&bdquo;The term flavour was first used in particle physics in the context of the quark model of hadrons

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(1)The LHCb experiment [PHY213 Kern- und Teilchenphysik II] Rafael Silva Coutinho May 23rd, 2018.

(2) Outline This lecturer will cover some aspects involved in the „flavour sector‰ Why LHCb? [Physics programme, b-physics, design]. The LHCb detector How to perform an analysis at LHCb? [e.g. CP violation and LFU measurement]. Lots of material taken from (thanks!) [Roger Forty: ICFA, School] [Monica Pepe-Altarelli, Carfu Summer School] [Daniel Saunders, iCSC]. R. Coutinho (UZH).

(3) Flavour physics. „The term flavour was first used in particle physics in the context of the quark model of hadrons. It was coined in 1971 by Murray GellMann and his student at the time, Harald Fritzsch, at a Baskin-Robbins ice-cream store in Pasadena. Just as ice cream has both colour and flavour so do quarks.‰ RMP 81 (2009) 1887. R. Coutinho (UZH). 3.

(4) Flavour physics. „The term flavour was first used in particle physics in the context of the quark model of hadrons. It was coined in 1971 by Murray GellMann and his student at the time, Harald Fritzsch, at a Baskin-Robbins ice-cream store in Pasadena. Just as ice cream has both colour and flavour so do quarks.‰ RMP 81 (2009) 1887. R. Coutinho (UZH). 4.

(5) Flavour physics Parameters of the Standard Model 3 gauge couplings 2 Higgs parameters 6 quark masses 3 quark mixing angles + 1 phase 3 + 3* lepton masses (3 lepton mixing angles + 1 phase). R. Coutinho (UZH). 5.

(6) Flavour physics Parameters of the Standard Model 3 gauge couplings 2 Higgs parameters 6 quark masses 3 quark mixing angles + 1 phase 3 + 3* lepton masses (3 lepton mixing angles + 1 phase). R. Coutinho (UZH). Flavour parameters. 6.

(7) How do we search for New Physics?. Direct production of new particles c.m.. energy. of. 3.2 GeV. Subse-. DECEMBER. 1974. 5000 2000. QeV.. I. 1000. l I l. I I. —. I. 500. ~ I. I I. 200 b. [LHCb JHEP 02 (2016) 104]. 10 I. &A„3&QeV &A, measurement showed no enhancement, but inS PE CTwere tated the ROMEinternally ap- 3.1-GeV measurements TER 70 out of eight runs giving a low six consistent er to re- section and two runs giving a factor of 3 to cross - H AtThis current could have normalpattern educe 5the higher cross section. at an resonance narrow been caused by a veryQgh rate, I0% current energy slightly larger than the nominal 3.1-QeV eope setting of the storage ring, the inconsistent 3.1and B.QeV cross sections then being caused by setting ) thereerrors in the ring energy. The 3.2-GeV enhancement would arise from radiative corrections of 3 ratail to the structure. which give a high-energy using k of Vfe have now repeated the measurements. I. I I. Ql. 0.5 0.5. 20 IO. 500. much finer energy steps and using a nuclear magto monitor the netic resonance magnetometer ntifica-ring energy. The magnetometer, coupled with of the circulating beam position ters are measurements made at sixteen points around ring the storage in nd alI the orbit, allowed the relative energy to be deterof the mined to 1 part in 104. The determination ee-di- absolute energy setting of the ring requires the knowledge of fBdl around the orbit and is accurts were ate to +0.1@. accep- The data are shown in Fig. 1. All cross secto Bhabha scattering at 20 q = + tions are normalized mrad. The cross section for the production of. 200. 0. 100 b. 50. IO. 200 100. −0.5 −0.5. 50. les ushadrons is shown in GeV inare required to have. Fig. 1(a). Hadronic events 20 b in the final state either ~ 3 detected charged particles or 2 charged particles ' noncoplanar by & 20'. The observed cross secectrum tion rises sharply from a level of about 25 nb to region a value of 2300 + 200 nb at the peak' and then exof ec widthhibits the long high-energye'etail characteristic mewl reactions. The de5.12 radiative corrections in 5.10 5-0 is 45% 25 5.5 e acci-tection efficiency for hadronic events over E, ~ (GeV) ween the the region shown. The error quoted me+e- above Qgv inFIG. 1. Cross section versus energy for (a) multicontriand a error statistical the both cludes hadron final states, (b) e g final states, and (c) p+p, clearFla.uncertainty efficiency. 2. Mass spectru in the m detection showing the J'. and K "K final states. The curve in (a) is the exbution from existence of ~+7t, identals. Our mass determined by the enResultsresolution from two isspectro pected shape of a g-function resonance folded with the meter settings are plotted which arises colliding the the Gaussian energy spread of the beams and including ass ergy spread showingin that peak isbeams independent of spectrometer radiative processes. The cross sections shown in (b) fluctuations in the synchrotron from quantum current s. The run at reduced current was taken two e mass and (c) are integrated over the detector acceptance. radiation emitted by the beams. The expected monthsm.later thandistribution The total hadron cross section, (a), has been corrected the normal run. in agree(@=0. 56 MeV), c. energy Gaussian for detection efficiency. ' eam. folded with the radiative processes, is shown as the dashed curve in Fig. 1(a). The width of the. Io-. 7%%uq. R. Coutinho (UZH). 0. 20. 2,. '. LHCb datadata ATLAS datadata LHCb ATLAS Belle datadata CMS datadata Belle CMS SM from DHMV SM from DHMV SM from ASZB SM from ASZB. 100. 50. drons. 3.. 11. 9. −1 −1 0 0. 5. 5. 10. 10. ψ(2S) ψ(2S). at a. 80- the measurementI at 3.2 we repeated oximaquently, te242 Events~at 3.1 and 3.3 and also made measurements GeV conver t- The 3.2-GeV results the 3.3reproduced,. 2 (1974) 1406]. J / ψ(1S) J / ψ(1S). observed. LETTERS PHYSICAL [PRL 332 REVIEW (1974) 1404, PRL 33 DECEMBER 1974. 5. 33&. PP' 5'. NUMBER 23 CAL VOLUME REVIEW LETTERS. Indirect effects of new particles on well-predicted observables, e.g., the flavour anomalies. 15. q2. 15. 2 4 [GeV /c ] q2 [GeV2/ c4]. [Belle arXiv:1612.05014, ATLAS-CONF-2017-023 CMS-PAS-BPH-15-008].

(8) 104. ATLAS Preliminary. Direct production ofData new particles, 10 Background-only fit by such as the initial excess observed ATLAS/CMS at 750 sGeV di-photon 10 = 13 TeV, 3.2 fb -1. [LHCb JHEP 02 (2016) 104] 5. ATLAS Preliminary Data. 11. LHCb datadata ATLAS datadata LHCb ATLAS Belle datadata CMS datadata Belle CMS SM from DHMV SM from DHMV SM from ASZB SM from ASZB. 3. 10 1. Background-only fit. 102 10−1. 15 10 10 5 10 −5 −10 −1 10 −15. 0.5 0.5. s = 13 TeV, 3.2 fb-1 200. 400. 600. 800. 1000. 1200. 1400. 1600 mγ γ [GeV]. 0. 0. −0.5 −0.5 −1 −1 0 0. 200 400 600 800 1000 1200 1400 1600 15 200 400 600 800 1000 1200 1400 1600 m [GeV] 10 mγγγγ [GeV] 5 [ATLAS-CONF-2015-081] re 1: Invariant mass distribution of the selected diphoton events. Residual number of events with respect to the 0 sult are shown in the bottom pane. The first two bins in the lower pane are outside the vertical plot range. [CMS-PAS-EXO-15-004] −5 −10 events in this region −15 are scrutinized. No detector or reconstruction effect that could explain the larger R. Coutinho is found, nor any indication(UZH) of anomalous background contamination. The kinematic properties of 200 400 600 800 1000 1200 1400 1600. 5. 5. 10. 10. ψ(2S) ψ(2S). 104 10. PP' 5'. Events / 40 GeV. 2. Data - fitted background Data - fitted background. Indirect effects of new particles on well-predicted observables, e.g., the flavour anomalies. 3. J / ψ(1S) J / ψ(1S). Events / 40 GeV. How do we search for New Physics?. 15. q2. 15. 2 4 [GeV /c ] q2 [GeV2/ c4]. [Belle arXiv:1612.05014, ATLAS-CONF-2017-023 CMS-PAS-BPH-15-008].

(9) 104. Data Direct production of new particles, Indirect effects Physics? of new particles on How do we search for New 10 How we search forwell-predicted New Physics? Background-only fit by such as the initial do excess observed observables, e.g., the ATLAS/CMS 750 sGeV di-photon Direct at production of new particles Indirect flavour effects of new particles on anomalies 10 = 13 TeV, 3.2 fb. ATLAS Preliminary. 3. Direct production of new particles [Aubert et al, PRL 33 (1974) 1404] -1. [Aubert et al, PRL 33 (1974) 1404]. 5. ATLAS Preliminary Data. 3. 10 1. Background-only fit. 102 10−1. 15 10 5 10 −5 −10 −1 10 −15 10. 200. Energy sfrontier, = 13 TeV, 3.2limited fb-1 Energy frontier, limited beam 400 600 800 by1000 1200 energy 1400 1600 by beam energy m [GeV] γγ. LHCb data ATLAS data Belle datadata CMS datadata Belle CMS Flavour frontier, where 0.5 SM from DHMV 0.5 Flavour frontier, where SM from DHMV virtual production SM allows to from ASZB SM from ASZB virtual production allows to. probe scales beyond the 0probe scales beyond the energy frontier when energy frontierhigh when performing precision −0.5 −0.5 performingmeasurements high precision measurements 0. −1 −1 0 03. 200 400 600 800 1000 1200 1400 1600 15 200 400 600 800 1000 1200 1400 1600 [Augustin et al, PRLm33 (1974) [GeV]1406] 10 mγγγγ [GeV] [Augustin et al, PRL 33 (1974) 1406] 3 5 [ATLAS-CONF-2015-081] re 1: Invariant mass distribution of the selected diphoton events. Residual number of events with respect to the 0 sult are shown in the bottom pane. The first two bins in the lower pane are outside the vertical plot range. [CMS-PAS-EXO-15-004] −5 −10 events in this region −15 are scrutinized. No detector or reconstruction effect that could explain the larger R. Coutinho is found, nor any indication(UZH) of anomalous background contamination. The kinematic properties of 200 400 600 800 1000 1200 1400 1600. ψ(2S) ψ(2S). 104 10. Indirect effects of new particles one.g., the well-predicted observables, [LHCb JHEP 02 (2016) 104] well-predicted observables, e.g., the flavour anomalies 11 flavour anomalies LHCb data ATLAS data. J / ψ(1S) J / ψ(1S). Data - fitted background Data - fitted background. Events / 40 GeV. 2. PP' 5'. Events / 40 GeV. How do we search for New Physics?. [Descotes-Genon et al, JHEP 12 (2014) 125] [Belle, arXiv:1612.05014][LHCb, JHEP 02 (2016) 104] [Descotes-Genon et al, JHEP 12 (2014) 125] 2 2 [Belle, arXiv:1612.05014][LHCb, JHEP 02 (2016) 104] 2. 5. 5. 10. 10. 15. 15. q [GeV / c42] 4 q [GeV / c ]. [Belle arXiv:1612.05014, ATLAS-CONF-2017-023 CMS-PAS-BPH-15-008].

(10) The indirect approach. Indirect searches for new physics. The indirect approach. Indirect searches for NP are typically with rare decays Decays Decays that are forbidden attree tree are sensitive quantum that are forbidden at levellevel are sensitive to quantumto corrections fromcorrection and try to access quantum corrections from physics at degrees of freedom at larger scales rom degrees of freedom at larger scales larger energy scales b. µs+. b. µ+. s. µ. This indirect approachhas has historically beenbeen used toused predictto thepredict existence the of new his indirect approach historically existence This approach has been used to predict the existence of new particle particles before direct observation was possible particles before direct observation was possible. direct observation was possible. 1 GeV. n n. 1 GeV. Proton. p e e ⌫ ⌫ p 4. R. Coutinho (UZH).

(11) The indirect approach Indirect searches. for new physics Indirect searches newnew physics Indirect searches for physics Thefor indirect approach. The indirect approach. Indirect searches for NP are typically with rare decays ecays that are forbidden at tree level are sensitive to quantum correct Indirect searches for NP are typically with rare decays Indirect searches for NP are typically with rare decays Decays Decays that are forbidden attree tree are sensitive quantum correction that are forbidden at levellevel are sensitive to corrections quantumto corrections from and try to access quantum from physics at anddegrees try to access quantum corrections from physics at om of freedom at larger scales and try to access quantum corrections from physics at degrees ofof freedom at larger scales rom degrees freedom at larger scales larger energy scales largerlarger energy scalesscales energy SM NP b. + µ µ+. b b. s. b. bµ+. µ+. µ. µ µ. s s. s. SM. b. W. b. sW. u , c ,µ t. +. u, c, t. µ/Z 0. NP. s. /Z 0 µ+ µ. s. b. b. g̃. g̃. µ+ µ. s. H+ H+. s. µ+ µ+. µ. µ. his approach has historically been used to predict the existen This indirect approachhas has historically beenbeen used toused predict the existence of new his indirect indirect approach historically to predict the existence This approach has been used tobeen predict theto existence of new particle This approach has been used to predict the existence of new particles bef particles before direct observation was possible articles before direct observation was possible This approach has used predict the existence particles before direct observation was possible observation was possiblewas possible directdirect observation wasobservation possible Proton Proton e direct p e. 11 GeV n GeV n. n 1 GeV. n. 1 GeV. n. ⌫. 1 GeVp. p e ee ⌫ ⌫⌫ p. 80 GeV. ⌫e. e d ⌫n d u. p. 4 4. R. Coutinho (UZH). W. u d p u.

(12) A lesson from history ⁄ New physics shows up at precision frontier before energy frontier -. GIM mechanism before discovery of charm. -. CP violation / CKM before discovery of bottom & top. -. Neutral currents before discovery of Z. Particularly sensitive – loop processes -. Standard Model contributions suppressed / absent. -. Flavour changing neutral currents (rare decays). -. CP violation. -. Lepton flavour / number violation / lepton universality. LHCb roadmap: search for NP in flavour sector! R. Coutinho (UZH). 12.

(13) Why the the bb quark? quark? Why Heaviest quark quark that that forms forms hadronic hadronic bound bound states states Heaviest All decays decays are are CKM CKM suppressed suppressed All Long lifetime lifetime (~1.6 (~1.6 ps) ps) Long Experimentally favourable favourable Experimentally. High mass: mass: many many accessible accessible final final High states with with different different expected expected rates rates states Dominant: „tree‰ „tree‰ b→c b→c transitions transitions Dominant: Very suppressed suppressed „tree‰ „tree‰ b→u b→u transition transition Very FCNC: „penguin‰ „penguin‰ b→s,d b→s,d transition transition FCNC: Flavour oscillation. CP violation – expect large CP asymmetries in some B decays CP violation – expect large CP asymmetries in some B decays R.Coutinho Coutinho(UZH) (UZH) R.. 13 13.

(14) Why Whythe thebbquark? quark? Heaviest Heaviestquark quarkthat thatforms formshadronic hadronicbound bound states states All Alldecays decaysare areCKM CKMsuppressed suppressed Longlifetime lifetime(~1.6 (~1.6ps) ps) Long Experimentallyfavourable favourable Experimentally. Highmass: mass:many manyaccessible accessiblefinal final High stateswith withdifferent differentexpected expectedrates rates states Dominant:„tree‰ „tree‰b→c b→ctransitions transitions Dominant: Verysuppressed suppressed„tree‰ „tree‰b→u b→u transition u transition transition Very FCNC:„penguin‰ „penguin‰b→s,d b→s,dtransition transition FCNC: Flavour oscillation. CP violation – expect large CP asymmetries in some B decays CP violation – expect large CP asymmetries in some B decays Coutinho (UZH) R. R. Coutinho (UZH). 13 14.

(15) Whythe thebbquark? quark? Why Heaviestquark quarkthat thatforms formshadronic hadronicbound bound states states Heaviest Alldecays decaysare areCKM CKMsuppressed suppressed All Longlifetime lifetime(~1.6 (~1.6ps) ps) Long Experimentallyfavourable favourable Experimentally. Highmass: mass:many manyaccessible accessiblefinal final High stateswith withdifferent differentexpected expectedrates rates states Dominant:„tree‰ „tree‰b→c b→ctransitions transitions Dominant: Verysuppressed suppressed„tree‰ „tree‰b→u b→u transition u transition transition Very FCNC:„penguin‰ „penguin‰b→s,d b→s,dtransition transition FCNC: Flavour oscillation. CP violation – expect large CP asymmetries in some B decays CP violation – expect large CP asymmetries in some B decays Coutinho (UZH) R. R. Coutinho (UZH). 13 15.

(16) Why the quark? Why Whythe thebbbquark? quark? Heaviest quark that forms hadronic bound states Heaviest Heaviestquark quarkthat thatforms formshadronic hadronicbound bound states All decays are CKM suppressed All Alldecays decaysare areCKM CKMsuppressed suppressed Longlifetime lifetime(~1.6 (~1.6ps) ps) Long lifetime (~1.6 ps) Long Experimentallyfavourable favourable Experimentally favourable Experimentally. Highmass: mass:many manyaccessible accessiblefinal final High mass: many accessible final High stateswith withdifferent differentexpected expectedrates rates states states with different expected rates Dominant:„tree‰ „tree‰b→c b→ctransitions transitions Dominant: Dominant: „tree‰ b→c transitions Verysuppressed suppressed„tree‰ „tree‰b→u b→u transition u transition Very Very suppressed „tree‰ b→u transition FCNC:„penguin‰ „penguin‰b→s,d b→s,dtransition transition FCNC: FCNC: „penguin‰ b→s,d transition Flavour oscillation. CPviolation violation––expect expect large large CP CP asymmetries asymmetries in some B decays CP CP violation – expect large CP asymmetries in some B decays Coutinho (UZH) R. Coutinho (UZH) R.R. Coutinho (UZH). 13 16 13.

(17) Why the quark? Why Whythe thebbbquark? quark? Heaviest quark that forms hadronic bound states Heaviest Heaviestquark quarkthat thatforms formshadronic hadronicbound bound states states All decays are CKM suppressed All Alldecays decaysare areCKM CKMsuppressed suppressed Long lifetime (~1.6 ps) Longlifetime lifetime(~1.6 (~1.6ps) ps) Long Experimentally favourable Experimentallyfavourable favourable Experimentally. High mass: many accessible final Highmass: mass:many manyaccessible accessiblefinal final High stateswith withdifferent differentexpected expectedrates rates states with different expected rates states Dominant:„tree‰ „tree‰b→c b→ctransitions transitions Dominant: „tree‰ b→c transitions Dominant: Verysuppressed suppressed„tree‰ „tree‰b→u b→u transition Very suppressed „tree‰ b→u transition u transition Very FCNC:„penguin‰ „penguin‰b→s,d b→s,dtransition transition FCNC: „penguin‰ b→s,d transition FCNC: Flavour oscillation. CPviolation violation––expect expectlarge large CP CP asymmetries asymmetries in in some B decays CP CP violation – expect large CP asymmetries in some B decays Coutinho (UZH) R.R. Coutinho (UZH) R. Coutinho (UZH). 13 13 17.

(18) Why Whythe thebbquark? quark? Heaviestquark quarkthat thatforms formshadronic hadronicbound boundstates states Heaviest Alldecays decaysare areCKM CKMsuppressed suppressed All Longlifetime lifetime(~1.6 (~1.6ps) ps) Long Experimentallyfavourable favourable Experimentally. Highmass: mass:many manyaccessible accessiblefinal final High stateswith withdifferent differentexpected expectedrates rates states Dominant:„tree‰ „tree‰b→c b→ctransitions transitions Dominant: Verysuppressed suppressed„tree‰ „tree‰b→u b→u transition u transition transition Very FCNC:„penguin‰ „penguin‰b→s,d b→s,dtransition transition FCNC: Flavour oscillation. CP violation – expect large CP asymmetries in some B decays CP violation – expect large CP asymmetries in some B decays Coutinho (UZH) R. R. Coutinho (UZH). 13 18.

(19) LHCb. 2 0. LHCb design h LHCb from general purpose detectors b̄ -2. -4. b. -6. Detector designed to maximise the acceptance of the b-quark -6. q. b. η. 2. ed. -88 -8. -2. 0. 2. 4. 6. 8 η. The correlation of the bb-pair produced is crucial in the design. 6. ith proper-time resolution. -4. LHCb acceptance. LHCb MC production s = 14 TeV 1. GPD acceptance. 4 2 0. b̄. -2. b. -4. b̄ han at central detectors q̄. b θ1 z. LHCb MC θ2 s = 14 TeV b. -6. vacuum vessel. b. -8 -8. -6. -4. -2. 0. 2. 4. 6. 8 η 1. nce to interaction point b. LHCb MC s = 14 TeV b. b̄. θ1 θ2. b. before first measurement. b. 0. LHCb MC s = 14 TeV. π/4. b. b̄. θ0 2 [rad] π/2 π/4. 3π/4. θ2 [rad] π/2. to identify tracks from B decays b̄. z. 3π/4. π π. 3π/4. π/2 π π. π/4. 0. 3π/4. θ1 [rad]. Figure 4.2: (left) Dominant Feynman diagrams for bb̄ production at LHCb (from top to. π/2. π/4. 0. θ1 [rad]. o resolve fast B B diagrams oscillations eft) Dominant Feynman for bb̄ production at LHCb (from top to 0 gluon 0 separation; and gluon fusion. The correlation of the bb̄ R. Coutinho bottom):(UZH) q q̄ annihilation;. 19.

(20) •. •x-axis: point to the Interaction Point •y-axis: upwards along the beam axis •z-axis: LHCb design. y. b. η. •pseudo-rapidity η = -lntan(θ/2). 2. y of polar θ •instead Detector designed to maximise the acceptance of the b-quark production pseudo-rapidity η = -lntan(θ/2) instead of polar θ •• x. x. 8 LHCb acceptance. 6. z. GPD acceptance. z. 4 2 0. b̄. -2. b. -4 -6. S. Leontsinis. -8 -8. S. Leontsinis. R. Coutinho (UZH). b̄. CMS/ATLAS coverage up to ~here. LHCb MC up to ~ CMS/ATLAS coverage s = 14 TeV U. Zurich. 7 -6. 7. -4. -2. 0. 2. 4. 6. 8 η 1. b θ1. 20.

(21) inal LHC luminosity multiple pp interactions per bunch crossing. Luminosity at LHCb pp. N int / t L × σ inelastic 1034 cm−2 s−1 × 80 mb <N int / BX > = = = ≈ 25 6 −1 N BX / t N BX / t 31.6 × 10 s Luminosity. At nominal LHC luminosity multiple pp interactions per bunch crossing by LHCb. At nominal LHC luminosity per bunch crossing article densities verymultiple high pp in interactions the forward direction covered pp. N int / t L × σ inelastic 1034 cm−2 s−1 × 80 mb <N int / BX > = = = ≈ 25 N BX / t N BX / t 31.6 × 106 s−1. nts with multiple pp interactions fi high detector upancy, lowdensities triggervery and reconstruction efficiency, but: particle high in the forward direction covered by LHCb Particle densities are very high in the forward signal /with background separation eventsacceptance multiple pp interactions fi high detector covered by LHCb. ●. ●. occupancy, low trigger and reconstruction efficiency, Events with multiple pp interactions multiple primary vertices poor signal / background separation in one event. ⇒ highvertices detector occupancy, low trigger and k to assign B decay vertex to wrong primary also: multiple primary in one event reconstruction poor S/B risk to assign B decay vertexefficiency, to wrong primary ,fi reconstruct wrong decay length / decay time. ●. vertex, reconstruct wrong decay length / decay time. Multiple primary vertices in one event solution: slightly mis-align LHC beams in LHCb in LHCb on: slightly mis-align LHC beams ⇒ risk to assign wrong PV and interaction point fi operate at the same time as ction point reconstruction fiatoperate at the same time as ATLAS/CMS, but lower instantaneous luminosity. ●. S/CMS, but luminosity: at lower instantaneous nominalSolution: LHCb × 10 cm sLHC , tuned to luminosity slightly2mis-align beams in LHCb 32. ●. -2 -1. maximize number of point BX with single pp interaction ⇒aoperate atinteraction the same time. as -2 -1 nal LHCb 2 ×- Facilities 1032 cm Flavour Physics luminosity: FS14 CKM (24/25)s , tuned to O. Steinkamp ATLAS/CMS mize number of BX with a single pp interaction. hysics FS14. R. Coutinho (UZH). CKM - Facilities (24/25). O. Steinkamp. 21.

(22) LHCb running conditions The CMS experiment ATLAS/CMS harsh environment would significantly affect the physics programme Another example. R. Coutinho (UZH). 22.

(23) LHCb running conditions ATLAS/CMS harsh environment would significantly affect the physics programme. R. Coutinho (UZH). 23.

(24) LHCb running conditions ATLAS/CMS harsh environment would significantly affect the physics programme. Daniel R. Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2) Coutinho (UZH). One of the hardest cases - Pb 32 collisions in ALICE, a real event. 24.

(25) Luminosity Leveling Luminosity Luminosity Levelling at LHCb Leveling KeepKeep luminosity in LHCb constant throughout LHC fills luminosity in LHCb constant throughout LHC fills ●. ●. continuously monitor luminosity inLHC LHCb interaction luminosity ininstantaneous LHCb constant throughout fills ● Keep continuously monitor instantaneous luminosity in LHCb interactionpoint, point, reduce beambeam separation when lumilumi drops below a pre-defined limit reduce separation when drops below a pre-defined Continuously monitor instantaneous luminosity in LHCb at the limit interaction ● larger ● larger integrated luminosity (= area underneath thethe curve) point, reduce beam separation when lumi drops below a pre-defined limit integrated luminosity (= area underneath curve) Larger integrated luminosity (= area underneath the curve) ● constant ● constant data data taking conditions (detector occupancies, trigger taking conditions (detector occupancies, triggerthresholds, thresholds,etc) etc) Constant data taking conditions (detector occupancies, trigger 32 -2 -1 ● note: thresholds, etc) actually operating cm × nominal luminosity note: LHCbLHCb actually operating at 4at× 410×3210cm s -2s=-12=×2 nominal luminosity. measured. measured luminosity luminosity. beam separation. beam separation. Flavour Physics FS14. FlavourR.Physics FS14 Coutinho (UZH). CKM - Facilities (25/25). CKM - Facilities (25/25). O. Steinkamp. O. Steinkamp. 25.

(26) The LHCb detector. R. Coutinho (UZH). 26.



(29) The LHCb detector Shielding wall (against radiation). Offset interaction point (to make best use of existing cavern). Electronics + CPU farm Detectors can be moved away from beam-line for access R. Coutinho (UZH). 29.

(30) The LHCb detector. ~ 300 mrad. p. p. 10 mrad. ! R. Coutinho (UZH). 30.

(31) The LHCb detector. ~ 300 mrad. p. p. 10 mrad. ! R. Coutinho (UZH). 31.

(32) The LHCb detector. ! R. Coutinho (UZH). 32.

(33) The LHCb detector Vertex Locator (Velo) 21 stations of silicon strip detectors (r-φ) ~ 8 µm hit resolution ~25 µm IP resolution. ! R. Coutinho (UZH). 33.

(34) The LHCb detector Vertex Locator (Velo) 21 stations of silicon strip detectors Example: Bs → Ds(r-φ) K ~ 8 µm hit resolution ~25 µm IP resolution 144 µm. 47 µm. σ(t) ~40 fs K+ K+. Bs. K−. Ds Primary vertex. d~1cm. π− 440 µm. ! R. Coutinho (UZH). 34.



(37) Outer Tracker. 24 layer Straws σhit~200µm. Trigger Tracker. Inner Tracker. 4 layers Si: ~200 µm pitch R. Coutinho (UZH). 37.

(38) Outer Tracker. 24 layer Straws σhit~200µm. Mass resolution σ ~14 MeV π+, K+. Bs Ds Primary vertex. Trigger Tracker. !. Bs→ Ds K Bs →Ds π. btag. K+ K− π−. Inner Tracker. 4 layers Si: ~200 µm pitch R. Coutinho (UZH). 38.



(41) The LHCb detector. e. ! R. Coutinho (UZH). 41.

(42) The LHCb detector. e. R. Coutinho (UZH). 42.

(43) The LHCb detector. µ. ! R. Coutinho (UZH). 43.

(44) The LHCb detector. µ. ! R. Coutinho (UZH). 44.

(45) Mass reconstruction From relativistic kinematics, the relation between energy E, momentum p, and (rest) mass m is: E2 = p2 + m2 [The full expression: E2 = p2c2 + m2c4 but factors of c are often dropped]. Consider a particle that decays to give two daughter particles:. The invariant mass of the two particles from the decay: M 2 = m12 + m22 + 2 (E1E2 − p1 p2 cosθ ) to reconstruct the parent mass a precise knowledge of the momentum and the angle θ of decay products is needed, from the tracking system, as well as their particle type, which determines their masses m1 and m2. R. Coutinho (UZH). 45.

(46) 5.2 H ! ZZ. Mass reconstruction Typical example of reconstruction of a particle decay: π0 → γγ one of the first composite particles reconstructed in the LHC experiments This technique an also be used to search for more exciting signals:. Data Sig+Bkg Fit (mH=126.5 GeV). 3000. 2000. 1000. weights / 2 GeV Events - Bkg. 500. 200100 100 0 -100 -200 100. s=7 TeV, ∫ Ldt=4.8fb-1 s=8 TeV, ∫ Ldt=5.9fb. (a). H→γ γ. 110. 120. 130. 140. 150. 160. 110. 120. 130. 140. 150. 160. (b) Data S/B Weighted. 100 80. R. Coutinho (UZH). 1000. 1000. -1. Sig+Bkg Fit (mH=126.5 GeV) Bkg (4th order polynomial). Unweighted. 1500. 1500. Bkg (4th order polynomial). 2500. 1500. s = 7 TeV, L = 5.1 fb-1 s = 8 TeV, L = 5.3 fb-1 Events / 1.5 GeV. ATLAS. 3500. S/(S+B) Weighted Events / 1.5 GeV. Events / 2 GeV. CMS. 500. 0. 120. 130. mγ γ (GeV). Data S+B Fit B Fit Component ±1 σ ±2 σ. 110. 120. 130. 140. 150. mγ γ (GeV). Figure 3: The diphoton invariant mass distribution with each event weighted by the S/(S 46 value of its category. The lines represent the fitted background and signal, and the colo.

(47) Tracking - Pattern recognition 2-side semi-circular (R and ϕ sensors) microstrip silicon 300µm n+-on-n sensors (two sensors are n+-on-p);. LHCb VErtex LOcator example LHC vacuum. Strip pitches from 40 to 120 µm; Evaporative CO2 cooling system to keep sensors at -7oC.. Secondary vacuum. Pile-up. p. Interaction point. Modules (21+2). p R. Coutinho (UZH). RF foil (300µm to beam vacuum) 47.

(48) Tracking - Pattern recognition Tracking - Pattern Recognition Example •Looking Lookingside sideon: on:. Particle tracks clearly visible to eye. • - Extra Particle tracks clearly to hits present, typicallyvisible electrical noise or secondary short tracks. •. -. eye. Extra hits: typically electrical noise and/or secondary show tracks. Recall data points in the format: (x, y,points z, time)into „Transform‰ data. •. (x, y, z time). x. z (beam). Time resolution only accurate to which collision the particles come from (25ns, Target: find an algorithm to track using sometimes worse…). •. this information: •. Have to find an algorithm to track using - Many possible choices, this information and„seeding‰ in these conditions. combinatorial, ⁄ Many choices - consider the following (LHC) examples…. R. Coutinho (UZH) Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). LHCb VELO data event (2d projection). 48 34.

(49) Tracking - Pattern recognition Tracking - Pattern Recognition Example Name Combinatorial. Description • •. Form every track from each possible combination of hits. Access each track by quality (e.g. !2) and tag.. Scalability nTracks!. LHCb VELO data event (2d projection, top half) 37 Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). 49.

(50) Tracking - Pattern recognition Tracking - Pattern Recognition Example Name Combinatorial. Description • •. Form every track from each possible combination of hits. Access each track by quality (e.g. !2) and tag.. Scalability nTracks!. LHCb VELO data event (2d projection, top half) 38 Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). 50.

(51) Tracking - Pattern recognition Tracking - Pattern Recognition Example Name Combinatorial Hough Transform. Description • • • • •. Form every track from each possible combination of hits. Access each track by quality (e.g. !2) and tag. Transform points into a system where clusters form. If straight tracks, take the difference between consecutive hits. Group (e.g. in a histogram) and tag peaks.. LHCb VELO data event (2d projection, top half) Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). Scalability nTracks!. nx2. 39. 51.

(52) Tracking - Pattern recognition Tracking - Pattern Recognition Example Name Combinatorial Hough Transform. Description • • • • •. Form every track from each possible combination of hits. Access each track by quality (e.g. !2) and tag. Transform points into a system where clusters form. If straight tracks, take the difference between consecutive hits. Group (e.g. in a histogram) and tag peaks.. LHCb VELO data event (2d projection, top half) Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). Scalability nTracks!. nx2. 40. 52.

(53) Tracking - Pattern recognition Tracking - Pattern Recognition Example Name Combinatorial Hough Transform. Description • • • • • •. Seeding. • •. Form every track from each possible combination. Access each track by quality (e.g. !2) and tag. Transform points into a system where clusters form. E.g. for straight tracks, take the difference between consecutive hits. Group (e.g. in a histogram) and tag peaks. Form seeds from pairs of hits on a sub set of the detector. Extrapolate the seed and count hits intercepted. Tag if sufficient number of hits.. Scalability nTracks!. nx2 nlog(n). LHCb VELO data event (2d projection, top half) 41 Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). 53.

(54) Tracking - Pattern recognition Tracking - Pattern Recognition Algorithms Three main features used to decide most appropriate algorithm •. -. Recall three main factors in choosing such algorithms:. Efficiency: real tracks found • Efficiency: fraction fraction ofof real tracks found • Purity: Purity: fraction oftracks tracksthat that fraction of areare realreal • Computational speed. Computational speed. • Toy simulation for LHCb VELO: Simplified simulation using LHCb VELO design. LHCb VELO toy event (2d projection) 43 Daniel Saunders,(UZH) iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2) R. Coutinho. 54.

(55) Tracking - Pattern recognition Tracking - Pattern Recognition Algorithms Three main features used to decide most appropriate algorithm •. -. Recall three main factors in choosing such algorithms: • Efficiency: fraction fraction ofof real tracks found Efficiency: real tracks found • Purity: fraction of areare realreal Purity: fraction oftracks tracksthat that • Computational speed. Computational speed. Any use case for green? Curves!. • Toy simulation for LHCb VELO: Simplified simulation using LHCb VELO design. 44. R. Coutinho Daniel Saunders,(UZH) iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). 55.

(56) Tracking - Pattern recognition Tracking - Pattern Recognition Algorithms. - Pattern Recognition Algorithms Typically useTracking a combination of these algorithms. Each example taken from LHC activities In general experiments use a combination of these approaches •. Typically use a combination of these algorithms. Each example taken from LHC activities: Combinatorial often used • Combinatorial often used at testbeams: at testbeams: • Low occupancy, so fast. • Low occupancy, so fast. • Efficient and pure. • Efficient and pure. •. •. Timepix3 Telescope Timepix3 Tracking Tracking Telescope. •. Testbeam Testbeam Data Data. • Hough transforms Hough transforms usedused forfor more complicated shapes more complicated shapes (e.g. rings in LHCb RICH*). (e.g. rings in LHCb RICH*).. LHCb RICH Subdetector. LHCb RICH Subdetector. RICH Data. RICH Data. All LHC experiments use seeding extensivelyuse • All LHC experiments (highest occupancy). seeding extensively •. (highest occupancy). *Often not needed to actually reconstruct rings.. ATLAS Tracker. Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). *Often not needed to actually reconstruct rings.. ATLAS Tracker. ATLAS Inner Layers. ATLAS Inner Layers. 45. 56.

(57) Tracking Fitting Tracking particles through the detectors involve two steps. Trackdetector Fittinghits in order to build a track Pattern recognition: identify Trackparticles fit: approximate the path oftwo thestep. particle with an equation • - Tracking through detectors involves -. •. Pattern recognition: identifying which detector hits for a track. Track fit: approximate the path of the particle with an equation.. • Mostly approximated using a „Kalman-Fitter‰:. •. a Kalman filter. Basic - Typically Trackuse is approximated assteps: a „zig-zag‰ -. Track is approximated as a ‘zig-zag’ (fewer free parameters than co-ordinates!). Start with a seed to estimate of track parameters • Start with seed or estimate of track parameters (e.g. straight line fit). to next the plane next(approximating plane B field, account for scattering in material). • propagate Propagate to the • Predict Predict position of nextof particle, by closest hits (needs be tuned). position nextweighting particle, weighting bytoo closest hits •. Kalman Filter Example Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2) R. Coutinho (UZH). 46. 57.

(58) Tracking refinement Track Refinement Common to tune pattern recognition to be efficient and impure → refine selection later using full particle information. Common to tune pattern recognition to be efficient and impure: refine selection later using addition information. •. • •. •. Can use !2 to find well fitting tracks. - Can Can 𝝌2 to findwith wellother fitted tracks alsouse use/combine - parameters: Typically combine with information • Number of hits (complimentary from different detectors and number information to !2). of hits • Fits from different sub detectors. LHCb. - For optimal approach a MVA is often Typically build an MVA out of different used in experiments. quality parameters - LHCb uses a neutralhits net.can also be part of multiple tracks: Detector. Detector spatial resolution too low to separate tracks - Secondary tracks produced with the interaction with material Caution: if fake/ghost tracks are formed from parts of real tracks, they may be lost. -. •. 47. Coutinho (UZH) Daniel R. Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). 58.

(59) Vertexing Vertexing involves clustering tracks that originated from the same point. Vertexing (Briefly). •-. Easy ininvolves cases clustering that the tracks vertexthatlocation known - extrapolate Vertexing originate is from the same point.. all tracks and. Easy in some cases where vertex location is known - extrapolate all tracks and apply apply selection selection criteria. Physics inputs can narrow search region significantly • Else, Physics input can narrow search region significantly. Some analytical methods can also be used to seed search • Can use analytic methods (e.g. distance of closest approach) to seed search. approach to seed projecting • Common Common to seed by projecting into 2Dby plane and searchinginfor2D pointplane of high and searching for a “track density” (essentially a peak finding/clustering problem). point with high track density •. -. p. x. p. p. Detector Vertex particle Tracked particle. 1cm. Vertex example in LHCb Velo.. End on projection. 49. Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). 59.

(60) Particle identification Particle ID (Briefly) • Classify each trackasasa atype typeof of particle particle event Classify each track eventby byevent: event • Needed to refine selections for offline analysis (remove background). Many kinds of particle, not just fundamental particles, also composite hadrons • Many kinds of particle: (e.g. Pion, Kaon) •. •. Not just fundamental particles, also composite hadrons (e.g. Pion, Kaon).. Some easy cases:. “Simple” example in CMS:. 50 Daniel Saunders, iCSC 2016 - Data Reconstruction in Modern Particle Physics (Lecture 1/2). R. Coutinho (UZH). 60.

(61) Particle identification RICH detector at LHCb uses Cherenkov radiation: -. Light emitted when a particle slows passing through a material Emission is isotropic, and forms rings on detectors Not required to reconstruct the ring itself - instead, test different hypotheses. Light produced in a cone with cosθc=1/βn can be detected as a ring image. By measuring θc (∝ radius of ring) the velocity β of the particle is found Then with knowledge of its momentum the mass of the particle can be found. R. Coutinho (UZH). 61.

(62) Particle identification RICH detector at LHCb uses Cherenkov radiation: -. Light emitted when a particle slows passing through a material Emission is isotropic, and forms rings on detectors Not required to reconstruct the ring itself - instead, test different hypotheses. Simulated event in RICH-1 Large rings: aerogel, small: C4F10 R. Coutinho (UZH). 62.

(63) Particle identification Test beam. Separating two particle types using the signal from a RICH detector is illustrated for K and π from a test beam Adjusting the position of the cut placed between the two peaks to identify a ring as belonging to a K or π gives a trade-off between efficiency and misidentification LHCb particle identification is actually built by combining not only information from the RICH, but also from other sub-detector in a multivariate analysis. R. Coutinho (UZH). LHCb simulation. 63.

(64) Particle identification performance Example: clean separation of Bd,s → hh modes. without PID. with PID. Criteria inly applied in the bachelor Kaon. R. Coutinho (UZH). 64.

(65) How to perform an analysis? Physics case - Previous lecture by Nico. CP Violation in the Early Universe • Very early in the universe might expect equal numbers of baryons and anti-baryons • However, today the universe is matter dominated (no evidence for anti-galaxies, etc.) • From “Big Bang Nucleosynthesis” obtain the matter/anti-matter asymmetry. i.e. for every baryon in the universe today there are. photons. • How did this happen? Early in the universe need to create a very small asymmetry between baryons and anti-baryons e.g. for every 109 anti-baryons there were 109+1 baryons baryons/anti-baryons annihilate 1 baryon + ~109 photons + no anti-baryons To generate this initial asymmetry three conditions must be met (Sakharov, 1967): ❶ “Baryon number violation”, i.e. is not constant ❷ “C and CP violation”, if CP is conserved for a reaction which generates a net number of baryons over anti-baryons there would be a CP conjugate reaction generating a net number of anti-baryons ❸ “Departure from thermal equilibrium”, in thermal equilibrium any baryon number violating process will be balanced by the inverse reaction Mark Thomson/Nico Serra. R. Coutinho (UZH). KTII - 2018. !2. 65.

(66) How to perform an analysis?. e 4: Summary of the values of the cuts used to form the Hb ! h+ h0 candidates by the ppingB2HHBDTLine, previous to the application of the BDT algorithm. The meaning of the us symbols is explained in the text.. Event Reconstruction Implementation. → →. → →. Events / ( 8 MeV/c2). Entries/(0.005 GeV/c2). → →. necessity) follows sequentially, e.g:. Particle ID. → Histogram plotting. Output. Such a chain can be performed for a single event,10000 or large set of events.. • 70000. •. Reminder: each event is (usually) statistically independent of each-other. No PID applied. 60000. 0. B → π +π 0 + B → K πPart. Reco. Bkg. Comb. bkg.. 8000. Strategy for single core is obvious, but for multi core, not so much. 6000 Nowadays, reconstruction involves tens of thousands of CPUs worldwide - need 4000 efficient strategy. pre-Selection Currently limited by memory: 2000. •. 50000. Entries Mean RMS. •. 40000. 30000. •. 20000. 5. •. htemp 3497883 5.345 0.2213. Events / ( 8 MeV/c2). Cut type value Track 2 /ndf e.g. Β< Analysis framework: →3 ππ decays • Each reconstruction stage <typically (sometimes by Track GhostProb 0.5 Track pT [ GeV/c ] > 1.0 Track dIP [ µm ] > 120 dCA [ µm ] < 100 b dH < 120 IP [ µm ] t⇡⇡ [ ps ] > 0.6 Hb Input pHistogram Track> 1.2 Track Vertexing T [ GeV/c ] fitting plotting finding. 8000. 6000. 4000. 2000. 5.2 5.3 of 2011 5.4 5.5 5.6 5.7 5.8 E.g. CMS end could only 6 out of 8 cores on average. m. 5.1. +. ⇡+⇡. π π-. re 1: Distribution of invariant mass under the final state hypothesis for the 0events 5 ving the StrippingB2HHBDTLine stripping selection and the trigger requirements described Daniel Saunders, iCSCof2016 - DataisReconstruction Modern Particle Physics (Lecture 1/2) e text. The total amount events about 3.75 inmillions. R. Coutinho (UZH). 5.2. 5.4. 5.6 5.8 mπ+π- [GeV/c2]. 0 54. 5 66.

(67) Background. 30 0.3. Background Background. 0.3 0.4. 20. 0.2 0.2 0.2 0.2. 0.2. 0.2 10-2. 10 105. 5. 0 0. 1020 25 15 + + min(p ,p ) (GeV/c) max(p ,p ) T8(GeV/c) T 6 10 T 10 T +2 +2 -2 2 log(max( χ (dχ (d ),χ ), (dχ (d ))) ))) log(min( 15. 0 0 0 0. 2. 4. 0 0. 6 8 10 + 2 2 log(min(χ (d ),χ (d ))) 0.0615 0.0820 IP 0.1IP25 + max(p d,p )(mm) (GeV/c) CA. Signal Signal Background Background. 30. 0.4-1 10. Background Background. T. 0.2 20. Signal. 0.4. 30. 0 10-3 0 0 0 0 0 0. Background. 10-1. 10-2. -2 0.110. 5. 5 0.0210. 0 0. 10 15 10 log(max(χ2(d+ ),χχ22(d ))) IP IP 20 0.06 0.08 30 vtx 0.1 Hb dCA (mm) p (GeV/c). 0.04. 0 10-3 0 0. 0.02 10. 0.04 5. 10-3 0. 0.06. 0.08 0.1 dCA (mm) 30 40 15 10 Hb 2 2 χ (d ) (mm) χvtx. 20. 0. 5. IP. Entries/(0.12) Entries/(0.2). Signal ackground. 0.2. T. Entries/(0.12) Entries/(0.2) Entries/(0.2 GeV/c). gnal. 0.4. 1. 10-1 10. 10. max(χ2(d+ ),χ2(d ))) IP IP. Background Background. 20. 10-2 0.2. 0.08 0.1 dCA10(mm). Signal Signal Background. 1. 0.3. 0.2. T. Signal Signal. Entries/(0.12). Background. IP. Signal Signal. 1 0.4. 0.0410. Entries/(0.12). Signal Background. IP. Entries/(0.15) Entries/(0.002 mm). Entries/(0.2 GeV/c) Entries/(0.15) Entries/(0.002 mm) Entries/(0.12). Signal. IP. Entries/(0.2). IP. T. 0.02 5. 5. 10-3 0. “Offline” selection: apply a set of criteria to have a “clean” signal distribution. T. 5 4. 2. 1 0.4. 10-1. 0.1 0.2. 0.1 0 0 0 0 0 0 0 0. Background Background. 0.4. How to perform an analysis?. 8 10 + 2 2 n( χ (d ),χ (d ))) 15 10 IP IP + min(p ,p- ) (GeV/c). Signal Signal Background. Entries/(0.25 G. Signal Signal Background Background. 0.4 0.4. Signal. Entries/(0.15). 0.6 0.4. 0.4 0.6. Entries/(0.15). Signal Background. Signal Signal. Entries/(0.002 mm) Entries/(0.25 GeV/c) Entries. Entries/(0.12) Entries/(0.25 Ge Entries/(0.1) Entries/(0.2 G. Signal. 0.4 Signal. Figure 40: Distribution of the variables used in the training of the BDT algor decays (red histogram) and high invariant mass sideband events (blue his 2011 and 2012 samples is used to produce the histograms. 0.4. Signal Background Background. 0.4 0.4 0.3. Background. 0.3. Signal Background. 0.4. 0.4. Background. 0.2. 0.2. 0.2 0.2 0.2. 0.1 0.2 0.1. 0 0 0.6. 10. 10 20. 30 Hb (GeV/c) log(χp2T(FD)) 30 40 Signal 2 Hb χ (d ) (mm). 0 0. 20. IP. 0 0.4. + ⇡ 0.3 Figure Distribution of0 the the BDT40: algorithms for B ! ⇡variables. R. Coutinho (UZH). ining of. 10. 5. Background. 10 5. 20 10. Signal. 30 40 Hb 2 χ (d ) (mm) IP. log(χ2(FD)). 0 es/(0.25 GeV/c). 0 0. Entries/(0.1). T. 0 ies/(0.2 GeV/c). 30 40 Hb 2 20 χ (dIP ) (mm) 30 H p b (GeV/c). 0.6. 67. used in the training of the BDT alg Background. 0.4.

(68) Figures 37 to 44 in Appendix B, for both background and signal events. port the distributions of the output of the BDT algorithms, correspondin to performWith an analysis? +K d BDTKHow selections. the label Train we identified the distribu m the samples used to train the three algorithms; with the label Optim. e distributions used in the optimization phase of the selection; with the l “Offline” selection: apply a set of criteria to have a “clean” signal distribution entified the distributions used in the final analysis. As it can be seen in - Typically experiments useagreement. a “multivariate” approach, which can then classify ses the distributions are in. Sig. Train Sig. Optim. Sig. Final Bkg. Train Bkg. Optim. Bkg. Final. 2. 1.5. 160. Entries/(0.02). Where to apply a “cut”?. ξ = S/ S+B. Entries/(0.02). the events as “signal-backgrounds”. Sig. Train Sig. Optim. Sig. Final Bkg. Train Bkg. Optim. Bkg. Final. 2. “Significance” 1.5. 140. 1. 1 120. 0.5. 0.5 100. 0 −1. −0.5. 0. 0.5. gure 3:R. Coutinho Distribution of the BDT (UZH). 1 BDT. −1. 0 −0.5 -1. 0. -0.5. 0.5. 0. BDT >. 0.5. p variable the background-like and 68 Figurefor 5: Estimated value of ⇠ = S/ (blue) (S + B) as.

(69) How to perform an analysis?. 0. + -. B→ π π 0 + B → K πPart. Reco. Bkg. Comb. bkg.. 10000 8000. Events / ( 0.008 GeV/c22) Events / ( 8 MeV/c ). Events / ( 8 MeV/c2). - Typically experiments use a “multivariate” approach, which can then classify the events as “signal-backgrounds” 0. 4000 8000 LHCb Preliminary. 3000 6000. 6000. B → 0π+π- + 0 B →K K Bs→πs0+π- + B→ Kπ 0 + B →Part. K π Reco. Bkg. Part. Reco.bkg. Bkg. Comb. Comb. bkg.. Events / ( 8 MeV/c2 ). “Offline” selection: apply a set of criteria to have a “clean” signal distribution. 3000. 2000. 4000 2000. 4000. 2000 1000. 2000. 00. 5.6 5.8 mπ+π- [GeV/c2]. 55. 5.2 5.2. 5.4 5.4. 0. 5. 5.6 5.8 5.6 5.8 22] + - [GeV/c m mπK+πK- (GeV/c ). ). R. Coutinho (UZH). 5.4. 2. 5.2. ). 5. 2. 0. 1000. Figure 4: Invariant mass fits used for the relative normalization of signal and background yields. 69.

(70) Invariant mass fit and raw CP asymmetries • The raw CP asymmetries are measured from unbinned maximum likelihood fits to. CP violation in B decays the Kπ invariant mass spectra. • Two different selections have been optimized in order to achieve the best sensitivities on the two CP asymmetries. B0K+π. Invariant mass fit and raw CP asymmetries B0K π+. • The raw CP asymmetries are measured from unbinned maximum likelihood fits to the Kπ invariant mass spectra. • Two different selections have been optimized in order to achieve the best sensitivities on the two CP asymmetries. 0Kπ) = -0.091 ± 0.006 Raw A (B CP - +. B0sK π Maria Zangoli. First 5. observation of CP violation in the decays of B0s at LHCb. 8/13. B0sK+π. B0sK π+. R. Coutinho (UZH). Raw ACP(B0sKπ) = 0.28 ± 0.04. 70.

(71) Physics case (II) - flavour anomalies ndirect searches for new physics. ndirect searches for NP are typically with rare decays The SM predicts that particles nd try to access quantum correctionscouple fromuniversally physics at to leptons of different flavours arger energy scales searches for new Indirect physics SM. s. b. W. Indirect searches for NP are typically with rare decays + µ u, c , t and try to access quantum corrections µfrom physics at /Z = 1 + O(10 ) µ µ larger energy scales b. s. +. 0. s. 3. SM. b. W. s. his approach has been usedµ+to predict theuexistence of new par b , c, t + e µ direct observation was possible Proto /Z µ +. 0. Measurements of lepton flavour universality (LFU) constitute theoretically every clean probes of this hypothesis. n 1 GeV. eµ. [PRD 69 074020 (2004)]. This approach has been used to predict the existence of ⌫. R. Coutinho (UZH).

(72) Physics case (II) - flavour anomalies ndirect searches for new physics. ndirect searches for NP are typically with rare decays SM, theories nd try to access quantum correctionsBeyond fromthe physics atcan feature non-universal couplings arger energy scales searches for new physics Indirect NP. s. b. g̃. Indirect searches for NP are typically with rare decays + µ and try to access quantum correctionsµfrom physics at H = 1 + O(10 ) µ µ larger energy scales b. s. +. +. s. 3. NP. b. g̃. s. his approach has been used +to predict the existence of new par µ b + µ e irect observation was possible Proto H µ +. +. Measurements of lepton flavour universality (LFU) constitute theoretically every clean probes of this hypothesis. n. eµ. [PRD 69 074020 (2004)]. This approach has been used to predict the existence of. 1 GeV R. Coutinho (UZH). ⌫.

(73) Physics case (II) - flavour anomalies ndirect searches for new physics. ndirect searches for NP are typically with rare decays flavour transitions nd try to access quantum correctionsThese from physics at can be measured through ratios of decay rates arger energy scales searches for new physics Indirect NPq. s. b. q. g̃. K , K ⇤ , .... Indirect searches for NP are typically with rare decays + µ B , B , ... and try to access quantum correctionsµfrom physics at H = 1 + O(10 ) µ µ larger energy scales b. s. 0. +. +. s. 3. NP. b. g̃. q. s. K , K ⇤ , .... his approach has been used +to predict the existence of new par µ b + µ e irect observation was possible Proto H µ +. +. Measurements of lepton flavour universality (LFU) constitute theoretically every clean probes of this hypothesis. n. eµ. [PRD 69 074020 (2004)]. This approach has been used to predict the existence of. 1 GeV R. Coutinho (UZH). ⌫.

(74) Part-Reco Background − I. in B-meson decays.. Long tracks have hits in all tracking sub-detectors, so they traverse the entire for› Partially-reconstructed backgrounds arise from decays invol ward tracking region. This provides them with the most accurate momentum K resonances with one or more decay products in addition t estimate of all track types. These tracks are the(+,*0) dominant + input - for physics that are not reconstructed analyses.. How to select B → K. e e events?. › Large variety of Downstream tracks have hits in the Tracker Turicensis and the T stations, but not * lived K and in the Vertex Locator. They are of interest when lookingB→K for long s 2 (1430)ee ⇤b particles, which decay outside of the VELO.. decays, most abundant due to B→K1(12. The electron identification at LHCb relies on a few detector features. T tracks contain only hits of the Inner Tracker and Outer Tracker. These tracks are used for pion and kaon reconstruction in RICH2.. Upstream. Bremsstrahlung − I. › Electrons emit a large amount of bremsstrahlung that results in degraded momentum and mass resolutions VELO X XX XXX X X X XXXX X. TT XX XX. XX XX. Long. T. XXX. XXX. › Two types of bremsstrahlung XXX. X XX. XXX. XXX. Downstream » Downstream of the magnet X - photon energy Simone in theBifani same Velo calorimeter cell as the electron X XX X Z regioncorrectly measuredXX XXX -Magnet momentum T−stations » Upstream of the magnet Figure 5.1: Counting IT and OT as one sub-detector, Long tracks traverse all track- photon energy in different ing sub-detectors. The Velo, Upstream,calorimeter Downstream,cells andthan T tracks cross subsets of electron the tracking sub-detectors, as shown in this illustration. - momentum evaluated after bremsstrahlung XX. R. Coutinho (UZH). XX. CERN Seminar. Upstream brem. Downstream brem. Air.

(75) How to obtain the number of events? [LHCb, LHCB-PAPER-2017-013]. 5400. B → K ee Low. 10. 020. B → 115 25 1010 20. 10−15 1155. 4500. 0. 2 10−−10 5 4500 −14500 5 5 10 4500 −20 5 10 −4500 5 4500 450010−−205. 4500 4500. Pulls Candidates per 10 MeV/c2. Pulls Candidates per 10 MeV/c2. LHCb. 50. 10 MeV/per c2 10 MeV/c2 Pulls Candidates Candidates Pulls per. 3. arXiv:1705.05802 B →K J / ψ. K µ µ B →KB J→ /ψ →2K *0µ +µ − arXiv:1705.05802 B0→K *0J / ψ 2 450 20 1.1< q <6.0 [GeV / c ] arXiv:1705.05802 Combinatorial Combinatorial 40 Combinatorial 40 503Combinatorial 40 0 ×103 + 50 0 ×10 Λb →3K +p J / ψ Λ →K p J / ψ 80 60 b 30 10 0×10 *0 3060 40 LHCb 40 30 BLHCb Bs0→K *0J / ψ LHCb LHCb s →K 0 J / ψ *0 70 80 0 60 *0 + − 30 *K 0 2+µ −µ 2 4 *→ 0 K µ +2µ −4 50 0B → 30 LHCb 2<1.1 2<6.0 20 2B →qK µ [GeV B0→qBK J /[GeV ψ /c ] 0.045< 2050LHCb 201.1< 1.1< q70 <6.0 [GeV /µ c4] / c ] 60q2<6.0 [GeV2/ c4] 1.1< 0 20B0→K *0µ +µ − 50 Combinatorial Combinatorial B →K *0J / ψ 20 5 Combinatorial Combinatorial 40 5200 60 5400 5600 5800 5200 10 5400 50 1040 10 10 + 0 Combinatorial Combinatorial 10 + − + − 0 pJ /ψ 2K 40 Λb → 50 c ] + 0 40 m(K π µ µ ) [MeV/ 30 0 Λ5b →K p J / ψ 556005200 530 5200 →5600 K *0J / ψ5800 5400 −5800 5 5 5200 5Bs5200 5200 5600 58005800 5400 5600 5400 58005400 5600 5800 5400 *0 40 +5600 30 30 +2+ − − + − 5400 +− − + − 22 4 + − 2 0 0s0→ 0 B +5600 + −K + J−/ ψ 22] 4c2] 0 0 2]5200 −µ +K −µ +µ −) [MeV/ c2] 5200 5800 5400 m ( K π µ µ ) [MeV/ 20 m ( K π µ ) [MeV/ c m ( K π µ µ ) [MeV/ c ] m ( π µ µ ) [MeV/ c m ( K π m (K π µ µ ) [MeV/ c2] 2 0.045< q [GeV <1.1 [GeV 1.1< q <6.0 [GeV / c ] 2<6.0 4] / c ] 20 1.1< q / c −5 −5 −5 −30 5 −5 + −. 700 B → B →K *0K µ +µµ− µ 60 Combinatorial Combinatorial. 5600 5600 5200. 50. 58005800 5400. + −+ +− −+ − m(Km20 c2] c2] (πK µπ µµ )µ[MeV/ ) [MeV/. 5200 5400 5 5200 5400 2 0 0 5600 5800 2 −5−5 m5200 (K +π −µ +µ −) [MeV/ c2] 5400 5200 5400 5600 5800. 10. 60 B. 0. 0. *0. + −. 50. 205600 2 +4 − +[GeV 20 5200 − 5400 2 5200 5600 58005400 5600 5200 5800 5400 5200 5600 5800 1.1< q2<6.0 / c5800 ] 5400 10 + − + − 2] 10 m(10 +µ −) c[MeV/ [MeV/ K π µ µ ) [MeV/c2] m(K +πm−µ (K+µ+π−)−µ c2] m(K +π −µ +µ −) [MeV/c2]. m(K π µ µ ) [MeV/c ]. 5 5 5200 5400 5400 05600 2 0 5200 5800 5 (K +π −µ +µ −) [MeV/ c2] −5 −m 52005200 54005400 5600 5800. 10. 5. Combinatorial Λb0→K +p J / ψ LHCb Bs0→K *0J / ψ B0→K *0J / ψ Combinatorial 5600 5800 Λb0→K ++p J−/ ψ+ − 2 0m(K*0π µ µ ) [MeV/ c ] Bs →K J / ψ 5600. 5800. + − + − m(K 5600 π µ µ ) [MeV/ c2] − 5600 + − + 5800. 5800. m(K π µ µ ) [MeV/c2]. m(K +π −µ +µ −) [MeV/c2]. 2 5 m µψ ) [MeV/ (:K 274K π µ µ ) [MeV/ c ] :(K05274K ψ21region m(K0 ± π µ18 µ ) [MeV/ c ] q : 285Central K353 π (K µ µπ J/ )µ± [MeV/ cregion ] c] Low ± 18 ±Central Low q2 : 285 ±±Central 21 J/ψ regionm5600 Low q : 353 285 18 5800 q : 353J/ q2:m05:(274K 21 0 m− π5200 µ µ ) [MeV/ c ]5400 5600 5600 5800 5800 5400. *0. 25. 60. 5600. + −5200 + −. 5600 −5. 5200. 5800. 2 5400. 5800. 5400. m(K +π −µ +µ −) [MeV/c2]. q2. 0. m(K +π −µ +µ −). *0. [MeV/c2]. 5600 5800 5600 + − + − 5800 2 5m+(K π+µµ−)µ[MeV/ ) [MeV/ −µ 2] c ] m(K π5200 c5400. −5600 5. 5800. + − + − 54002 π ) [MeV/ m(m K(+K π −5200 µ +µ µ −)µ[MeV/ c2]c ]. m(K +π −µ +µ −). [MeV/c2]. 5600 5600+ 5800 5800 2 + − + −− + − 5400 2 5200. −55600. 5800. + − + − 5400 2 5200 µ ) [MeV/ m(K +mπ(−Kµ +πµ −µ) [MeV/ c2] c ]. 5200. 5600. + − + −. 5600. 5400. 5800. 5600. + − + −. 5800. 2. 2. 5800. m(K +π −µ +µ −) [MeV/c2]. m(K +π −µ +µ −) [MeV/c2]. 8000 BLHCb → K ee B 0 → K *0 2 : 353 ± 21 3 ee35q2 : 285 LHCb LHCb LHCb : 274K Low ± 18 Central q J/ ψ region 274K : 285 ± 18 Central 353 21 2 2 J/ψ region : 274K J/ψ regionLHCb q − : 285 ± 181030 Central q : 353 Signal ±021 *0 + − 10 LHCb0 7000 *0 + 0 Low B →K e e B →K e e B →K *0J / ψ 25 LHCb LHCb 10. 35. 35. 8000 LHCb. 8000. 22 Candidates perper 3434MeV/ MeV/ cc Pulls Candidates Candidates Candidates perper 34 34MeV/ MeV/cc2 2 Pulls. 0. m(K π µ µ ) [MeV/c ]. per Candidates 10 MeV/c2 per 10 MeV/c2 Pulls Candidates Pulls. 5200. 5800. 2 Candidates Pulls per 2 10 MeV/c 10 MeV/cper Pulls Candidates Candidates 10 MeV/per c2 10 MeV/c2 Pulls per Pulls Candidates. 5200. 5400. π µ µ 5600. 5 0 −5. per Candidates 10 MeV/c2 per 10 MeV/c2 Pulls Pulls Candidates. 5 0 −5. 5400. 2 10 MeV/c2 Candidates Pulls per 10 MeV/cper Pulls Candidates Candidates Pulls per 10 MeV/per c2 10 MeV/c2 Pulls Candidates. 5200. Pulls per Candidates 10 MeV/c2 per 10 MeV/c2 Pulls Candidates. 5400. 70 *0 + − 0 70 60 60 50 50 50 8090 40 40 LHCb 10 80 40 90 70LHCb *0 + 2− 0 30 30 2 4 B → K µ µ 2<1.1 [GeV2/ c4] 70 30 0.045<80q <1.1 [GeV / c ] 0.045< q * 0 0 + − 60 20 20 60 B →K µ µ Combinatorial 20 70 5600 5800 50 Combinatorial 10 10 10 50 2 60 m(K + − + −) [MeV/ c] 40 50 5 5200 5 40 5 5200 5200 5400 5600 5800 5400 5400 5600 5800 + 0 m(K +π −µ +µ −) [MeV/ c2] 0 30 0 π −µ +µ −) [MeV/ 2 30 40 m(K 2<1.1 [GeV2/ c4c] ] 0.045< q −5−5 −5 2 4 + − 5800 30 5200 + −5400 205200 200.045< 5600 25200 5400 5600 5800 5400 q2<1.1 [GeV /c ] + − + − m(K +20 π −µ +µ −) [MeV/c2] m ( K π µ µ ) [MeV/ c2] 1010 10 5. 3. Candidates perper3434MeV/ MeV/cc2 2 Pulls Candidates. 10−15. 5400. 0 *0 30 B →K K µ µµ µ 2 4→ B [GeV / c ] Combinatorial B → K µµ 20 80. B0→K *0µ2+µ − 70 0Combinatorial *0 0.045< q <1.1 60. 3. 2 Candidates per per 34Candidates 34MeV/ MeV/per cc2 per Pulls Candidates Candidates 3434MeV/ MeV/cc2 2 Pulls. 10. B → K µµ. Candidates Candidates per per 3434MeV/ MeV/cc2 2 Pulls 22 Candidates perper34 34MeV/ MeV/ cc Pulls Candidates 2 234MeV/ Candidates Candidates perper 34 MeV/cc2 2 Candidates perper 3434MeV/ MeV/ cc Pulls Pulls Candidates. 115. 90 LHCb. *0. Candidates 3434MeV/ MeV/cc2 2 Candidates per per 34Candidates 34MeV/ MeV/per cc2 2per Pulls Pulls Candidates. 20. 90 080 70 60 50 90 40 80 30 70 20 520060 10 50 405 5200 0 305 − 20 5200 10. B 0 → K µµ. Candidates perper3434MeV/ MeV/cc2 2 Pulls Candidates. 10. B 0 → K *0 µµ. ×10. LHCb RK* YIELDS 70 RK* YIELDS RK* YIELDS B0→K *0µ +µ − 80. B0→K *0J / ψ 60 Combinatorial Combinatorial arXiv:1705.0580240 arXiv:1705.05802 arXiv:1705.05802 → K *0 µµ 50 + 0 R YIELDS Λ → K pJ /ψ R YIELDS ×10 K* b × 10 K* 40 ×10 80 * 0 60 0 R YIELDS 30 8090 60 80 60LHCb LHCb K* LHCb Bs →K J / ψ LHCb LHCb LHCb 80 LHCb LHCb 70 *0 *0 *0 0 0 0. B Combinatorial *0 B0. 2 234 Candidates Candidates per 34MeV/ MeV/c2c222 Pulls Candidates Candidates per 34 34 MeV/ cccper Pulls 2 234MeV/ Candidates Candidates per per MeV/ cc Pulls Candidates perper per 34 34MeV/ MeV/ MeV/ c34 Pulls Candidates. 25. →K *0µ +µ −. 0. 2 3434MeV/ Candidates MeV/cc2 2 Pulls Candidates per per 34Candidates 34MeV/ MeV/per cc2 per Pulls Candidates. 5 0 −5. LHCb. 2 10 MeV/c2 10 MeV/cper Pulls Candidates Candidates Pulls per. 90 80 70 60 50 40 30 20 10. 22 Candidates perper34 34MeV/ MeV/ cc Pulls Candidates Candidates Candidates per per 3434MeV/ MeV/cc2 2 Pulls. Candidates perper3434MeV/ MeV/cc2 2 Pulls Candidates. Pulls Candidates per 10 MeV/c2. 3. LHCb 35 LHCb LHCb 25 LHCb 3LHCb LHCb 3 LHCb LHCb LHCb LHCb B0→K *0J / ψ LHCb 30 10 LHCb 7000LHCb 10 LHCb 10 *0 + − *0 7000 10 0 6000 *0 + − *0 0 10 0 300→0K *0e+ 30 10 LHCb0 Combinatorial 7000 Signal B → K e e B → K J / ψ *0e−+ − Signal 10 2 B → K e e B → K e e B → K J / ψ * 0 0 *0 0 B 0 * 0 + − *0 25 0 * 0 30 Combinatorial 6000 20 Combinatorial Combinatorial Signal Signal B →K e e B 0→K **00J / ψ → ee B →K J / ψ 0Signal *0 B →KB J /K ψ 10 6000 20 → 2Combinatorial B K ee 2 K ee 25 Signal B→ K0 J / ψ + Combinatorial Combinatorial Combinatorial 6000 Combinatorial 25 Combinatorial Combinatorial Combinatorial * 0 Combinatorial 10 2 + + 5000 B BCombinatorial →Combinatorial K ee− 10 − 5000 Combinatorial Combinatorial Combinatorial Combinatorial Combinatorial + − + Combinatorial 0 Combinatorial 5000 25B→ + 10 0 − Xe + − *0 − Xe e B(→0+→ Λ K +p J / ψ → X)(ee→eYK )ee Λ eYK →Xe X Λ 20 Combinatorial B200→ Xe + −)ee 5000 → X(→eYK Combinatorial 0 b0+→ 0→K0+p J / ψ + − *0 b0→K +p J / ψ 1150→XeB++e→ 2015 B→Xe e b Λb0→ K pJK / ψ*0p J / ψ B*→ eYK 100B→*0Xe → X (→ )ee *00 135125 B0→ Xe+e− 1 0 0pXe 4000 Λs0b→ →KK*0pJ J/ ψ/ ψ *0 e 20 * 0 0b→K 4000 Λ J / ψ 8000 + 0 8000 0e− * 0 4000 Λ → 35 + − B B → K J / ψ 0 B → K J / ψ 10 1 B → K J / ψ * 0 B → K J / ψ ( → ee) B → Xe 103LHCb B 00 *0*0 8000LHCb s0 B15 (→−ee) LHCb 0K J/ ψ+ 4000 Λs0b→ → K pJ J/K /ψ J / ψ *0 b→ LHCb LHCb LHCb 35→ 0* → K J / ψ B LHCb 30 25 LHCb LHCb B → Xe e LHCb10 15LHCb LHCb 10 B K ψ B → K J / ψ ( → ee) 10 B → K J / ψ B → Xe e s 10 0 B → K J / ψ ( → ee) 0 LHCb 3000 B → K J / ψ LHCb 3 LHCb *0 * 0 3000 Bs → 10 LHCb0 7000s 215 10 15q2<6.0 [GeV2/ c4] 02 +4 − *0 10 LHCb 1030 7000 0 *J0 / ψ*0 + − 10300.045< 0 K *K 0 *+ 2<1.1*0[GeV 0B 0→ 2<6.0 +e−2/ c4] 10 q0 2<1.1*0[GeV / c4] 10 LHCb − e] 2 1.1< 7000 Signal * 0 3000 B → K J / ψ 0.045< q0→ 10 0 e B → K J / ψ 1.1< q [GeV / c * 0 0 B → K e e Signal B K e B → K J / ψ + − B → K e s + − 0 * 0 2 30 10 Signal B → K 2<1.1 [GeV / c4] 0 q2<6.0 *0 [GeV3000 4] 20 10 Signal B → K e e B → K J / ψ B → K e e 2000 0.045< q 1 2 2 s −Signal 1 1.1< / c 0 * 0 B →K J / ψ J / ψ 2000 12 1.1< 6000 4] B →K J / ψ 1 20 6000 Signal −/1 10 q2<6.0 [GeV / c 5 B21→K J / ψ 100.045< 6000 q2<1.1 [GeV c14] Combinatorial Combinatorial Combinatorial Combinatorial 2000 Combinatorial 10−25 −25 Combinatorial Combinatorial Combinatorial 2 Combinatorial 10 −25 1 Combinatorial 10 Combinatorial Combinatorial 5 10 10 1000 5 5 10 1000 10 Combinatorial 5000 + Combinatorial 0 Combinatorial 10 + − 5000 −1 Λ Combinatorial 0 0 + + − 2000 + + −e−*0)ee 1 205 Combinatorial + 15 B Combinatorial 0 + − B Λ BK → B p(J→/ eψYK*0)1 →Xe X ee B0→ Xe 1000 →→ X(Xe →eYK 105000 b0→K +p J / ψ 201 →Xe +e− − 1 b0→ 10−4000 B→ →Xe X(→eYK )ee −Λ1b0→K +p J / ψ 20 11550→Xe +e − 6000 5000 1 1 5Λ0 →0K 500 0 *0*0 +6000 0p*J 0 *+ 0b→K*0p J / ψ 4000 Λ 0*/0ψ 10 5 5 1 − 5500 6000 4500 5000 5500 6000 4500 5000 5500 6000 10 * 0 * 0 0 0 0 4000 Λ → K p J / ψ 5000 5500B4500 4500 5000 5500 6000 4500 5000 5500 + B → K →0KJK/ ψ Bs+0b→B → − B0 B →K KXe 10 Xe e s0 BK→ J/ Jψ/(ψ → ee) 10 5 → J/ Jψ/(eψ → ee) B(4500 B →πKKeJ/eJψ)/(ψ *0J / ψ6000 10 5 mB(K→πXe 5m s5 B(K → → ee) 0bK→πKe*0eJ )/ ψ + − + − 010 −2(0K→ −m20(K5500 −m 2B −e+e* −) [MeV/1000 2] 4500 5000 c ] 5500 6000 5500 e ee ) [MeV/ m [MeV/5000 c] [MeV/ c] 4500 5000 −3000 20 15 −215 15 m π e e ) [MeV/ c2] π c26000 (5K +π −+e+e−) [MeV/ c2] 3000 B → K J /ψ − 1 B → K J / ψ 10 10 10 10 2 s − 5 − 5 − 2 s 3000 B → K J / ψ 0 0 + 2 0 + 2 4 10 10 −e+e[GeV 4] −[GeV −) [MeV/ 2m +e5000 −) [MeV/ 5 5500 −4500 2 4 4500 6000 4500 5500 6000 −50.045< 25500 −2s 26000 −2q2<6.0 [GeV 0.045< q <1.1 (K π[GeV m(K π −e+e−) [MeV/ c2] K π6000 e10 1.1<qm2(<6.0 / c4] c2] 10 0.045< q2<1.1 [GeV c5000 5000 5500 6000/5000 4500 5000 5500 6000 5000 5500 6000 1.1< q4500 <6.0 / c45000 ] / c c]2] −10 q2<1.1 [GeV /c ] 4500 6000 4500 5000 5500 6000 4500 5000 5500 6000 1.1< / c4] 5000 5500 6000 4500 5500 4500 5500 6000 10 10m −4500 55500 −4500 5+− + − −10 5 +− + − 5000 + − + − 5000 +1 + 10− + − 2000 2 2 2 − 1 2000 1 + −1 + + 5000 5500 6000 5000 5500 6000 4500 5000 5500 6000 − − + − 2 + − 2 2000 2 1 − 1 + − 1 K5m(K π πe −ee+e)−[MeV/ cc2]] m π πe −ee+e)−[MeV/ cc2]] m(6000 −π +5e −e+ )−[MeV/ c2 ] 5cc2]] m(10 5 2 m(Km(Kπ4500 50004500 6000 5500 6000 5000 m(K Km(π cc6000 Km(5500 e −−ee++e)−[MeV/ cc2]] 2 2]] 4500 10 (54500 ) [MeV/ πe −ee+e)−[MeV/ ) [MeV/ (K ) [MeV/ 10 m(π K 5500 π e e ) [MeV/c ] K +5500 πe −+e e+e)+−−[MeV/ )+[MeV/ K1++π )− [MeV/ 10(Km4500 5000 6000 5000 50005000 5500 10 + −e+ −)−[MeV/ c22] 6000 −)−[MeV/ 55500 +e −e+ + 1000 m ( K π ) c m ( K π −e e 5K m ( K π e −e e c ] −[MeV/ 2]] 1000 + + 2 1000 m ( π e e ) [MeV/ c m ( K π e ) [MeV/ m ( K π e ) [MeV/ c ] −1 c2] −20 −20 m(K +π −e+e−) [MeV/ c2] [MeV/ m(K +π −e+e−c)] [MeV/ c2] 10−15 102−15 5 m(K +π −e+e−) 10 2 5 5 10 10 5 5 5 −4500 5q2 : 111 −region 5 4500 5000 5500 6000 5500 6000 5000 5500 4500 5500 4500 5000 5500 6000 5000 5500 Central 6000 5500 :4500 6000 5000 4500 ± Central 5000 5500 6000 J/60005000 ψ6000 58K5000 q6000 :4500 ± 11)5000 q 2:4500 ±J/14 5000 5500 6000 5000 5500 6000 4500 6000 ψ5000 region :45005500 58K Low q2 : 89 ± 11 6000Low 0 −111 0 0 + − + − 089 −14 0 +π+−π −) [MeV/ −+)e[MeV/ −) [MeV/ 205500 (20K +π −e+e−) [MeV/ c2] K +mπ(−Ke+eπ−−)e[MeV/ c24500 ] c2] m: (K 58K π e e ) [MeV/ c2] 6000 e+11 ee5000 c2]c2] 5000 4500 5500 [MeV/ cq ] 2 : 89 [MeV/ m(K π e e ) [MeV/ c ] 6000 J/cψ] region5500 q2−−205:m(111 ±+e14 10−10 10 10−−−25−25 m(K π e eLow 10−m−2(5K π e e )5000 10m−5500 10−−m25(mK(K± −5 −5 + Central 10 + −5 +. 5000 5000. 0. Signal *0 + −. − 6000 5500 + −5000 55004500 4500 + 6000 5000 −e+ −e+ m (eeK+−e)−[MeV/ e2−) m(Km+(π − K +π ) [MeV/cc2]]. + − + −. 2. + − + −. 2. 4500 5000 4500 5000 2 5500 6000 4500 4500 5000 5500 6000 2]− + − 5000 [MeV/ 2 m(Kmc+(π e −ee+e)−[MeV/ K +π ) [MeV/cc2]]. m(K π πe e ) [MeV/ c ]. 5500 6000 5500 6000 4500 5000 5500 6000 5500 6000 5000 + 4500 +− −+ + mm (Km π cc22]]cc22]] (K ππe+−πeee+−−ee)−+−[MeV/ [MeV/ (m K(+K )e)−[MeV/ ) [MeV/. *0. 0 0. *0. *0. 0 0. *0 + − + −. 2. + − + −. 2. − 5000 4500 4500 5000 + − 5500 6000 4500 5000 4500 5500 −6000 +e−2) m−e(+−Kee+−e+)−[MeV/ e5000 m(Km+(π K +π ) [MeV/cc2]]. 8000. + − + −. + − + −. Francesco Polci. m(K π πe e ) [MeV/ c ]. Francesco PolciLHCP, LHCP,May Shanghai, 15-21 May 2017 15-21 May 12 2017 Shanghai, 2017 LHCP, Francesco Polci 15-21 Shanghai,. R. Coutinho (UZH). 2. 5500 6000 5500 6000 2 4500 5000 5500 60005000 5500 6000 4500 5000 5500 6000 4500 5000 55004500 6000 2 ++ −c [MeV/ −)−[MeV/ 2 + m(Km cc22]] cc22]]m(Km+(K π +−πe+−ee+−e)−[MeV/ m+((π π−ee]+−+−πee)+−[MeV/ KKm πe(K )ee+[MeV/ ) [MeV/ e ) [MeV/ 12cc2]]. Francesco PolciLHCP, Shanghai, 15-21 LHCP, Shanghai, 12 May 2017 15-21 May 2017 Francesco Polci LHCP, Shanghai, 215-21 May 2017 2 2 2 2 2 ψ region : 58K J/14 ψ region 58K±J/14 11Low qq ::111 q : 111 ±Central Low q : 89 ± 11 Low q : 89 ±Central 14 89 ±±Central 11 q ::111. Francesco Polci. 2. −e+ 6000 − 2 + 5500 6000 m5500 (Kπ +−(eπ −e + )−[MeV/ c2 ] K + − π e e c)22][MeV/c ] m(Km+(m K π −ee+e)−[MeV/ ) [MeV/c ]. J/ψ region : 58K 12. 12. 12. 75.

(76) Analyses results [LHCb, LHCB-PAPER-2017-013] [Belle, PRL 103 (2009) 171801] Ratios of „branching fractions‰ - lepton flavour universality [BaBar, PRD 86 (2012) 032012] [LHCb, PRL 113 (2014) 151601]. y. RK. LHCb. BaBar. Belle. 2. LHCb 1.5. RK ⇤0. s. 1.0 0.8 0.6. 1. SM. 0.5. 0.4 0.2. LHCb. 2.6σ tension with the SM. 0 0. 0.0. 5. 10. 15. 20. LHCb BIP CDHMV EOS flav.io JC. 0. 1. 2. 3. q2 [GeV2/ c4]. 4. 5. 6. q 2 [GeV2/c4]. [Bobeth et al, JHEP 12 (2007) 040]. Intriguing! What happens next? Measure, measure, measure ⁄. R. Coutinho (UZH). 76.