 1 output).The four left plots of �gure 8 result from the NN trained to guess the visible energy,while for the right plots we used Eb de�ned as the b quark energy before parton shower.We have hosen to train on a mixture of 90 and 120 GeV/c2 events: this makes the NNresponse somehow more linear. As seen with the b jet-gun events, the NN has a mu heasier task when Eraw is used, giving a single jet rms of 0.22, and 0.25 when Eb is used.The same �gures shows that the NN response is linear with Emc. We also observe adistortion for Emc <200 GeV. The minimal Enn value is around 100-150 GeV and does notextrapolate to zero: the NN is using an Emc average value when Eraw is too small, and noadditional information is available to guess the right Emc. This an also be seen from theEmc vs Eraw s atter plot of �gure 9: the pro�le has also an inter ept at around Emc =200GeV. This threshold is responsible for the urvature in the R vs Emc s atter plot at smallEmc. Finally, no large distortions are present at the a eptan e limits of the dete tor, as an be dedu ed from the �at pseudorapidity plot: it is remakable that this is not the asewhen the number of train y les, or �histories�, is too low.The number of train histories was hosen to minimize the rms of the R distribution.An example of su h optimization pro edure is shown in �gure 10. We an see that theoptimum is around 400�500 histories, for a train set of about 10'000 jets.The four-momenta of the two jets are subsequently ombined to obtain the Higgs

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Figure 8: The group at the left are results for the NN trained as-suming the visible energy as training parameter, at the right assum-ing the b quark energy Eb. For ea h group, the top left plot: R =(Emc − Enn)/Emc, top right: E vs Enn, bottom left: R vs Enn, bottomright: R vs η. The red dots are the s atter plot pro�les

Figure 9: Emc vs Eraw for jets from Higgs with a mass of 120 GeV/c2

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M Eraw FWHM Enn FWHM Enn (Eb) FWHM90 61.5 36.5 84.3 34.2 99.7 29.2110 75.2 38.2 99.1 36.5 113.9 36.5120 82.0 44.5 107.1 41.0 120.8 42.2130 91.1 45.6 115.1 44.4 126.5 39.9140 97.3 50.0 121.9 47.9 133.9 45.6Table 1: Di-jet masses obtained for 5 generated mass values indi atedin the �rst olumn. Columns 2 and 3 are the results from Eraw, whilein olumn 4 and 5 we have used NN orre ted jet energies. The lasttwo olumns orrespond to the NN trained to guess the b quark energy.Units are GeV/c2mass estimate. The plots of �gure 11 are the mass distributions, obtained from the twopraw four-momenta, and after NN. A Gaussian �t is shown superimposed. The energy ofthe peak is shifted by the NN a tion from 82 to 107 GeV/c2, loser to the 120 GeV/c2generated. We ompute graphi ally the performan e by taking the FWHM of the peak.De�ning the relative resolution r = FWHM/Mjj, where Mjj is the enter of the peak, wemeasure an improvement from r=44.5/82=54% to r=41/107=38%, before and after NNrespe tively.The behavior of our setup is summarized in table 1 and �gure 12, for Higgs masses of90, 110, 120, 140 GeV/c2. The bottom series of points in the �gure is the raw response,from the praw four-momenta. The two other series are the results after NN, assumingvisible energy (points in the middle), and Eb (at the top). One an noti e that theresponse of the NN trained to re onstru t Eb, gives a mass value loser to the diagonal,but the response has slope less than 1.As a �nal test, we have redone the pro edure for di�erent values of Rmax. The plot of�gure 13 shows that after NN is applied the di-jet mass resolution is only slowly dependenton Rmax, and that the value of 0.5 is optimal.5 Dis ussionAs seen before, the relative resolution FWHM/Mjj for a 120 GeV/c2 Higgs is 38% afterNN, to be ompared with 54% for raw energies.In the ontext of Higgs analysis the irredu ible ba kground omes from ZZ and ZWevents: these events have the same topology as Higgs in asso iated produ tion. Theseparation of a 90 GeV/c2 parti le (∼Z) from a 120 GeV/c2 one an be inferred from theresults of table 1:

s = (M120 − M90)/√

σ2120 + σ2

90where Mx is the re onstru ted mass for a generated mass x, and σx = FWHMx/2.36.We have obtained an in rease from s = 0.84 for raw di-jet masses, to s = 1.01 after NNpro essing (0.97 fo NN trained to �nd Eb). The result is less good as one ould hope from11

Figure 10: Single jet resolution (rms of the R distribution) vs numberof train histories

Figure 11: Left: di-jet invariant mass, for a generated Higgs mass of120 GeV/c2. The top plot is the raw result, the bottom after NN. Theresult of a Gaussian �t is also shown. Right: idem, for three generatedenergies of 90, 120, and 140 GeV/c2, before (top) and after (bottom)the NN pro edure12

Figure 12: Re onstru ted Higgs di-jet invariant mass as a fun tion ofthe generated mass value. The dots shows the values obtained from thetwo praw four-momenta. The squares and triangles are obtained afterNN orre tion, with th NN trained to guess the visible energy and Eb,respe tively. The points and error bars represents the enter of the peakand σ = FWHM/2.36

Figure 13: Di-jet mass relative resolution for 6 values of Rmax. Thegenerated Higgs mass is 120 GeV/c2

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the �lo al� improvement of the resolution at 120 GeV/c2: the problem an be understoodfrom �gure 12 where we noti e that the NN orre ted (red) point for 90 GeV/c2 mass hasbeen pushed loser to the diagonal than the other four points.6 A orre tion for the underlying event ?The UE is generated by the hadronization of the spe tator partons. In addition, in the ase of multiple beam intera tions the produ t of other p�p ollisions an overlap, givingrise to a se ond ontribution. This se ond ontribution is not addressed here and deservesa separate treatment. The e�e t of UE has already been shown in �gure 1. The rise afterRmax ≈ 0.5 is learly seen and the omparison with the resolution obtained from pureHiggs de ay parti les, shows that the degradation is due to the olle tion of parti les fromthe UE. An attempt to orre t for this e�e t was presented in [4℄.In the present study, we have addressed the problem at a pure four-ve tor level. Thispoint is ru ial to disso iate from dete tor e�e ts, whi h an mask orrelations or reatearti� ial ones. The MC events onsidered are Higgs (M=120 GeV/ 2) produ ed in asso- iation with a Z or W. The bosons are tagged by an isolated lepton with a Pt>10 GeV/ .The dete tor is simulated only by its a eptan e, whi h is hosen to be from 15 to 300mrad.The plots of �gure 14 give the di-jet invariant mass for 4 values of Rmax (the one seedis now given by the dire tion of the hadrons arrying a b quark). The bla k histogramsare al ulated using parti les oming from the Higgs de ay only, while the red histogramsin ludes all the visible parti les (in a eptan e, the neutrinos have been dis arded).Again, we an see that the UE ontributes signi� antly only for Rmax >0.5. One analso see from the bla k urves, that there is a potential improvement in mass resolutionfor Rmax =0.8 ompared to Rmax =0.5. On the other hand, it is lear that we an takeadvantage of this only if we an �nd a way to orre t for the UE.A orre tion to the jet energy an be written as Ecor = Econe − δ, where Econe is thejet energy estimated by the one algorithm (we have hosen Rmax =0.8), and δ is the ontribution from UE parti les in the same one area. Our aim is to estimate δ valuefor ea h event: a set of variables has been investigated to see if they arry the wishedinformation. Again we have used a NN to do the tests.The initial jet four-momentum pj is al ulated with Rmax =0.8, on all visible parti les,and the energy of pj is indi ated by E (note that we have two b jets ( p1

j and p2j ) in theHiggs de ay: the pro edure is done independently for the two jets). Seven variables are al ulated with the following pro edure:

• A new four-ve tor pL is omputed, with same dire tion as pj , but with a larger oneaperture Rmax =1.0. The di�eren e four-momentum is ∆p = pL − pj, with energy∆EL, and ML = |∆p|.

• The event visible total four ve tor pE is omputed, the two jets are subtra ted andalso the four-momentum of the tagging lepton (or the two leptons from a Z). Theenergy, and the invariant mass from the resulting four-ve tor are ∆EE and ME .14

• We also ompute three �pseudo-jets� hoosing three one dire tions whi h have thesame pseudorapidity as pj. Two φ angles are hosen in su h a way that the onesare adja ent to the pj one. The third has opposed azimuthal angle than pj. Theenergies integrated in the ones are E1, E2 and E3.The variables tested are ∆EL/E, ML, ∆EE/E, ME , E1, E2, and E3 shown in �gure 15,before and after regularization. The same is done for the variable to guess, whi h wasde�ned to be X=(E-U)/E, where U is the jet energy omputed from pure Higgs de ays(no UE). The original and transformed distributions for X are shown in �gure 16.Unfortunately, no evident orrelation exists between X and any of the hosen variables,as an be seen from s atter plots. We have plugged the normalized variables into a 7 inputsNN (2 hidden layers with 7 nodes ea h, 1 output). The training is done on about 7000events. The test on a similar amount of data onsists for ea h Higgs event to orre tthe energy of the two jets, p1j and p2

j . The di-jet mass plots obtained before and after al ulation are shown on the �gure 17, in red and blue respe tively. For referen e, we alsoshow in bla k the di-jet mass obtained dis arding the UE ba kground parti les.The FWHM resolutions before and after orre tion are almost identi al (33% and32%). The improvement (if any) is marginal, and for the moment we have dropped thepro edure.

Figure 14: Di-jet invariant mass distributions for 4 values of Rmax. Forthe histograms in red all the visible parti les are used in the al ula-tion, while UE parti les are dis arded for the al ulation of the bla khistograms15

Figure 15: Two �rst olumns: six of the seven variables onsidered forfeeding the neural-network. Two last olumns: as before but after �reg-ularization�, to better �t the preferred NN input range

Figure 16: X=(E-U)/E is used to measure the UE ontribution, to besubtra ted. The bottom plot is after regularization16

Figure 17: Di-jet mass obtained before and after NN, in red and bluerespe tively. In bla k the di-jet mass obtained dis arding UE parti les(the ideal ase)7 Con lusion. Algorithm implementationWe have des ribed a NN-based method for the measurement of the energy of b jets, anddis ussed three optimization strategies. Our baseline is to optimize the resolution of thevisible energy �ow in a one de�ned in η − φ, within a radius Rmax =0.5. The NN taskis to orre t for dete tor e�e ts, in parti ular the one asso iated to the alorimetri ellsaturation, and for jets falling loser to the boundaries to ompensate for the energylost outside a eptan e. An experimental version of our pro edure is des ribed in theappendix. The user has to provide the one dire tion (the seed), whi h is subsequentytranformed into an estimate of the jet four-momentum.Depending on the physi s pro ess under study, additional orre tions for neutrinos,hard gluon emission,... might be onsidered, taking into a ount the whole event infor-mation. This will be the subjet of future studies. Nevertheless we also provide the NNtrained to guess the b quark energy (see appendix, option 3).The possibility to orre t for the underlying event has also been examined. We havenot been able to improve the resolution, but one should note that in this study multiplep-p intera tions were not onsidered.

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APPENDIX: the JetEnergyOptimization ToolThis tool performs a NN alibration of b jets. The dete tor information is olle ted withina one in η − φ with Rmax =0.5.7.1 C++ CodeIn order to use the tool in DaVin i, you need to opy the pa kage and pla e the followingline in the requirement �le under the dependen ies se tion:use JetEnergyOptimization v* PhysThe ode must ontain the interfa e of the tool "Kernel/IJetEnergyOptimization" andde lare a pointer m_jet alib to the lass IJetEnergyOptimization as a global variable:#in lude "Kernel/IJetEnergyOptimization"IJetEnergyOptimization* m_jet alib = 0;The tool an now be used after initialization of the pointer:if ( 0 == m_jet alib )m_jet alib = tool<IJetEnergyOptimization> ( "JetEnergyOptimization" , this );. The tool requests the input of a ve tor of seeds (std::ve tor<Gaudi::LorentzVe tor> orstd::ve tor<LHCb::Parti le>), all the harged parti les of the event (the re onstru tionof all the harged parti les is ommented below, and has to be alled before your ownalgorithm), and the primary vertex. It returns the ve tor of the seeds whi h have beenrepla ed by alibrated 4-ve tors.LHCb::Re Vertex::Container:: onst_iterator ipv = PVs->begin();LHCb::Re Vertex V0 = *(*ipv);LHCb::Parti le::ConstVe tor Parts = desktop()->parti les();std::ve tor<Gaudi::LorentzVe tor> Sele tedSeeds;//sele t your seeds...//and now you an get your alibrated jets.StatusCode s = m_jet alib->GetCalibratedJets( Sele tedSeeds,Parts,V0);//your Sele tedSeeds have been alibrated.18

7.2 optionsAt present the only available option is the type of neural network to use. In your DaVin ialgorithm python options you an add the tool and then hose the neural network bysetting MyDVAlg.JetEnergyOptimization.mlpnn at one of the three values 0, 1, or 2:MyDVAlg.InputLo ations = [ "StdTightMuons","StdTightEle trons","StdNoPIDsPions", "StdLooseAllPhotons" ℄MyDVAlg.addTool( JetEnergyOptimization() )MyDVAlg.JetEnergyOptimization.mlpnn = 1 \\ or 0 or 2The three posibilities orresponds to the following setup:• mlpnn = 0: The NN has been trained to guess the visible jet energy, on Higgs eventswith a mass MH0 = 120GeV/ 2

• mlpnn = 1: Idem on a mixed sample of Higgs events with a massMH0 = 90GeV/ 2andMH0 = 120GeV/ 2

• mlpnn = 2: Like 1, but the training was setup to guess the b-quark energyReferen es[1℄ C. Currat, A. Bay, M. Koratzinos, �Jet studies in LHCb�,LHCb/99-016 PHYS, (1999)[2℄ C. Currat, �Dire t sear h for Higgs boson in LHCb�,Thesis of the University of Lausanne CERN thesis-2001-024 (2001)[3℄ L. Lo atelli, �Dire t sear h for Higgs boson in LHCb and ontribution to the de-velopment of the Vertex Dete tor�, Thesis of the E ole Polyte hnique Fédérale deLausanne,http://lphe.epfl. h/engl/publi ations/theses.html (2007)[4℄ V. Co o, �Re onstru tion et identi� ation de jets beaux dans l'experien e LHCb envue d'etudier sa sensibilite a un boson de Higgs standard se desintegrant en pairesbbbar�,Thesis of the University of Savoie (2007)

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