ATLAS tau identification

Transcription

1 ALAS tau identification a short history of taurec and introduction to the identification and fake-rate sessions of the 212 ALAS au Workshop Ryan Reece University of Pennsylvania ryan.reece@cern.ch ALAS au Workshop, Oxford March 21, 212

2 Outline 1. My short history of the evolution of taurec 2. Present issues and current direction 3. Comparison to CMS 4. Introduction to our speakers 2

3 Seeds of reconstruction Once upon a time, there were two tau reconstruction algorithms. 1. taurec - seeded by p > GeV anti-k.4 topo-jets. calo-seeded 2. tau1p3p - seeded by p > 6 GeV inner detector tracks. track-seeded Fraction of candidates of tau candidate (GeV) E ALAS calo-seeded only both seeds track-seeded only Since virtually all candidates have a calo-seed, we effectively merged the variable calculation of both algorithms, using only calo-seeds. Performance of the tau reconstruction and identification algorithm with and mc8 [AL-COM-PHYS ] 3

4 Early MV identification Jet-tau discrimination Prefers narrow calorimeter jets, likelihood-based discriminant. R EM = Ri <.4 i Ri <.4 i E EM,i R i E EM,i Rejection ALAS 1 prong all calo seeds Electron-tau discrimination, = - 3 GeV E = 3-6 GeV E = 6 - GeV E Efficiency Rejection ALAS 3 prong all calo seeds = - 3 GeV E = 3-6 GeV E = 6 - GeV E Efficiency Figure 13: Rejection vs efficiency for single or three prong candidates, for events where -candidates Candidate IsEle(%) IsEle eg(%) Overall 1P 3P Overall 1P 3P τ from W τν τ form A ττ Electron form W eν Electron form A ττ [AL-COM-PHYS ] 4

5 Early sub-structure studies Fraction of candidates.3.2 ALAS τ πν τ ρν τ a 1 (2π π) ν Fraction of candidates ALAS τ πν τ ρν τ a 1 (2π π) ν number of reconstructed π Invariant mass (GeV) Monte Carlo based substructure studies Cell-based shower-shape subtraction π reconstruction. Still unvalidated with data. [AL-COM-PHYS ] 5

6 First data candidates /.1 Number of τ ALAS Preliminary Integrated Luminosity 15.6 nb Data 2 ( s = 7 ev ) Pythia QCD Jets Pythia Z->ττ R EM ε bkgd Integrated Luminosity 244 nb / Loose Cuts (Data/MC) / Medium Cuts (Data/MC) / ight Cuts Data(MC) ALAS Preliminary First comparisons of background distributions and the QCD fake-rate between data and Monte Carlo. p [GeV] Already see that MC over-estimates the jet fake-rate. kw.5 Reconstruction of hadronic tau candidates in QCD events at ALAS with 7 ev pp collisions [ALAS-CONF-2-59] au Reconstruction and Identification Performance in ALAS [ALAS-CONF-2-86] 6

7 au discriminants Cuts p -parametrized cuts on R EM and R track, and a cut on f track. Projective) likelihood d = ln( LS L B = ( ) N i=1 ln p S i (x i ) p B i (x i) Boosted decision trees (BD) track R Arbitrary Units [GeV] p W τν+z ττ ALAS Preliminary dijet Monte Carlo 3 prongs 15 GeV<p <6 GeV -1 2 dijet data dt L = 23 pb Likelihood Score Arbitrary Units.12 W τν+z ττ dijet Monte Carlo dijet data dt L = 23 pb.8 ALAS Preliminary 3 prongs 15 GeV<p <6 GeV BD Score 7

8 Maturing of discriminants EM R [GeV] p Inverse Background Efficiency ALAS Preliminary 1-Prong p >2GeV Cuts BD Likelihood Signal Efficiency (a) R cut curves for 1-prong Cuts are pt-parametrized to account for the Lorentz collimation of boosted taus. Experience grows with LLH and BD discriminants, which become the preferred discriminants in 211. Reconstruction, Energy Calibration, and Identification of Hadronically Decaying au Leptons in the ALAS Experiment [ALAS-CONF , AL-PHYS-IN ] 8

9 Seeing first hadronic taus Number of events ALAS Preliminary Data 2 ( s= 7 ev) W τ h ν τ EW background QCD background (B) -1 L dt = 34 pb Number of events / ALAS Preliminary Data 2 ( s= 7 ev) W τ h ν τ EW background QCD background (BD) -1 L dt = 34 pb Number of tracks R EM Nov 2: Observation of W τhν [ALAS-CONF-2-97] Feb 211: Observation of Z τhτl [ALAS-CONF-211-] 9

10 W τν cross section σ(w τν) = 11.1±.3(stat.)±1.7(sys.)±.4(lumi.) nb σ theory =.46±.52 nb at NNLO ALASW τ ν τ ALASW e ν e ALASW µν µ ALAS Preliminary Data 2 ( s = 7 ev) Stat uncertainty Sys Stat Sys Stat Lumi Prediction (NNLO) heory uncertainty σ(w l ν l ) [nb] Dominant systematics τ h efficiency.3% τ h energy scale 8.% τ h + ME trigger efficiency 7.% luminosity 3.4% acceptance 2.3% Measurement of the W τν cross section in pp collisions at sqrt(s)= 7 ev with the ALAS experiment [arxiv:18.41]

11 Z ττ cross section σ combined =.97±.7(stat.)±.7(sys.)±.3(lumi.) nb σ theory =.96±.5 nb at NNLO Z ττ combined 36pb -1 Z ee/µµ 33-36pb τ µ τ h τ e τ h τ e τ µ τ µ τ µ -1 ALAS Preliminary Stat Syst Stat Syst Stat Lumi heory (NNLO) σ(z ll, 66<m <116 GeV) [nb] inv Dominant systematics τ h energy scale 11% τ h efficiency 8.6% µ efficiency 8.6% e efficiency 3-% acceptance 3% luminosity 3.4% Measurement of the Z ττ cross section in pp collisions at sqrt(s)= 7 ev with the ALAS detector [arxiv:18.216] 11

12 Pile-up dependence Efficiency prong true visible E [GeV] loose, n vx = 1 loose, n vx = 2 loose, n vx = 3 loose, n = 4 vx medium, n = 1 vx medium, n vx = 2 medium, n = 3 vx medium, n vx = 4 tight, n = 1 vx tight, n In 2, the LHC luminosity climbed from 27 to 32 cm -2 s -1, giving N(vertex) 1-5. tight, n tight, n = 2 vx = 3 vx = 4 vx In 211, we reached 33 with N(vertex) will bring us to 34 with N(vertex) 3-4. REM in R <.4 effectively requires taus to be isolated in the calorimeter, introducing sensitivity to pile-up. 12

13 Pile-up robust variables +,* Previous tau au ++ +,* %&'()*% -. %&'()*% -. 76* ,59: &21563(1562( /.1&2 ',34(31562( 76* ,59:1 &21563(1562( ;:6',+&62,22*'*: :*<+3,5+1=36>1 &:6',+&621,22*'*:?!"$!"#!"$!"# [R. Reece - au WG ] 13

14 Pile-up robust variables +,* Previous tau au ++ +,* %&'()*% -. %&'()*% -. 76* ,59: &21563(1562( /.1&2 ',34(31562( 76* ,59:1 &21563(1562( ;:6',+&62,22*'*: :*<+3,5+1=36>1 &:6',+&621,22*'*:?!"$!"#!"$!"# [R. Reece - au WG ] 14

15 au-vertex association Efficiency 1-prong Efficiency 3-prong p (τ h ) [GeV] Default = choose vertex with highest p (track) 2 µ 2 p (τ h ) [GeV] Corrected = choose vertex with reconstructed z closest to Monte Carlo truth of the tau z. (ideal) JVA = choose vertex with maximum JVF for this tau candidate. [Kong Guan an, Guilherme Hanninger, Elisabetta Barberio] 15

16 High p tau retuning Efficiency / (2. GeV) Summer 211 Loose au ID p (τ h ) [GeV] reco reco w/numrack Loose BD Loose Llh Loose Cut Signal Efficiency / 16 GeV Fall LLH and BD retuning 1.1 New BD bits Validation New LLH bits Validation p vis 1-Prong Loose [GeV] (of match) [A.una - au WG ] [N.Dawe - au WG ] 16

17 High p tau track loss High p tracking inefficiency Reconstruct 3 2 tracks Highly collimated overlapping hits Fraction rue MC 3-prong taus p (τ h ) reco 2prong reco 3prong reco 4prong [A.una - au WG ] 17

18 CMS Particle Flow!"#$%&'()*)+,&-,"#$%!&'()&*+!"#$%!&'()&*+!"#$%!&'()&*+,"#$%!&'()&*+.(/1'&$()+2'3$14$1&,.2 /+()+2'3$.(/1'& 1&,.2'&$5)1 ''$!"#$%6#$%789:;9;;< Matches track to clusters to form charged and neutral PF objects. PF objects are used as input for all CMS tau reconstruction. 18

19 CMS: Hadron Plus Strip (HPS) Discrimination with calorimeter based isolation R <.5. [CMS PAS AU-11-1] 19

20 CMS: au Neural Classifier (anc) Uses a shrinking core-cone: R(photons) <.15 for photons R(charged) < (5 GeV)/E for charged hadrons R(charged) < R(isolation) <.5 Immediately discarded if the candidate doesn t match an expected tau decay mode. Decay mode Resonance Mass (MeV/c 2 ) Branching fraction (%) τ h ν τ 11.6% τ h π ν τ ρ % τ h π π ν τ a % τ h h + h ν τ a % τ h h + h π ν τ 4.8% Dedicated Neural-net classifier for each decay mode [CMS PAS AU-11-1] 2

21 CMS Performance efficiency.8 CMS Simulation, 1 HPS loose HPS medium HPS tight s= 7 ev efficiency CMS Simulation, 1.8 s= 7 ev Not trivial to compare ALAS and CMS tau performance because we bin fake-rates in N(track) instead of categorizing the decay mode. expected τ misidentification rate for jets τ CMS, generated p -1 s= 7 ev, 36 pb HPS loose τ h W µνdata (GeV/c) W µνsimulation QCD Data QCD Simulation QCDµ Data QCDµ Simulation expected τ misidentification rate for jets τ anc loose anc medium anc tight 5-2 CMS, generated p -1 s= 7 ev, 36 pb W µνdata W µνsimulation QCD Data QCD Simulation QCDµ Data τ h anc loose (GeV/c) -3-3 QCDµ Simulation Data-Sim. Simulation Data-Sim. Simulation jet p (GeV/c) jet p (GeV/c) [CMS PAS AU-11-1] 21

22 CMS decay mode ID decay mode πππ CMS Simulation, s= 7 ev relative yield.8.6 CMS, Data Z ττ W+jets tt/ewk QCD -1 s= 7 ev, 36 pb reconstructed τ ππ π π ππ (π ) generated τ πππ decay mode π ππ reconstructed τ πππ decay mode [CMS PAS AU-11-1] 22

23 Calorimeter granularity ALAS B = 2. η φ = R =.4 anti-k topo-jets CMS B = 3.8 η φ = R =.5 anti-k PF-jets ALAS Barrel EM Calorimeter η = 4.3X ϕ=.245x4 36.8mmx4 =147.3mm 15 mm 47 mm ϕ 1.7X 16X 37.5mm/8 = 4.69 mm η =.31 η Cells in Layer 3 ϕ η = X η =.25 Strip cells in Layer 1 ϕ =.245 rigger ower η =.1 Square cells in Layer 2 rigger ower ϕ =.982 Granularity could fundamentally limit our capacity to reconstruct sub-structure / π s. 23

24 Observed variance in fake-rates pass/fail.1 W CR: Inclusive, 1p W CR: OS, 1p W CR: SS, 1p pass/fail.1 QCD CR: Inclusive, 1p QCD CR: OS, 1p QCD CR: SS, 1p.5.5 ratio 2 1 ratio p p 24

25 Personal recommendations Sub-structure We need to make better visualizations of the calorimeter information to better understand how clearly we can separate π s. Could lead us to consider discrimination with decay mode identification. Validate π reconstruction techniques in data with high purity Z ττ. Shrinking-cone / Lorentz parametrization is still a good idea. Fake-rates share experience among analyses classify variance of fake-rates among samples motivate how sensitive the fake-rates are to the selection how stable is the quark/gluon fraction? 25

26 oday s agenda: Sub-structure Pile-up robust multivariate tau identification for Almut Pingel (NBI) π reconstruction and validation -- Pawel Malecki (Krakow) Cell-based π reconstruction -- Benedict Winter (Bonn) Cluster-based π reconstruction -- Michel rottier-mcdonald (Simon Fraser) Panau Status -- Christian Limbach (Bonn) 26

27 oday s agenda: Fake-rate racking optimization for taus -- Anna Kopp (Freiburg) Electron and muon fake-rates -- Nikolaos Rompotis (Washington) Jet fake-rate comparison -- Matteo Volpi (Melbourne) Jet fake-rate comparison -- Alex una (Penn) 27

28 Back up

29 Phenomenology of tau decays τ 17.8% 9.% e ν e ν τ µ ν µ ν τ 17.4% π π ν τ 25.5% π ν τ.9% π 2π ν τ 9.3% K (Nπ ) (NK ) ν τ 1.5% π 3π ν τ 1.% π π π + ν τ π π π + π ν τ 4.6% } leptonic 35.2% 1 prong 49.5% } 3 prong 15.2% 29

30 au identification variables Electrmagnetic radius: R EM = rack radius: R track = Ri <.4 i {EM 2} EEM,i R i Ri <.4 i {EM 2} EEM,i Ri <.4 i p,i R i Ri <.4 i p,i Leading track momentum fraction: f track = ptrack,1 Core energy fraction: f core = Electromagnetic fraction: f EM = p τ Ri <.1 E EM i {all},i Ri <.4 E EM i {all},i Ri <.4 i {EM 2} EEM,i R j <.4 E EM j {all},j Cluster mass: m clusters, invariant mass clusters at the EM energy scale. rack mass: m tracks, invariant mass of the track system. ransverse flight path significance: S flight Motivation: taus tend to be collimated more than jets, have a leading track, and often significant neutral pion deposits in the EM calorimeter. 3

31 e-veto too tight? Inverse Background Efficiency ALAS Preliminary tau performance 1-prong, p > 2 GeV, η BD-based electron veto Cut-based electron veto Performance of the Reconstruction and Identification of Hadronic au decays with ALAS [ALAS-CONF ] ε = 31

32 Nikos e-veto perf plot $!"#$%&"'$()"*+$,$-$./ ε = 32

33 First systematic recommendations ) gen /p ES (p ).8 Underlying event ALAS Preliminary Hadronic shower.6 1 prong: η 1.3 Detector material Noise threshold EM Scale.4 Non-closure otal sys. error p gen [GeV] Systematic uncertainties estimated with dedicated Monte Carlo with shifts in UE, hadronization, and detector-related effects. au efficiency Ratio Looser Cuts working point 1 prong ALAS Preliminary Visible p Nominal Detector Material Hadronic Shower Underlying Event Noise threshold otal sys. error of truth tau [GeV] he efficiency measurement has been superseded with datadriven measurements from Z ττ and W τν tag-and-probe. [ALAS-CONF ] 33

34 W τν tag-and-probe Events ALAS Preliminary Before tau ID -1 ) Data 2 (34 pb τ(w τν) e (W eν) Jet background ag jet + ME events, probe for tau. 7 Looser identification working point ALAS Preliminary L = 34 pb Cuts 1.4 ±.6(stat) ±.4(sys) (ag&probe) -1 Events Number of tracks ALAS Preliminary After tau ID (Looser Cuts working point) Number of tracks -1 ) Data 2 (34 pb τ(w τν) e (W eν) Jet background apply ID Likelihood 1.2 ±.9(stat) ±.5(sys) (ag&probe) Boosted decision trees (ag&probe) Cuts 1. ±.5(stat) ±.13(sys) (Cross section) Likelihood (Cross section) 1.5 ±.6(stat) ±.5(sys) 1.2 ±.4(stat) ±.16(sys) Boosted decision trees.94 ±.7(stat) ±.13(sys) (Cross section) au identification efficiency scale factor Measurement of hadronic tau identification efficiency with W τν events [ALAS-CONF ] 34

35 Z ττ tag-and-probe Events / 5 GeV Before tau ID dt L = 8 pb s= 7 ev ALAS Preliminary Data 211 Z ττ(truth-matched) Z ττ(non-truth-matched) Multijet W µν W τν Z µµ tt Events / 5 GeV After tight BD ID dt L = 8 pb s= 7 ev -1 ALAS Preliminary Data 211 Z ττ(truth-matched) Z ττ(non-truth-matched) W µν Multijet Z µµ tt W τν m vis (µ,τ )[GeV] h m vis (µ,τ )[GeV] h Performance of the Reconstruction and Identification of Hadronic au decays with ALAS [ALAS-CONF ] 35

36 Jet width for quark/gluons Ψ(r) = fraction of jet energy within R < r. Quark jets are more narrow than gluon jets of the same energy. au identification prefers narrow candidates. his is consistent with samples of quark-enriched jets, like W+jet, having higher fake-rates. J. Gallicchio, M. Schwartz. Quark and Gluon agging at the LHC. arxiv:

37 OS vs SS W+jet Leading order W+jet production: q q g q q g g W q W q W (a) (b) (c) he charge of the quark should correlate with the reconstructed charge of the tau candidate, therefore (a) and (b) preferably produce opposite sign W+jet events. OS and SS will have different quark/gluon fractions. 37

38 Madgraph predicted Quark/Gluon % 8% G % 8% G 6% 6% 4% Q 4% Q 2% 2% % p Cut on All Jets (GeV) % p Cut on All Jets (GeV) % 8% GG % 8% GGG 6% 4% 2% % QG QQ p Cut on All Jets (GeV) 6% 4% 2% % QGG QQG QQQ p Cut on All Jets (GeV) J. Gallicchio, M. Schwartz. Pure Samples of Quark and Gluon Jets at the LHC. arxiv: